Biology subjects

Katz, S.

Publications and source records attributed to Katz, S..

17 recordsLinked to original sources

bioRxiv2026

Structural Basis for Near Infrared Absorption and Noncanonical Photoisomerization in Neorhodopsin

In retinal proteins, light absorption leads to a double bond isomerization of the chromophore, followed by conformational relaxations that drive functional protein rearrangements. The balance between photoisomerization and radiative or non-radiative decays of the excited state is adjusted to the demands of the respective photoreceptor function. The molecular determinants controlling the excited state processes remain poorly understood and are the focus of this combined crystallographic, spectroscopic, and theoretical work on the recently discovered neorhodopsin (NeoR). NeoR exhibits a highly red-shifted and fluorescent dark state and selectively photoisomerizes from all-trans to 7-cis. We show that the local electrostatic environment reduces the negative charge density near the Schiff base and suppresses torsional motion around the canonical C13=C14 isomerization site of microbial rhodopsins. Instead, the chromophore-binding pocket permits only low-efficiency C7=C8 isomerization, accompanied by polyene inversion, {beta}-ionone ring displacement, and Schiff base deprotonation. Consequently, unlike other retinal photoreceptors, NeoR undergoes structural changes localized near the {beta}-ionone ring.

biophysics↗

bioRxiv2025

Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissue

Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, compositions, interactions, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution. Leading spatial-capture or spatial-tagging transcriptomics techniques that rely on in-vitro sequencing offer whole-transcriptome coverage, in principle, but at the cost of lower spatial resolution compared to image-based techniques. In contrast, high-performance image-based spatial transcriptomics techniques, which rely on in situ hybridization or in situ sequencing, achieve single-molecule spatial resolution and retain sub-cellular morphologies, but are limited by probe libraries that target only a subset of the transcriptome, typically covering several hundred to a few thousand transcript species. Together, these limitations hinder unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop a new image-based spatial transcriptomics technology termed Reverse-padlock Amplicon Encoding FISH (RAEFISH) with whole-genome level coverage while retaining single-molecule spatial resolution in intact tissues. We demonstrate image-based spatial transcriptomics targeting 23,000 human transcript species or 22,000 mouse transcript species, including nearly the entire protein-coding transcriptome and several thousand long-noncoding RNAs, in single cells in cultures and in tissue sections. Our analyses reveal differential subcellular localizations of diverse transcripts, cell-type-specific and cell-type-invariant tissue zonation dependent transcriptome, and gene expression programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.

genetics↗

bioRxiv2024

Escherichia coli adaptation under prolonged resource exhaustion is characterized by extreme parallelism and frequent historical contingency

Like many other non-sporulating bacterial species, Escherichia coli is able to survive prolonged periods of resource exhaustion, by entering a state of growth called long-term stationary phase (LTSP). In July 2015, we initiated a set of evolutionary experiments aimed at characterizing the dynamics of E. coli adaptation under LTSP. In these experiments populations of E. coli were allowed to initially grow on fresh rich media, but where not provided with any new external growth resources since their establishment. Utilizing whole genome sequencing data obtained for hundreds of clones sampled at 12 time points spanning the first six years of these experiments, we reveal several novel aspects of the dynamics of adaptation. First, we show that E. coli continuously adapts genetically, up to six years under resource exhaustion, through the highly convergent accumulation of mutations. We further show that upon entry into LTSP, long-lasting lineages are established. This lineage structure is in itself convergent, with similar lineages arising across independently evolving populations. The high parallelism with which adaptations occur under LTSP, combined with the LTSP populations lineage structure, enable us to screen for pairs of loci displaying a significant association in the occurrence of mutations, suggestive of a historical contingency. We find that such associations are highly frequent and that a third of convergently mutated loci are involved in at least one such association. Combined our results demonstrate that LTSP adaptation is characterized by remarkably high parallelism and frequent historical contingency. Author summaryCharacterizing the dynamics by which adaptation occurs is a major aim of evolutionary biology. Here, we study these dynamics in five populations of Escherichia coli, independently evolving over six years under conditions of resource exhaustion. We show that even under very prolonged resource exhaustion bacteria continuously genetically adapt. Within our populations long lasting lineages are established, each of which undergoes independent and continuous adaptation. We demonstrate that bacterial adaptation under resource exhaustion is both highly convergent - meaning that same adaptive mutations occur across independently evolving populations and lineages, and frequently historically contingent - meaning that the adaptive nature of many of the adaptations we see depends on the previous occurrence of other adaptations.

