Plant Pathol J. 2023 Dec;39(6):584-591. doi: 10.5423/PPJ.FT.09.2023.0125. Epub 2023 Dec 1.

ABSTRACT

Active plant immune response involving programmed cell death called the hypersensitive response (HR) is elicited by microbial effectors delivered through the type III secretion system (T3SS). The marine bacterium Hahella chejuensis contains two T3SSs that are similar to those of animal pathogens, but it was able to elicit HR-like cell death in the land plant Nicotiana benthamiana. The cell death was comparable with the transcriptional patterns of H. chejuensis T3SS-1 genes, was mediated by SGT1, a general regulator of plant resistance, and was suppressed by AvrPto1, a type III-secreted effector of a plant pathogen that inhibits HR. Thus, type III-secreted effectors of a marine bacterium are capable of inducing the nonhost HR in a land plant it has never encountered before. This suggests that plants may have evolved to cope with a potential threat posed by alien pathogen effectors. Our work documents an exceptional case of nonhost HR and provides an expanded perspective for studying plant nonhost resistance.

PMID:38081318 | DOI:10.5423/PPJ.FT.09.2023.0125

Elife. 2023 Dec 11;12:RP87719. doi: 10.7554/eLife.87719.

ABSTRACT

Many species are able to recognize objects, but it has been proven difficult to pinpoint and compare how different species solve this task. Recent research suggested to combine computational and animal modelling in order to obtain a more systematic understanding of task complexity and compare strategies between species. In this study, we created a large multidimensional stimulus set and designed a visual discrimination task partially based upon modelling with a convolutional deep neural network (CNN). Experiments included rats (N = 11; 1115 daily sessions in total for all rats together) and humans (N = 45). Each species was able to master the task and generalize to a variety of new images. Nevertheless, rats and humans showed very little convergence in terms of which object pairs were associated with high and low performance, suggesting the use of different strategies. There was an interaction between species and whether stimulus pairs favoured early or late processing in a CNN. A direct comparison with CNN representations and visual feature analyses revealed that rat performance was best captured by late convolutional layers and partially by visual features such as brightness and pixel-level similarity, while human performance related more to the higher-up fully connected layers. These findings highlight the additional value of using a computational approach for the design of object recognition tasks. Overall, this computationally informed investigation of object recognition behaviour reveals a strong discrepancy in strategies between rodent and human vision.

PMID:38079481 | DOI:10.7554/eLife.87719

PLoS Comput Biol. 2023 Dec 11;19(12):e1011711. doi: 10.1371/journal.pcbi.1011711. Online ahead of print.

ABSTRACT

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

PMID:38079453 | DOI:10.1371/journal.pcbi.1011711

ACS Nano. 2023 Dec 11. doi: 10.1021/acsnano.3c09224. Online ahead of print.

ABSTRACT

Metal single-atom catalysts (MSACs) possess multiple advantages in chemical synthesis; their efficient fabrication routes, however, remain a challenge to date. Here, an interdisciplinary design using M13 bacteriophage virus as a biotemplate to carry Fe nanoclusters, which we figuratively call "Fe-nanonests", is proposed to enable facile and versatile synthesis of MSACs. The feasibility and generality of this self-assembly method was demonstrated by the observation of six different metal single atoms (MSAs) including Ag, Pt, Pd, Zn, Cu, and Ni. With Pd as a representative, key factors dominating the fabrication were determined. The Pd single atoms exhibited excellent horseradish peroxidase (HRP)-like activity, which was further improved by 50% via genetic editing of the M13 pVIII protein terminals. Excellent stability was also observed in the quantification of acid phosphatase, a cancer predictor. X-ray absorption near-edge structure spectroscopy has been applied to the analysis of Pd single atoms as well, and the Pd-N4 coordination explained the mechanism of high HRP-like catalytic activity. The MSAs synthesized by the M13 phage and Fe-nanonest self-assembly method show promising prospects in non-cold-chain medical detection applications.

PMID:38079359 | DOI:10.1021/acsnano.3c09224

Elife. 2023 Dec 11;12:RP90027. doi: 10.7554/eLife.90027.

