Biology subjects

Oh, S.

Publications and source records attributed to Oh, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

Delayed blood pressure recovery after exercise stress test is associated with autonomic dysfunction and pulse pressure in a middle-aged healthy group

BackgroundDelayed heart rate (HR) and blood pressure recovery after exercise test is known as the reliable indexes of autonomic dysfunction. Here we tried to evaluate the serial changes in various indicators during exercise test and correlations among them in a normotensive healthy middle-aged group. MethodsA total of 122 patients without hypertension or diabetes was enrolled (mean age, 55.6 {+/-} 11.0; male, 56.6 %; mean blood pressure, 124.8 {+/-} 16.6 / 81.5 {+/-} 9.6 mmHg). Treadmill test was performed for evaluation of chest pain. Patients with coronary artery disease, positive treadmill test result, left ventricular dysfunction or renal failure were excluded. Heart rate recovery was calculated by subtracting the HR in the first or second minute of recovery period from the HR of peak exercise (HRR1 or HRR2). Systolic blood pressure in the 4th minute of recovery stage (SBPR4) was used to show delayed blood pressure recovery. ResultsMetabolic equivalents (METs) and HR in stage 2 to 4 were significantly correlated with both HRR1 and HRR2. Multiple regression analysis of HRR revealed significant correlation of METs and SBPR4. SBPR4 was significantly correlated with both HRR1 and HRR2 (HRR1, r=-0.376, p<0.001; HRR2, r=-0.244, p=0.008). SBPR4 revealed significant correlation with SBP in the baseline to stage 3 and pulse pressure (r=0.406, p<0.001). ConclusionsDelayed BP recovery after peak exercise test revealed significant association with autonomic dysfunction and pulse pressure in normotensive middle-aged healthy group. It can be a simple and useful marker of autonomic dysfunction and arterial stiffness.

neuroscience↗

bioRxiv2023

Scalable Mesenchymal Stem Cells Enrichment from Bone Marrow Aspirate using Deterministic Lateral Displacement (DLD) Microfluidics Sorting

The growing interest in regenerative medicine has opened new avenues for novel cell therapies using stem cells. Bone Marrow Aspirate (BMA) is an important source of stromal mesenchymal stem cells (MSCs). Conventional MSC harvesting from BMA relies on archaic centrifugation methods, often leading to poor yield due to osmotic stress, high centrifugation force, convoluted workflow, and long experimental time ([~] 2 - 3 hours). To address these issues, we have developed a scalable microfluidic technology based on Deterministic Lateral Displacement (DLD) for MSC isolation. This passive, label-free cell sorting method capitalizes on the morphological differences between MSCs and blood cells (leukocytes and RBCs) for effective separation using an inverted L-shaped pillar array. To improve throughput, we developed a novel portable multiplexed DLD system that can process 2.5 mL of raw BMA in 20 {+/-} 5 minutes, achieving a 2-fold increase in MSC recovery compared to centrifugation methods. Taken together, we envision the developed DLD platform will enable fast and efficient isolation of MSCs from BMA for effective downstream cell therapy in clinical settings.

bioengineering↗

bioRxiv2023

Electrophysiological correlates of attention in the locus coeruleus - anterior cingulate cortex circuit during the rodent continuous performance test

Sustained attention, the ability to focus on an activity or stimulus over time, is significantly impaired in many psychiatric disorders, and there remains a major unmet need in treating impaired attention. Continuous performance tests (CPTs) were developed to measure sustained attention in humans, non-human primates, rats, and mice, and similar neural circuits are engaged across species during CPT performance, supporting their use in translational studies to identify novel therapeutics. Here, we identified electrophysiological correlates of attentional performance in a touchscreen-based rodent CPT (rCPT) in the locus coeruleus (LC) and anterior cingulate cortex (ACC), two inter-connected regions that are implicated in attentional processes. We used viral labeling and molecular techniques to demonstrate that neural activity is recruited in LC-ACC projections during the rCPT, and that this recruitment increases with cognitive demand. We implanted male mice with depth electrodes within the LC and ACC for local field potential (LFP) recordings during rCPT training, and identified an increase in ACC delta and theta power, and an increase in LC delta power during correct responses in the rCPT. We also found that the LC leads the ACC in theta frequencies during correct responses while the ACC leads the LC in gamma frequencies during incorrect responses. These findings may represent translational biomarkers that can be used to screen novel therapeutics for drug discovery in attention.