evolutionary biology↗

bioRxiv2024

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyp- ing and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders - external factors unrelated to the condition, e.g. batch effect or age - on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include: i) removal of latent features correlated with confounders ii) a conditional variational autoencoder, iii) adversarial training, and iv) adding a regularization term to the loss function. Using real-life multi-omics data from TCGA, we simulated various confounding effects (linear, non-linear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically-driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine.

genomics↗

bioRxiv2023

Pseudomonas Putida Dynamics of Adaptation under Prolonged Resource Exhaustion

Many non-sporulating Bacterial species survive prolonged resource exhaustion, by entering a state termed long-term stationary phase (LTSP). Here, we performed LTSP evolutionary experiments on the bacterium Pseudomonas putida, followed by whole genome sequencing of evolved clones. We show that P. putida is able to persist and adapt genetically under LTSP. We observed a gradual accumulation of mutations within the evolving P. putida populations. Within each population, independently evolving lineages are established early on and persist throughout the four-month-long experiment. Mutations accumulate in a highly convergent manner, with similar loci being mutated across independently evolving populations. Across populations, mutators emerge, that due to mutations within mismatch repair genes developed a much higher rate of mutation than other clones with which they co-existed within their respective populations. While these general dynamics of the adaptive process are quite similar to those we previously observed in the model bacterium Escherichia coli, the specific loci that are involved in adaptation only partially overlap between P. putida and E. coli. Significance statementBacteria often face conditions of prolonged nutrient limitation, following periods of growth. One strategy for dealing with this challenge is entry into a state termed long-term stationary phase (LTSP), in which a small minority of cells within a population can survive and persist, by recycling the remains of their deceased brethren. Here, we broaden our understanding of adaptation under LTSP, by studying it in the bacterium Pseudomonas putida. We show that many of the dynamics of LTSP genetic adaptation are quite general, as reflected by great similarity to what was previously observed in the model bacterium Escherichia coli. However, the specific loci that are involved in adaptation substantially vary between P. putida and E. coli.

evolutionary biology↗

bioRxiv2023

mEthAE: an Explainable AutoEncoder for methylation data

1In the quest to unravel the mysteries of our epigenetic landscape, researchers are continually challenged by the relationships among CpG sites. Traditional approaches are often limited by the immense complexity and high dimensionality of DNA methylation data. To address this problem, deep learning algorithms, such as autoencoders, are increasingly applied to capture the complex patterns and reduce dimensionality into latent space. In this pioneering study, we introduce an innovative chromosome-wise autoencoder, termed mEthAE, specifically designed for the interpretive reduction of methylation data. mEthAE achieves an impressive 400-fold reduction in data dimensions without compromising on reconstruction accuracy or predictive power in the latent space. In attempt to go beyond mere data compression, we developed a perturbation-based method for interpretation of latent dimensions. Through our approach we identified clusters of CpG sites that exhibit strong connections across all latent dimensions, which we refer to as global CpGs. Remarkably, these global CpGs are more frequently highlighted in epigenome-wide association studies (EWAS), suggesting our methods ability to pinpoint biologically significant CpG sites. Our findings reveal a surprising lack of correlation patterns, or even physical proximity on the chromosome among these connected CpGs. This leads us to propose an intriguing hypothesis: our autoencoder may be detecting complex, long-range, non-linear interaction patterns among CpGs. These patterns, largely uncharacterised in current epigenetic research, hold the potential to shed new light on our understanding of epigenetics. In conclusion, this study not only showcases the power of autoencoders in untangling the complexities of epigenetic data but also opens up new avenues for understanding the hidden connections within CpGs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/549496v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@16c5076org.highwire.dtl.DTLVardef@16b455org.highwire.dtl.DTLVardef@9948e8org.highwire.dtl.DTLVardef@181914b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Multi-omic Profiling Reveals Early Immunological Indicators for Identifying COVID-19 Progressors