ABSTRACT

Many classes of drugs can induce fatal cardiac arrhythmias by disrupting the electrophysiology of cardiomyocytes. Safety guidelines thus require all new drugs to be assessed for pro-arrhythmic risk prior to conducting human trials. The standard safety protocols primarily focus on drug blockade of the delayed-rectifier potassium current (IKr). Yet the risk is better assessed using four key ion currents (IKr, ICaL, INaL, IKs). We simulated 100,000 phenotypically diverse cardiomyocytes to identify the underlying relationship between the blockade of those currents and the emergence of ectopic beats in the action potential. We call that relationship the axis of arrhythmia. It serves as a yardstick for quantifying the arrhythmogenic risk of any drug from its profile of multi-channel block alone. We tested it on 109 drugs and found that it predicted the clinical risk labels with an accuracy of 88.1-90.8%. Pharmacologists can use our method to assess the safety of novel drugs without resorting to animal testing or unwieldy computer simulations.

PMID:38079357 | DOI:10.7554/eLife.90027

JMIR Med Inform. 2023 Dec 11;11:e50017. doi: 10.2196/50017.

ABSTRACT

BACKGROUND: In molecular tumor boards (MTBs), patients with rare or advanced cancers are discussed by a multidisciplinary team of health care professionals. Software support for MTBs is lacking; in particular, tools for preparing and documenting MTB therapy recommendations need to be developed.

OBJECTIVE: We aimed to implement an extension to cBioPortal to provide a tool for the documentation of therapy recommendations from MTB sessions in a secure and standardized manner. The developed extension should be embedded in the patient view of cBioPortal to enable easy documentation during MTB sessions. The resulting architecture for storing therapy recommendations should be integrable into various hospital information systems.

METHODS: On the basis of a requirements analysis and technology analysis for authentication techniques, a prototype was developed and iteratively refined through a user-centered development process. In conclusion, the tool was evaluated via a usability evaluation, including interviews, structured questionnaires, and the System Usability Scale.

RESULTS: The patient view of cBioPortal was extended with a new tab that enables users to document MTB sessions and therapy recommendations. The role-based access control was expanded to allow for a finer distinction among the rights to view, edit, and delete data. The usability evaluation showed overall good usability and a System Usability Scale score of 83.57.

CONCLUSIONS: This study demonstrates how cBioPortal can be extended to not only visualize MTB patient data but also be used as a documentation platform for therapy recommendations.

PMID:38079196 | DOI:10.2196/50017

mSystems. 2023 Dec 11:e0002623. doi: 10.1128/msystems.00026-23. Online ahead of print.

ABSTRACT

Microbes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.

PMID:38078749 | DOI:10.1128/msystems.00026-23

J Biomed Opt. 2023 Nov;28(11):116502. doi: 10.1117/1.JBO.28.11.116502. Epub 2023 Nov 17.

ABSTRACT

SIGNIFICANCE: HiLo microscopy synthesizes an optically sectioned image from two images, one obtained with uniform and another with patterned illumination, such as laser speckle. Speckle-based HiLo has the advantage of being robust to aberrations but is susceptible to residual speckle noise that is difficult to control. We present a computational method to reduce this residual noise without undermining resolution. In addition, we improve the versatility of HiLo microscopy by enabling simultaneous multiplane imaging (here nine planes).

AIM: Our goal is to perform fast, high-contrast, multiplane imaging with a conventional camera-based fluorescence microscope.

APPROACH: Multiplane HiLo imaging is achieved with the use of a single camera and z-splitter prism. Speckle noise reduction is based on the application of a non-local means (NLM) denoising method to perform ensemble averaging of speckle grains.

RESULTS: We demonstrate the capabilities of multiplane HiLo with NLM denoising both with synthesized data and by imaging cardiac and brain activity in zebrafish larvae at 40 Hz frame rates.

CONCLUSIONS: Multiplane HiLo microscopy aided by NLM denoising provides a simple tool for fast optically sectioned volumetric imaging that can be of general utility for fluorescence imaging applications.