neuroscience↗

bioRxiv2023

Evaluating proteomics imputation methods with improved criteria

Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. This missingness hinders reproducibility, reduces statistical power, and makes it difficult to compare across samples or experiments. Although many methods exist for imputing missing values in proteomics data, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error, such as the mean-squared error between the imputed and the held-out observed values. Here we evaluate the performance of a set of commonly used imputation methods using three practical, "downstream-centric" criteria, which measure the ability of imputation methods to reconstruct differentially expressed peptides, identify new quantitative peptides, and improve peptide lower limit of quantification. Our evaluation spans several experiment types and acquisition strategies, including datadependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides, but that it can identify new quantitative peptides and improve peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that exisiting imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

bioinformatics↗

bioRxiv2023

Interpretable spatial cell learning enhances the characterization of patient tissue microenvironments with highly multiplexed imaging data

Multiplexed imaging technologies enable highly resolved spatial characterization of cellular environments. However, exploiting these rich spatial cell datasets for biological insight is a considerable analytical challenge. In particular, effective approaches to define disease-specific microenvironments on the basis of clinical outcomes is a complex problem with immediate pathological value. Here we present InterSTELLAR, a geometric deep learning framework for multiplexed imaging data, to directly link tissue subtypes with corresponding cell communities that have clinical relevance. Using a publicly available breast cancer imaging mass cytometry dataset, InterSTELLAR allows simultaneous tissue type prediction and interested community detection, with improved performance over conventional methods. Downstream analyses demonstrate InterSTELLAR is able to capture specific pathological features from different clinical cancer subtypes. The method is able to reveal potential relationships between these regions and patient prognosis. InterSTELLAR represents an application of geometric deep learning with direct benefits for extracting enhanced microenvironment characterization for multiplexed imaging of patient samples.

bioinformatics↗

bioRxiv2023

Sequence-to-sequence translation from mass spectra to peptides with a transformer model

A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum. Although approaches that leverage known peptide sequence databases are widely used and effective for well-characterized model organisms, such methods cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to the acquired tandem mass spectra without prior information--de novo peptide sequencing--is valuable for gaining biological insights for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this de novo sequencing problem, it remains an outstanding challenge, in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovos superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.

bioinformatics↗

bioRxiv2022

Multiomic Profiling Reveals Metabolic Alterations Mediating Aberrant Platelet Activity and Inflammation in Myeloproliferative Neoplasms

Platelets from patients with myeloproliferative neoplasms (MPNs) exhibit a hyperreactive phenotype. Here, we found elevated P-selectin exposure and platelet-leukocyte aggregates indicating activation of platelets from essential thrombocythemia (ET) patients. Single cell RNA-seq analysis of primary samples revealed significant enrichment of transcripts related to platelet activation, mTOR and oxidative phosphorylation (OXPHOS) in ET patient platelets. These observations were validated via proteomic profiling. Platelet metabolomics revealed distinct metabolic phenotypes consisting of elevated ATP generation, accompanied by increases in the levels of multiple intermediates of the tricarboxylic acid (TCA) cycle, but lower alpha-ketoglutarate (-KG) in MPN patients. Inhibition of PI3K/AKT/mTOR signaling significantly reduced metabolic responses and hyperreactivity in MPN patient platelets, while -KG supplementation markedly reduced oxygen consumption and ATP generation. Ex vivo incubation of platelets from both MPN patients and Jak2 V617F mice with -KG significantly reduced platelet activation responses. Oral -KG supplementation of Jak2 V617F mice decreased splenomegaly and reduced hematocrit, monocyte and platelet counts. Finally, -KG incubation significantly decreased proinflammatory cytokine secretion from MPN CD14+ monocytes. Our results reveal a previously unrecognized metabolic disorder in conjunction with aberrant PI3K/AKT/mTOR signaling, contributing to platelet hyperreactivity in MPN patients.

cancer biology↗

bioRxiv2022

Blocking necroptosis reduces inflammation and tumor incidence in a mouse model of diet-induced hepatocellular carcinoma