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a rapid response by the scientific community to further understand and combat its associated pathologic etiology. A focal point has been on the immune responses mounted during the acute and post-acute phases of infection, but the immediate post-diagnosis phase remains relatively understudied. We sought to better understand the immediate post-diagnosis phase by collecting blood from study participants soon after a positive test and identifying molecular associations with longitudinal disease outcomes. Multi-omic analyses identified differences in immune cell composition, cytokine levels, and cell subset-specific transcriptomic and epigenomic signatures between individuals on a more serious disease trajectory (Progressors) as compared to those on a milder course (Non-progressors). Higher levels of multiple cytokines were observed in Progressors, with IL-6 showing the largest difference. Blood monocyte cell subsets were also skewed, showing a comparative decrease in non-classical CD14-CD16+ and intermediate CD14+CD16+ monocytes. Additionally, in the lymphocyte compartment, CD8+ T effector memory cells displayed a gene expression signature consistent with stronger T cell activation in Progressors. Importantly, the identification of these cellular and molecular immune changes occurred at the early stages of COVID-19 disease. These observations could serve as the basis for the development of prognostic biomarkers of disease risk and interventional strategies to improve the management of severe COVID-19. One Sentence SummaryImmunological changes associated with COVID-19 progression can be detected during the early stages of infection.

immunology↗

bioRxiv2023

Compensatory adaptation does not fully alleviate the costs associated with rifampicin resistance and occurs predominantly through off-target mutations

The manner in which costs associated with antibiotic resistance mutations are alleviated through the acquisition of compensatory mutations has bearing on the dynamics of resistance accumulation and spread as well as on our more general understanding of the persistence of adaptive genotypes. Here, we combine evolutionary experiments, fitness analyses, and whole genome sequencing to examine the way compensation to alleviate costs associated with rifampicin resistance is achieved, both when resistance is fixed and when initial majorities of resistant cells are grown alongside susceptible cells. We found compensatory mutations to occur across all examined populations. However, compensation never fully alleviates the initial costs of resistance. In contrast to prevalent assumptions, compensatory mutations mostly occurred outside of the RNA polymerase core enzyme, which serves as the target of rifampicin. The target size for compensatory mutations appears to be high, as great variation exists in compensatory mutations, even within a single population. At the same time, the pathways of compensation are quite convergent, meaning that, across independent populations, we often observe compensatory mutations in the same loci and sometimes even observe the precise same compensatory mutations across populations.

evolutionary biology↗

bioRxiv2022

Population level genetic memory of prior metabolic adaptation in E. coli

Bacteria must often survive following the exhaustion of their external growth resources. Fitting with this need, many bacterial species that cannot sporulate, can enter a state known as long term stationary phase (LTSP) in which they can persist for years within spent media. Several recent studies have revealed the dynamics of genetic adaptation of Escherichia coli under LTSP. Yet, the metabolic consequences of such genetic adaptation were not addressed. Here, we characterized the metabolic changes LTSP populations experience and link them to their genetic adaptation. We observed that during growth within fresh resources E. coli produces the short chain fatty acid butyrate, which wildtype E. coli cannot consume. Once resources are otherwise exhausted, E. coli adapts genetically to consume butyrate through the convergent, temporally precise emergence of mutation combinations within genes that regulate fatty acid metabolism. These mutations appear to negatively affect bacterial fitness when butyrate is not available, and hence rapidly decrease in frequency, once all butyrate is consumed. Yet despite this, E. coli populations show a remarkable capability of maintaining a population-level genetic memory of prior adaptation to consume butyrate. The maintenance of such a memory allows bacteria to rapidly re-adapt, at an ecological, rather than an evolutionary timeframe, to re-consume previously encountered metabolites.