PMID:38078150 | PMC:PMC10704089 | DOI:10.1117/1.JBO.28.11.116502

Front Plant Sci. 2023 Nov 23;14:1273958. doi: 10.3389/fpls.2023.1273958. eCollection 2023.

ABSTRACT

Plants have been explored as a platform to produce pharmaceutical proteins for over 20 years. Important features such as the cost-effectiveness of production, the ease of scaling up to manufacturing capacity, the lack of cold chain requirements and the ability to produce complex therapeutic proteins which are biologically and functionally identical to their mammalian counterparts, make plants a strong alternative for vaccine production. This review article focuses on both the expression as well as the downstream purification processes for plant made vaccines. Expression strategies including transgenic, transient and cell suspension cultures are outlined, and various plant tissues targeted such as leaves and seeds are described. The principal components used for downstream processing of plant made vaccines are examined. The review concludes with a reflection of the future benefits of plant production platforms for vaccine production.

PMID:38078091 | PMC:PMC10701439 | DOI:10.3389/fpls.2023.1273958

Front Plant Sci. 2023 Nov 23;14:1140829. doi: 10.3389/fpls.2023.1140829. eCollection 2023.

ABSTRACT

INTRODUCTION: Flux phenotypes from different organisms and growth conditions allow better understanding of differential metabolic networks functions. Fluxes of metabolic reactions represent the integrated outcome of transcription, translation, and post-translational modifications, and directly affect growth and fitness. However, fluxes of intracellular metabolic reactions cannot be directly measured, but are estimated via metabolic flux analysis (MFA) that integrates data on isotope labeling patterns of metabolites with metabolic models. While the application of metabolomics technologies in photosynthetic organisms have resulted in unprecedented data from 13CO2-labeling experiments, the bottleneck in flux estimation remains the application of isotopically nonstationary MFA (INST-MFA). INST-MFA entails fitting a (large) system of coupled ordinary differential equations, with metabolite pools and reaction fluxes as parameters. Here, we focus on the Calvin-Benson cycle (CBC) as a key pathway for carbon fixation in photosynthesizing organisms and ask if approaches other than classical INST-MFA can provide reliable estimation of fluxes for reactions comprising this pathway.

METHODS: First, we show that flux estimation with the labeling patterns of all CBC intermediates can be formulated as a single constrained regression problem, avoiding the need for repeated simulation of time-resolved labeling patterns.

RESULTS: We then compare the flux estimates of the simulation-free constrained regression approach with those obtained from the classical INST-MFA based on labeling patterns of metabolites from the microalgae Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii under different growth conditions.

DISCUSSION: Our findings indicate that, in data-rich scenarios, simulation-free regression-based approaches provide a suitable alternative for flux estimation from classical INST-MFA since we observe a high qualitative agreement (rs=0.89) to predictions obtained from INCA, a state-of-the-art tool for INST-MFA.

PMID:38078077 | PMC:PMC10702240 | DOI:10.3389/fpls.2023.1140829

Front Cell Dev Biol. 2023 Nov 24;11:1235116. doi: 10.3389/fcell.2023.1235116. eCollection 2023.

ABSTRACT

Background: The concept of the latent geometry of a network that can be represented as a graph has emerged from the classrooms of mathematicians and theoretical physicists to become an indispensable tool for determining the structural and dynamic properties of the network in many application areas, including contact networks, social networks, and especially biological networks. It is precisely latent geometry that we discuss in this article to show how the geometry of the metric space of the graph representing the network can influence its dynamics. Methods: We considered the transcriptome network of the Chronic Myeloid Laeukemia K562 cells. We modelled the gene network as a system of springs using a generalization of the Hooke's law to n-dimension (n ≥ 1). We embedded the network, described by the matrix of spring's stiffnesses, in Euclidean, hyperbolic, and spherical metric spaces to determine which one of these metric spaces best approximates the network's latent geometry. We found that the gene network has hyperbolic latent geometry, and, based on this result, we proceeded to cluster the nodes according to their radial coordinate, that in this geometry represents the node popularity. Results: Clustering according to radial coordinate in a hyperbolic metric space when the input to network embedding procedure is the matrix of the stiffnesses of the spring representing the edges, allowed to identify the most popular genes that are also centres of effective spreading and passage of information through the entire network and can therefore be considered the drivers of its dynamics. Conclusion: The correct identification of the latent geometry of the network leads to experimentally confirmed clusters of genes drivers of the dynamics, and, because of this, it is a trustable mean to unveil important information on the dynamics of the network. Not considering the latent metric space of the network, or the assumption of a Euclidean space when this metric structure is not proven to be relevant to the network, especially for complex networks with hierarchical or modularised structure can lead to unreliable network analysis results.