Background & AimsNonalcoholic fatty liver disease (NAFLD) is one of the etiologies that contribute to hepatocellular carcinoma (HCC), and chronic inflammation is one of the proposed mediators of HCC. As necroptosis is a cell death pathway that induces inflammation, we tested whether necroptosis- induced inflammation contributes to the progression of NAFLD to HCC in a mouse model of diet- induced HCC. MethodsMale and female wild-type (WT) mice or mouse models where necroptosis is blocked (Ripk3-/- or Mlkl-/- mice) were fed a control diet or choline-deficient low fat diet (CD-LFD) or CD-high fat diet (CD-HFD) for 6 months. Changes in inflammation, immune cell infiltration, activation of oncogenic pathways, and tumor incidence were assessed by gene expression analysis, western blotting, and flow cytometry. RNA sequencing (RNA-seq) was performed to assess the changes in liver transcriptome. ResultsBlocking necroptosis by deleting either Ripk3 or Mlkl reduced markers of inflammation [proinflammatory cytokines (TNF, IL-6, and IL-1{beta}), F4/80+ve macrophages, CCR2+ve infiltrating monocytes], inflammation associated oncogenic pathways (JNK, PD-L1/PD-1, {beta}-catenin), and HCC in male mice. In female mice, blocking necroptosis reduced HCC independent of inflammation. Blocking necroptosis reduced cell senescence markers in males and females, suggesting a novel cross-talk between necroptosis and cell senescence. ConclusionsOur data show that hepatic necroptosis promotes recruitment and activation of liver macrophages leading to chronic inflammation, which in turn trigger oncogenic pathways leading to the progression of NAFLD to HCC in male mice. In female mice necroptosis contributes to HCC independent of inflammation. Thus, our study suggests that necroptosis is a valid target for NAFLD-mediated HCC. SynopsisNecroptosis is a cell death pathway that mediate inflammation. Blocking necroptosis attenuated chronic inflammation by reducing recruitment and activation of liver macrophages, which in turn reduced activation of oncogenic pathways and progression of NAFLD to HCC in mice. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/502666v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1bf82d7org.highwire.dtl.DTLVardef@26fc68org.highwire.dtl.DTLVardef@18538f4org.highwire.dtl.DTLVardef@15e338b_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments that has the ability to detect the spatial distribution of at least 40 cell markers. However, this new modality has unique image data processing requirements, particularly when applying this technology to patient tissue specimens. In these cases, signal-to-noise ratio for particular markers can be low despite optimization of staining conditions, and the presence of pixel intensity artifacts can deteriorate image quality and the subsequent performance of downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images. Specifically, we deploy a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for filtering shot noise (DeepSNF). IMC-Denoise outperforms existing methods for adaptive hot pixel removal, and delivers significant image quality improvement and background noise removal to a diverse set of IMC channels and datasets. This includes a unique, technically challenging, human bone marrow IMC dataset; in which we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately two-fold improved F1 score. Our approach remarkably enhances both manual gating and automated phenotyping with cell-scale down-stream analysis on these complex data. We anticipate that IMC-Denoise will provide similar benefits in mass cytometry imaging domains to more deeply characterize the complex and diverse tissue microenvironment.

bioinformatics↗

bioRxiv2022

Audiovisual Integration in the Human Brain: A Coordinate-based Meta-analysis

People can seamlessly integrate a vast array of information from what we see and hear in the noisy and uncertain world. However, the neural underpinnings of audiovisual integration continue to be the topic of debate. Using strict inclusion criteria, we performed activation likelihood estimation meta-analysis on 121 neuroimaging experiments with a total of 2,092 participants. We found that audiovisual integration is linked with the coexistence of multiple integration sites including early cortical, subcortical, and higher association areas. Although activity was consistently found within the superior temporal cortex, different portions of this cortical region were identified depending on the analytical contrast used, complexity of the stimuli, and modality within which attention was directed. The context-dependent neural activity related to audiovisual integration suggests a flexible rather than fixed neural pathway for audiovisual integration. Together, our findings highlight a flexible multiple pathways model for audiovisual integration, with superior temporal cortex as the central node in these neural assemblies.

neuroscience↗

bioRxiv2022

Cryo-EM structure of a RAS/RAF recruitment complex

Cryo-EM structures of a KRAS/BRAF/MEK1/14-3-3 complex reveal KRAS bound to the flexible Ras-binding domain of BRAF, captured in two orientations. Autoinhibitory interactions are unperturbed by binding of KRAS and in vitro activation studies confirm that KRAS binding is insufficient to activate BRAF, absent membrane recruitment. These structures illustrate the separability of binding and activation of BRAF by Ras and suggest stabilization of this pre-activation intermediate as an alternative to blocking binding of KRAS.

biochemistry↗

bioRxiv2022

Combination therapy with nirmatrelvir and molnupiravir improves the survival of SARS-CoV-2 infected mice