evolutionary biology↗

bioRxiv2022

From pseudotime to true dynamics: reconstructing a real-time axis for T cells differentiation

Numerous methods have recently emerged for ordering single cells along developmental trajectories. However, accurate depiction of developmental dynamics can only be achieved after rescaling the trajectory according to the relative time spent at each developmental point. We formulate a model which estimates local cell densities and fluxes, and incorporates cell division and apoptosis rates, to infer the real time dimension of the developmental trajectory. We validate the model using mathematical simulations, and apply it on experimental high dimensional cytometry data obtained from the mouse thymus to construct the true time-profile of the thymocyte developmental process. Our method can easily be implemented in any of the existing tools for trajectory inference.

systems biology↗

bioRxiv2021

Selective whole genome amplification as a tool to enrich specimens with low Treponema pallidum genomic DNA copies for whole genome sequencing

Downstream next generation sequencing (NGS) of the syphilis spirochete Treponema pallidum subspecies pallidum (T. pallidum) is hindered by low bacterial loads and the overwhelming presence of background metagenomic DNA in clinical specimens. In this study, we investigated selective whole genome amplification (SWGA) utilizing multiple displacement amplification (MDA) in conjunction with custom oligonucleotides with an increased specificity for the T. pallidum genome, and the capture and removal of CpG-methylated host DNA using the NEBNext(R) Microbiome DNA Enrichment Kit followed by MDA with the REPLI-g Single Cell Kit as enrichment methods to improve the yields of T. pallidum DNA in isolates and lesion specimens from syphilis patients. Sequencing was performed using the Illumina MiSeq v2 500 cycle or NovaSeq 6000 SP platform. These two enrichment methods led to 93-98% genome coverage at 5 reads/site in 5 clinical specimens from the United States and rabbit propagated isolates, containing >14 T. pallidum genomic copies/l of sample for SWGA and >129 genomic copies/l for CpG methylation capture with MDA. Variant analysis using sequencing data derived from SWGA-enriched specimens, showed that all 5 clinical strains had the A2058G mutation associated with azithromycin resistance. SWGA is a robust method that allows direct whole genome sequencing (WGS) of specimens containing very low numbers of T. pallidum, which have been challenging until now. ImportanceSyphilis is a sexually transmitted, disseminated acute and chronic infection caused by the bacterial pathogen Treponema pallidum subspecies pallidum. Primary syphilis typically presents as single or multiple mucocutaneous lesions, and if left untreated, can progress through multiple stages with varied clinical manifestations. Molecular studies often rely on direct amplification of DNA sequences from clinical specimens; however, this can be impacted by inadequate samples due to disease progression or timing of patients seeking clinical care. While genotyping has provided important data on circulating strains over the past two decades, WGS data is needed to better understand strain diversity, perform evolutionary tracing, and monitor antimicrobial resistance markers. The significance of our research is the development of a SWGA DNA enrichment method that expands the range of clinical specimens that can be directly sequenced to include samples with low numbers of T. pallidum.

