Microbiol Spectr. 2024 Dec 10:e0207324. doi: 10.1128/spectrum.02073-24. Online ahead of print.

ABSTRACT

Respiratory syncytial virus (RSV) causes lower respiratory tract infections in infants and young children, leading to a pathogenesis-associated imbalance in CD4+ T-cell subsets and monocyte subsets. To investigate whether RSV affects the imbalance of CD4+ T-cells through monocytes, we examined the effects of the RSV-infected monocyte subset on CD4+ T-cell subsets, namely, Th1, Th2, Th17, and regulatory T (Treg) subsets, on proliferation in vitro and identified key monocyte-derived cytokines. We found that RSV efficiently infects CD16+ monocytes, but not CD16- monocytes, via cocultures of CD4+ T-cells with RSV-infected CD16+ monocytes, inhibits Treg cell proliferation and increases Th2 cell frequency, suggesting that RSV causes an imbalance in the CD4+ T-cell subset by primarily infecting CD16+ monocytes. Our data also revealed that IL-1β and IL-10 are key cytokines responsible for the activities of RSV-infected CD16+ monocytes. In a mouse model, we found that high-efficiency RSV infection of mouse Ly6C- monocytes, corresponding to CD16+ monocytes in humans, and adoptive transfer of Ly6C- monocytes during RSV infection decreased the Treg frequency in the lungs and aggravated pneumonia. Our data indicate that RSV can increase its pathogenesis through infection of nonclassic monocytes, leading to a CD4+ T-cell imbalance.IMPORTANCEThis study identified a pathogenesis pathway related to the RSV-nonclassic monocyte-IL-1β/IL-10-CD4+ T-cell subset balance, which links the heterogeneity of monocytes to RSV pathogenesis and elucidates a new mechanism by which RSV infection disrupts the balance of CD4+ T cells during RSV infection. These new findings provide potential therapeutic targets for RSV infection.

PMID:39656009 | DOI:10.1128/spectrum.02073-24

Eur J Pain. 2025 Jan;29(1):e4767. doi: 10.1002/ejp.4767.

ABSTRACT

BACKGROUND: Pain is the leading cause of disability and reduced quality of life worldwide. Despite the increasing burden for patients and healthcare systems, pain research remains underfunded and under focused. Having stakeholders identify and prioritize areas that need urgent attention in the field will help focus funding topics, reduce 'research waste', improve the effectiveness of pain research and therapy and promote the uptake of research evidence. In this study, the European Pain Federation (EFIC) developed a Pain Research Strategy for Europe.

METHODS: The study used multiple methods, including literature searches, multidisciplinary expert debate, a survey and a final consensus meeting. The cross-sectional survey was conducted among 628 European pain researchers, clinicians, educators and industry professionals to obtain the rating and hierarchy of pain research priorities. The final consensus meeting involved a multidisciplinary expert panel including people with lived experience from 23 countries. The survey results guided discussions where top priorities were agreed.

RESULTS: Content analysis identified nine survey themes, of which five emerged as top priorities: (i) understand the pathophysiology of pain; (ii) understand and address comorbidities; (iii) critically assess current therapies; (iv) develop new treatments; and (v) explore the biopsychosocial impacts of pain. Physical, psychological and social approaches were prioritized at the same level as pharmacological treatments. The top priorities were endorsed by a multidisciplinary expert panel. The panel emphasized the importance of also clearly communicating the concepts of prediction, prevention self-management and personalized pain management in the final strategy.

CONCLUSIONS: The content of the final top research priorities' list reflects a holistic approach to pain management. The equal importance given to physical, psychological and social aspects alongside pharmacological treatments highlights the importance of a comprehensive biopsychosocial-orientated research strategy. The expert panel's endorsement of five top priorities, coupled with an emphasis on communicating the concepts of prediction, prevention, self-management and personalized pain management, provides a clear direction for future basic, translational and clinical research.

SIGNIFICANCE: EFIC has developed a Pain Research Strategy for Europe that identifies pain research areas deserving the most focus and financial support. Implementation and wide dissemination of this Strategy is vital to increase the conduct of urgent pain projects, pain research funding and the implementation of research findings into practice, to ultimately decrease the personal, societal and financial burden of pain.