As the SARS-CoV-2 pandemic remains uncontrolled owing to the continuous emergence of variants of concern, there is an immediate need to implement the most effective antiviral treatment strategies, especially for risk groups. Here, we evaluated the therapeutic potency of nirmatrelvir, remdesivir, and molnupiravir and their combinations in SARS-CoV-2-infected K18-hACE2 transgenic mice. Systemic treatment of mice with each drug (20 mg/kg) resulted in slightly enhanced antiviral efficacy and yielded an increased life expectancy of only about 20-40% survival. However, combination therapy with nirmatrelvir (20 mg/kg) and molnupiravir (20 mg/kg) in lethally infected mice showed profound inhibition of SARS-CoV-2 replication in both the lung and brain and synergistically improved survival times up to 80% compared to those with nirmatrelvir (P= 0.0001) and molnupiravir (P= 0.0001) administered alone. This combination therapy effectively reduced clinical severity score, virus-induced tissue damage, and viral distribution compared to those in animals treated with these monotherapies. Furthermore, all these assessments associated with this combination were also significantly higher than that of mice receiving remdesivir monotherapy (P= 0.0001) and the nirmatrelvir (20 mg/kg) and remdesivir (20 mg/kg) combination (P= 0.0001), underscored the clinical significance of this combination. By contrast, the nirmatrelvir and remdesivir combination showed less antiviral efficacy, with lower survival compared to nirmatrelvir monotherapy, demonstrating the inefficient therapeutic effect of this combination. The combination therapy with nirmatrelvir and molnupiravir contributes to alleviated morbidity and mortality, which can serve as a basis for the design of clinical studies of this combination in the treatment of COVID-19 patients. IMPORTANCESince SARS-CoV-2 spread rapidly with the emergence of new variants of concerns, it is necessary to develop effective treatment strategies to treat elderly individuals and those with comorbidities. Antiviral therapy using a combination of drugs is more effective in eradicating viruses and will undoubtedly improve the clinical outcome and survival probability of hospitalized SARS-CoV-2 patients. In the current study, we observed three FDA-approved antivirals nirmatrelvir, remdesivir, and molnupiravir have therapeutic significance with moderate survival for their monotherapies against SARS-CoV-2 infected K18-hACE2 mouse model. The combination of nirmatrelvir and molnupiravir showed significant antiviral activity and a higher survival rate of approximately 80%, providing in vivo evidence of the potential utility of this combination. In contrast, nirmatrelvir and remdesivir combination showed less antiviral potency and emphasized the ineffective significance with less survival. The current study suggests that the nirmatrelvir and molnupiravir combination is an effective drug regimen strategy in treating SARS-CoV-2 patients.

microbiology↗

bioRxiv2022

Reconstruction of visual images from mouse retinal ganglion cell spiking activity using convolutional neural networks

All visual information in mammals is encoded in the aggregate pattern of retinal ganglion cell (RGC) firing. How this information is decoded to yield percepts remains incompletely understood. We have trained convolutional neural networks with multielectrode array-recorded murine RGC responses to projected images. The trained model accurately reconstructed novel facial images solely from RGC firing data. In this model, subpopulations of cells with faster firing rates are largely sufficient for accurate reconstruction, and ON- and OFF-cells contribute complementary and overlapping information to image reconstruction. Information content for reconstruction correlates with overall firing rate, and locality of information contributing to reconstruction varies substantially across the image and retina. This model demonstrates that artificial neural networks are capable of learning multicellular sensory neural encoding, and provides a viable model for understanding visual information encoding. Significance StatementConvolutional neural networks can be trained on high-density neuronal firing data from the optic nerve to reconstruct complicated images within a defined image space.

neuroscience↗

bioRxiv2022

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

Quantitative mass spectrometry measurements of peptides necessarily incorporate sequence-specific biases that reflect the behavior of the peptide during enzymatic digestion, liquid chromatography, and in the mass spectrometer. These sequence-specific effects impair quantification accuracy, yielding peptide quantities that are systematically under- or over-estimated. We provide empirical evidence for the existence of such biases, and we use a deep neural network, called Pepper, to automatically identify and reduce these biases. The model generalizes to new proteins and new runs within a related set of MS/MS experiments, and the learned coefficients themselves reflect expected physicochemical properties of the corresponding peptide sequences. The resulting adjusted abundance measurements are more correlated with mRNA-based gene expression measurements than the unadjusted measurements. Pepper is suitable for data generated on a variety of mass spectrometry instruments, and can be used with labeled or label-free approaches, and with data-independent or data-dependent acquisition.

bioinformatics↗

bioRxiv2022

Characterizing dry mass and volume changes in human multiple myeloma cells upon treatment with proteotoxic and genotoxic drugs