genomics↗

bioRxiv2021

Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains

Although closely related genetically, bacterial strains belonging to the same species show significant variability in their growth and death dynamics. However, our understanding of the underlying processes that lead to this variability is still lacking. Here, we measured the growth and death dynamics of 11 strains of E. coli originating from different hosts and developed a mathematical model that captures their growth and death dynamics. Our model considers two environmental factors that determine growth dynamics: resource utilization efficiency and density-dependent growth inhibition. Here we show that both factors are required to capture the measured dynamics. Interestingly, our model results indicate that the main process that determines the major differences between the strains is the critical density at which they slow down their growth, rather than maximal growth rate or death rate. Finally, we found that bacterial growth and death dynamics can be reduced to only two dimensions and described by death rates and density-dependent growth inhibition alone. ImportanceUnderstanding the dynamics of bacterial growth has been an area of intense study. However, these dynamics have often been characterized through the narrow prism of describing growth rates, without considering parameters that may modulate these rates. Here, we generate a model that describes bacterial growth and death dynamics, incorporating two essential, growth-modulating factors: density-dependent reductions in growth rates and resource utilization efficiency. This model allows us to demonstrate that variation in the growth curves of closely related bacterial strains can be reduced to two dimensions and explained almost entirely by variation in the cellular density at which bacteria slow down their growth, combined with their death rates.

microbiology↗

bioRxiv2020

Culture volume influences the dynamics of adaptation under long-term stationary phase

Escherichia coli and many other bacterial species, which are incapable of sporulation, can nevertheless survive within resource exhausted media by entering a state termed long-term stationary phase (LTSP). We have previously shown that E. coli populations adapt genetically under LTSP in an extremely convergent manner. Here we examine how the dynamics of LTSP genetic adaptation are influenced by varying a single parameter of the experiment - culture volume. We find that culture volume affects survival under LTSP, with viable counts decreasing as volumes increase. Across all volumes, mutations accumulate with time, and the majority of mutations accumulated demonstrate signals of being adaptive. However, positive selection appears to affect mutation accumulation more strongly at higher, compared to lower volumes. Finally, we find that several similar genes are likely involved in adaptation across volumes. However, the specific mutations within these genes that contribute to adaptation can vary in a consistent manner. Combined, our results demonstrate how varying a single parameter of an evolutionary experiment can substantially influence the dynamics of observed adaptation.

evolutionary biology↗

bioRxiv2020

Dynamics of mutation accumulation and adaptation during three years of evolution under long-term stationary phase

Many bacterial species that cannot sporulate, such as the model bacterium Escherichia coli, can nevertheless survive for years under resource exhaustion, in a state termed long-term stationary phase (LTSP). Here we describe the dynamics of E. coli adaptation during the first three years spent under LTSP. We show that during this time E. coli continuously adapts genetically, through the accumulation of mutations. For non-mutator clones, the majority of mutations accumulated appear to be adaptive under LTSP, reflected in an extremely convergent pattern of mutation accumulation. Despite the rapid and convergent manner in which populations adapt under LTSP, they continue to harbor extensive genetic variation. The dynamics of evolution of mutation rates under LTSP are particularly interesting. The emergence of mutators, affects overall mutation accumulation rates as well as the mutational spectra and the ultimate spectrum of adaptive alleles acquired under LTSP. With time, mutators can evolve even higher mutation rates, through the acquisition of additional mutation-rate enhancing mutations. Different mutator and non-mutator clones within a single population and time point can display extreme variation in their mutation rates, resulting in differences in both the dynamics of adaptation and their associated deleterious burdens. Despite these differences, clones that vary greatly in their mutation rates tend to co-exist within their populations for many years, under LTSP.

evolutionary biology↗

bioRxiv2020

TRIAGE: A web-based iterative analysis platform integrating pathway and network databases optimizes hit selection from high- throughput assays.

Comprehensive and efficient gene hit selection from high throughput assays remains a critical bottleneck in realizing the potential of genome-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs, and are often combined in an ad hoc manner. The specific limitations of these individual approaches, the lack of a systematic way by which to integrate their rankings, and the inaccessibility of complex computational approaches to many researchers, has contributed to unexpected variability and limited overlap in the reported results from comparable genome-wide studies. Using a set of three highly studied genome-wide datasets for HIV host factors that have been broadly cited for their limited number of shared candidates, we characterize the specific complementary contributions of commonly used analysis approaches and find an optimal framework by which to integrate these methods. We describe Throughput Ranking by Iterative Analysis of Genomic Enrichment (TRIAGE), an integrated, iterative approach which uses pathway and network statistical methods and publicly available databases to optimize gene prioritization. TRIAGE is accessible as a secure, rapid, user-friendly web-based application (https://triage.niaid.nih.gov). O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/204917v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@7150f8org.highwire.dtl.DTLVardef@17af411org.highwire.dtl.DTLVardef@12d8f01org.highwire.dtl.DTLVardef@78c48_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioinformatics↗