PMID:38078013 | PMC:PMC10704170 | DOI:10.3389/fcell.2023.1235116

PeerJ. 2023 Dec 6;11:e16481. doi: 10.7717/peerj.16481. eCollection 2023.

ABSTRACT

BACKGROUND: Exosomes, microvesicles, carry and release several vital molecules across cells, tissues, and organs. Epicardial adipose tissue exosomes are critical in the development and progression of coronary artery disease (CAD). It is hypothesized that exosomes may transport causative molecules from inflamed tissue and deliver to the target tissue and progress CAD. Thus, identifying and inhibiting the CAD-associated proteins that are being transported to other cells via exosomes will help slow the progression of CAD.

METHODS: This study uses a systems biological approach that integrates differential gene expression in the CAD, exosomal cargo assessment, protein network construction, and functional enrichment to identify the crucial exosomal cargo protein target. Meanwhile, absorption, distribution, metabolism, and excretion (ADME) screening of Panax ginseng-derived compounds was conducted and then docked against the protein target to identify potential inhibitors and then subjected to molecular dynamics simulation (MDS) to understand the behavior of the protein-ligand complex till 100 nanoseconds. Finally, density functional theory (DFT) calculation was performed on the ligand with the highest affinity with the target.

RESULTS: Through the systems biological approach, Mothers against decapentaplegic homolog 2 protein (SMAD2) was determined as a potential target that linked with PI3K-Akt signaling, Ubiquitin mediated proteolysis, and the focal adhesion pathway. Further, screening of 190 Panax ginseng compounds, 27 showed drug-likeness properties. Inermin, a phytochemical showed good docking with -5.02 kcal/mol and achieved stability confirmation with SMAD2 based on MDS when compared to the known CAD drugs. Additionally, DFT analysis of inermin showed high chemical activity that significantly contributes to effective target binding. Overall, our computational study suggests that inermin could act against SMAD2 and may aid in the management of CAD.

PMID:38077444 | PMC:PMC10710165 | DOI:10.7717/peerj.16481

Front Immunol. 2023 Nov 23;14:1192057. doi: 10.3389/fimmu.2023.1192057. eCollection 2023.

ABSTRACT

Dendritic cells (DC) play a crucial role in generating and maintaining antiviral immunity. While DC are implicated in the antiviral defense by inducing T cell responses, they can also become infected by Cytomegalovirus (CMV). CMV is not only highly species-specific but also specialized in evading immune protection, and this specialization is in part due to characteristic genes encoded by a given virus. Here, we investigated whether rat CMV can infect XCR1+ DC and if infection of DC alters expression of cell surface markers and migration behavior. We demonstrate that wild-type RCMV and a mutant virus lacking the γ-chemokine ligand xcl1 (Δvxcl1 RCMV) infect splenic rat DC ex vivo and identify viral assembly compartments. Replication-competent RCMV reduced XCR1 and MHCII surface expression. Further, gene expression of infected DC was analyzed by bulk RNA-sequencing (RNA-Seq). RCMV infection reverted a state of DC activation that was induced by DC cultivation. On the functional level, we observed impaired chemotactic activity of infected XCR1+ DC compared to mock-treated cells. We therefore speculate that as a result of RCMV infection, DC exhibit diminished XCR1 expression and are thereby blocked from the lymphocyte crosstalk.

PMID:38077365 | PMC:PMC10702230 | DOI:10.3389/fimmu.2023.1192057

Front Immunol. 2023 Nov 23;14:1259381. doi: 10.3389/fimmu.2023.1259381. eCollection 2023.