Multiple myeloma (MM) is a cancer of terminally differentiated plasma cells. MM remains incurable, but overall survival of patients has progressively increased over the past two decades largely due to novel agents such as proteasome inhibitors (PI) and the immunomodulatory agents. While these therapies are highly effective, MM patients can be de novo resistant and acquired resistance with prolonged treatment is inevitable. There is growing interest in early, accurate identification of responsive versus non-responsive patients, however limited sample availability and need for rapid assays are limiting factors. Here, we test dry mass and volume as label-free biomarkers to monitor early response of MM cells to treatment with bortezomib, doxorubicin, and ultraviolet light. For the dry mass measurement, we use two types of phase-sensitive optical microscopy techniques: digital holographic tomography and computationally-enhanced quantitative phase microscopy. We show that human MM cell lines (RPMI8226, MM.1S, KMS20, and AMO1) increase dry mass upon bortezomib treatment. This dry mass increase after bortezomib treatment occurs as early as 1 hour for sensitive cells and 4 hours for all tested cells. We further confirm this observation using primary multiple myeloma cells derived from patients and show that a correlation exists between increase in dry mass and sensitivity to bortezomib, supporting the use of dry mass as a biomarker. The volume measurement using Coulter counter shows a more complex behavior; RPMI8226 cells increase the volume at an early stage of apoptosis, but MM.1S cells show the volume decrease typically observed with apoptotic cells. Altogether, this cell study presents complex kinetics of dry mass and volume at an early stage of apoptosis, which may serve as a basis for the detection and treatment of MM cells.

systems biology↗

bioRxiv2022

The decision to develop into αβ or γδ T cells is pre-programmed in distinct subpopulations of DN1 thymocytes

The {beta} and {gamma}{delta} T cell lineages are both are thought to differentiate in the thymus from common uncommitted progenitors. The earliest stage of T cell development is known as known as CD4-CD8- double negative 1 (DN1). These thymocytes have previously been revealed to be a heterogenous mixture of cells, which of only the CD117+ fraction have been proposed to be true T cell progenitors that progress to the DN2 and DN3 thymocyte stages, at which point the development of the {beta} and {gamma}{delta} T cell lineages diverge. However, recently, it has been shown that at least some {gamma}{delta} T cells are actually derived from a subset of CD117- DN thymocytes. Along with other ambiguities, this suggests that T cell development may not be as straightforward as previously thought. To better understand early T cell development, particularly the heterogeneity of DN1 thymocytes, we performed single cell RNA sequence (scRNAseq) of mouse DN and {gamma}{delta} thymocytes and show that the various DN stages are indeed comprised of transcriptionally diverse subpopulations of cells. We also show that multiple subpopulations of DN1 thymocytes exhibit preferential development towards the {gamma}{delta} lineage. Furthermore, specific {gamma}{delta}-primed DN1 subpopulations preferentially develop into IL-17 or IFN{gamma}-producing {gamma}{delta} T cells. We show that DN1 subpopulations that only give rise to IL-17-producing {gamma}{delta} T cells already express many of the transcription factors associated with type 17 immune cell differentiation, while the DN1 subpopulations that can give rise to IFN{gamma}-producing {gamma}{delta} T cell already express transcription factors associated with type 1 immune cell differentiation.

immunology↗

bioRxiv2022

De novo mass spectrometry peptide sequencing with a transformer model

Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids--the peptide--responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods.

bioinformatics↗

bioRxiv2021

The energy requirements of ion homeostasis determine the lifespan of starving bacteria

The majority of microbes on earth, whether they live in the ocean, the soil or in animals, are not growing, but instead struggling to survive starvation1-6. Some genes and environmental conditions affecting starvation survival have been identified7-13, but despite almost a century of study14-16, we do not know which processes lead to irreversible loss of viability, which maintenance processes counteract them and how lifespan is determined from the balance of these opposing processes. Here, we used time-lapse microscopy to capture and characterize the cell death process of E. coli during carbon starvation for the first time. We found that a lack of nutrients results in the collapse of ion homeostasis, triggering a positive-feedback cascade of osmotic swelling and membrane permeabilization that ultimately results in lysis. Based on these findings, we hypothesized that ion transport is the major energetic requirement for starving cells and the primary determinant of the timing of lysis. We therefore developed a mathematical model that integrates ion homeostasis and cannibalistic nutrient recycling from perished cells16,17 to predict lifespan changes under diverse conditions, such as changes of cell size, medium composition, and prior growth conditions. Guided by model predictions, we found that cell death during starvation could be dramatically slowed by replacing inorganic ions from the medium with a non-permeating osmoprotectant, removing the cost of ion homeostasis and preventing lysis. Our quantitative and predictive model explains how survival kinetics are determined in starvation and elucidates the mechanistic underpinnings of starvation survival.

microbiology↗