bioRxiv2020

Adaptations accumulated under prolonged resource exhaustion are highly transient

Many non-sproulating bacterial species can survive for years within exhausted growth media in a state termed long-term stationary phase (LTSP). We have been carrying out evolutionary experiments aimed at elucidating the dynamics of genetic adaptation under LTSP. We showed that Escherichia coli adapts to prolonged resource exhaustion through the highly convergent acquisition of mutations. In the most striking example of such convergent adaptation, we observed that across all independently evolving LTSP populations, over 90% of E. coli cells carry mutations to one of three specific sites of the RNA polymerase core enzyme (RNAPC). These LTSP adaptations reduce the ability of the cells carrying them to grow once fresh resources are again provided. Here, we examine how LTSP populations recover from costs associated with their adaptation, once resources are again provided to them. We demonstrate that due to the ability of LTSP populations to maintain high levels of standing genetic variation during adaptation, costly adaptations are very rapidly purged from the population once they are provided with fresh resources. We further demonstrate that recovery from costs acquired during adaptation under LTSP occurs much more rapidly than would be possible if LTSP adaptations had fixed during the time populations spent under resource exhaustion. Finally, we previously reported that under LTSP, some clones develop a mutator phenotype, greatly increasing their mutation accumulation rates. Here, we show that the mechanisms, by which populations recover from costs associated with fixed adaptations, may depend on mutator status.

evolutionary biology↗

bioRxiv2020

Phosphorylation of Threonine 107 by Calcium/Calmodulin dependent Kinase II δ Regulates the Detoxification Efficiency and Proteomic Integrity of Glyoxalase 1

The glyoxalase system is a ubiquitously expressed enzyme system with narrow substrate specificity and is responsible for the detoxification of harmful methylglyoxal (MG), a spontaneous by-product of energy metabolism. Glyoxalase 1 (Glo1) is the first and therefore rate limiting enzyme of this protective system. In this study we were able to show that a phosphorylation of threonine-107 in the Glo1 protein, mediated by Ca2+/Calmodulin-dependent Kinase II delta (CamKII{delta}), is associated with elevated catalytic efficiency of Glo1. In fact, Michaelis-Menten kinetics of Glo1 mutants revealed that a permanent phosphorylation of Glo1 was associated with increased Vmax (1.23 {micro}mol/min/mg) and decreased Km (0.19 mM HTA), whereas the non-phosphorylatable Glo1 showed significantly lower Vmax (0.66 {micro}mol/min/mg) and increased Km (0.31 mM HTA). This was also confirmed with human recombinant Glo1 (Vmax (Glo1phos) = 999 {micro}mol/min/mg; Km (Glo1phos) = 0.09 mM HTA vs. Vmax (Glo1red) = 497 {micro}mol/min/mg; Km (Glo1red) = 0.12 mM HTA). Additionally, proteasomal degradation of non-phosphorylated Glo1 via ubiquitination occurred more rapidly as compared to native Glo1. The absence of the responsible kinase CamKII{delta} was associated with poor MG detoxification capacity and decreased protein content of Glo1 in a murine CamKII{delta} knock-out model. Furthermore, this regulatory mechanism is also related to an altered Glo1 status in cancer, diabetes and during aging. In summary, phosphorylation of threonine-107 in the Glo1 protein by CamKII{delta} is a quick and precise mechanism regulating Glo1 activity.

molecular biology↗