ABSTRACT

Serum C-reactive protein (CRP) has been found elevated during COVID-19 infection, and associated with systematic inflammation as well as a poor clinical outcome. However, how did CRP participated in the COVID-19 pathogenesis remains poorly understood. Here, we report that serum C-reactive protein (CRP) levels are correlated with megakaryocyte marker genes and could regulate immune response through interaction with megakaryocytes. Molecular dynamics simulation through ColabFold showed a reliable interaction between monomeric form of CRP (mCRP) and the secreted protein acidic and rich in cysteine (SPARC). The interaction does not affect the physiological activities of SPARC while would be disturbed by pentamerization of CRP. Interplay between SPARC and mCRP results in a more intense immune response which may led to poor prognosis. This study highlights the complex interplay between inflammatory markers, megakaryocytes, and immune regulation in COVID-19 and sheds light on potential therapeutic targets.

PMID:38077346 | PMC:PMC10706481 | DOI:10.3389/fimmu.2023.1259381

Front Immunol. 2023 Nov 20;14:1275136. doi: 10.3389/fimmu.2023.1275136. eCollection 2023.

ABSTRACT

INTRODUCTION: People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers.

METHODS: Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study.

RESULTS: Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV.

DISCUSSION: These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

PMID:38077315 | PMC:PMC10703486 | DOI:10.3389/fimmu.2023.1275136

iScience. 2023 Nov 13;26(12):108456. doi: 10.1016/j.isci.2023.108456. eCollection 2023 Dec 15.

ABSTRACT

Male fertility decreases with aging, with spermatogenic decline being one of its causes. Altered testis environment is suggested as a cause of the phenotype; however, the associated mechanisms remain unclear. Herein, we investigated the age-related changes in testicular somatic cells on spermatogenic activity. The number and proliferation of spermatogonia significantly reduced with aging in mice. Interestingly, senescence-associated β-galactosidase-positive cells appeared in testicular endothelial cell (EC) populations, but not in germ cell populations, with aging. Transcriptome analysis of ECs indicated that senescence occurred in the ECs of aged mice. Furthermore, the support capacity of ECs for spermatogonial proliferation significantly decreased with aging; however, the senolytic-induced removal of senescent cells from aged ECs restored their supporting capacity to a comparable level as that of young ECs. Our results suggest that the accumulation of senescent ECs in the testis is a potential factor contributing to the age-related decline in spermatogenic activity.

PMID:38077127 | PMC:PMC10700819 | DOI:10.1016/j.isci.2023.108456

Front Bioinform. 2023 Nov 23;3:1304099. doi: 10.3389/fbinf.2023.1304099. eCollection 2023.

ABSTRACT

The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the "language of proteins" invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.

PMID:38076030 | PMC:PMC10701588 | DOI:10.3389/fbinf.2023.1304099

Front Microbiol. 2023 Nov 24;14:1295262. doi: 10.3389/fmicb.2023.1295262. eCollection 2023.

ABSTRACT

The soil-dwelling delta-proteobacterium Myxococcus xanthus is a model organism to study predation and competition. M. xanthus preys on a broad range of bacteria mediated by lytic enzymes, exopolysaccharides, Type-IV pilus-based motility, and specialized metabolites. Competition between M. xanthus and prey bacterial strains with various specialized metabolite profiles indicates a range of fitness, suggesting that specialized metabolites contribute to prey survival. To expand our understanding of how specialized metabolites affect predator-prey dynamics, we assessed interspecies interactions between M. xanthus and two strains of Bacillus cereus. While strain ATCC 14579 resisted predation, strain T was found to be highly sensitive to M. xanthus predation. The interaction between B. cereus ATCC 14579 and M. xanthus appears to be competitive, resulting in population loss for both predator and prey. Genome analysis revealed that ATCC 14579 belongs to a clade that possesses the biosynthetic gene cluster for production of thiocillins, whereas B. cereus strain T lacks those genes. Further, purified thiocillin protects B. cereus strains unable to produce this specialized metabolite, strengthening the finding that thiocillin protects against predation and contributes to the ecological fitness of B. cereus ATCC 14579. Lastly, strains that produce thiocillin appear to confer some level of protection to their own antibiotic by encoding an additional copy of the L11 ribosomal protein, a known target for thiopeptides. This work highlights the importance of specialized metabolites affecting predator-prey dynamics in soil microenvironments.

PMID:38075900 | PMC:PMC10704990 | DOI:10.3389/fmicb.2023.1295262

Front Genet. 2023 Nov 23;14:1286561. doi: 10.3389/fgene.2023.1286561. eCollection 2023.

ABSTRACT

Polygenic risk score (PRS) predictions often show bias toward the population of available genome-wide association studies (GWASs), which is typically of European ancestry. This study aimed to assess the performance differences of ancestry-specific PRS and test the implementation of multi-ancestry PRS to enhance the generalizability of low-density lipoprotein (LDL) cholesterol predictions in the East Asian (EAS) population. In this study, we computed ancestry-specific and multi-ancestry PRSs for LDL using data obtained from the Global Lipid Genetics Consortium, while accounting for population-specific linkage disequilibrium patterns using the PRS-CSx method in the United Kingdom Biobank dataset (UKB, n = 423,596) and Taiwan Biobank dataset (TWB, n = 68,978). Population-specific PRSs were able to predict LDL levels better within the target population, whereas multi-ancestry PRSs were more generalizable. In the TWB dataset, covariate-adjusted R 2 values were 9.3% for ancestry-specific PRS, 6.7% for multi-ancestry PRS, and 4.5% for European-specific PRS. Similar trends (8.6%, 7.8%, and 6.2%) were observed in the smaller EAS population of the UKB (n = 1,480). Consistent with R 2 values, PRS stratification in EAS regions (TWB) effectively captured a heterogenous variability in LDL blood cholesterol levels across PRS strata. The mean difference in LDL levels between the lowest and highest EAS-specific PRS (EAS_PRS) deciles was 0.82, compared to 0.59 for European-specific PRS (EUR_PRS) and 0.76 for multi-ancestry PRS. Notably, the mean LDL values in the top decile of multi-ancestry PRS were comparable to those of EAS_PRS (3.543 vs. 3.541, p = 0.86). Our analysis of the PRS prediction model for LDL cholesterol further supports the issue of PRS generalizability across populations. Our targeted analysis of the EAS population revealed that integrating non-European genotyping data with a powerful European-based GWAS can enhance the generalizability of LDL PRS.

PMID:38075701 | PMC:PMC10704094 | DOI:10.3389/fgene.2023.1286561

Biochem Biophys Rep. 2023 Nov 29;37:101589. doi: 10.1016/j.bbrep.2023.101589. eCollection 2024 Mar.

ABSTRACT

Single-cell multi-omics technologies have revolutionized cancer research by allowing us to examine individual cells at a molecular level. Unlike traditional bulk omics approaches, which analyze populations of cells together, single-cell multi-omics enables us to uncover the heterogeneity within tumors and understand the unique molecular characteristics of different cell populations. By doing so, we can identify rare subpopulations of cells that are influential in tumor growth, metastasis, and resistance to therapy. Moreover, single-cell multi-omics analysis provides valuable insights into the immune response triggered by various therapeutic interventions, such as immune checkpoint blockade, chemotherapy, and cell therapy. It also helps us better understand the intricate tumor microenvironment and its impact on patient prognosis and response to treatment. This comprehensive review focuses on the recent advancements in single-cell multi-omics methodologies, with an emphasis on single-cell multi-omics technologies. It highlights the important role of these techniques in uncovering the complexity of tumorigenesis and its multiple applications in cancer research, as well as their equally great contributions in other areas such as immunology. Through single-cell multi-omics, we gain a deeper understanding of cancer biology and pave the way for more precise and effective therapeutic strategies. Apart from those above, this paper also aims to introduce the advancements in live cell imaging technology, the latest developments in protein detection techniques, and explore their seamless integration with single-cell multi-omics technology.

PMID:38074997 | PMC:PMC10698529 | DOI:10.1016/j.bbrep.2023.101589