PMID:39655849 | DOI:10.1002/ejp.4767

J Am Heart Assoc. 2024 Dec 10:e036906. doi: 10.1161/JAHA.124.036906. Online ahead of print.

ABSTRACT

BACKGROUND: Contemporary risk assessment in patients with coronary atherosclerotic disease (CAD) often relies on invasive angiography. However, we aimed to explore the potential of metabolomic biomarkers in reflecting residual risk in patients with CAD after moderate lipid-lowering therapy.

METHODS AND RESULTS: We analyzed serum metabolomic profile among 2560 patients with newly diagnosed CAD undergoing moderate lipid-lowering therapy, through nuclear magnetic resonance spectroscopy and quantified 175 metabolites, predominantly lipoproteins and their components. CAD severity was evaluated using Gensini score for plaque burden and circulating cardiac troponin T levels for plaque instability. The association of metabolites with CAD severity was examined using multivariate linear regression, and the underlying potential causality was explored using a 2-sample Mendelian randomization approach. Two composite metabolomic indices were constructed to reflect CAD severity using least absolute shrinkage and selection operator linear regression, and their associations with risk of major adverse cardiac events during a median follow-up of 3.8 years were evaluated using Cox models. Our investigation revealed that triglycerides and apolipoprotein B in low-density lipoprotein particles displayed stronger associations with CAD severity compared with the clinically used low-density lipoprotein cholesterol marker. In large high-density lipoprotein, components like cholesterol, cholesterol esters, triglyceride, apolipoprotein A1/A2 showed inverse associations with CAD severity. Certain metabolites, including apolipoprotein B and dihydrothymine, showed a putative causal link with Gensini score. Notably, per standard deviation increase in Gensini score-based metabolomic index was associated with 14.8% higher major adverse cardiac event risk (hazard ratio, 1.148 [95% CI, 1.018-1.295]) independent of demographic factors, medication use, and disease status.

CONCLUSIONS: Our findings highlight the potential of nuclear magnetic resonance-based metabolomics in identifying novel biomarkers of plaque burden and instability. Metabolites related to plaque burden may facilitate noninvasive assessment of CAD prognosis.

PMID:39655754 | DOI:10.1161/JAHA.124.036906

J Immunotoxicol. 2024 Oct;21(sup1):S5-S12. doi: 10.1080/1547691X.2024.2345154. Epub 2024 Dec 10.

ABSTRACT

This work focuses on the need for modeling and predicting adverse outcomes in immunotoxicology to improve nonclinical assessments of the safety of immunomodulatory therapies. The integrated approach includes, first, the adverse outcome pathway concept established in the toxicology field, and, second, the systems medicine disease map approach for describing molecular mechanisms involved in a particular pathology. The proposed systems immunotoxicology workflow is illustrated with chimeric antigen receptor (CAR) T cell treatment as a use case. To this end, the linear adverse outcome pathway (AOP) is expanded into a molecular interaction model in standard systems biology formats. Then it is shown how knowledge related to immunotoxic events can be integrated, encoded, managed, and explored to benefit the research community. The map is accessible online at https://imsavar.elixir-luxembourg.org via the MINERVA Platform for browsing, commenting, and data visualization. Our work transforms a graphical illustration of an AOP into a digitally structured and standardized form, featuring precise and controlled vocabulary and supporting reproducible computational analyses. Because of annotations to source literature and databases, the map can be further expanded to match the evolving knowledge and research questions.

PMID:39655493 | DOI:10.1080/1547691X.2024.2345154

Metab Eng Commun. 2024 Nov 19;19:e00251. doi: 10.1016/j.mec.2024.e00251. eCollection 2024 Dec.

ABSTRACT

In pursuit of reliable and efficient industrial microbes, this study integrates cutting-edge systems biology tools with Halomonas bluephagenesis TD01, a robust halophilic bacterium. We generated the complete and annotated circular genome sequence for this model organism, constructed and meticulously curated a genome-scale metabolic network, achieving striking 86.32% agreement with Biolog Phenotype Microarray data and visualize the network via an interactive Electron/Thrift server architecture. We then analyzed the genome-scale network using vertex sampling analysis (VSA) and found that productions of biomass, polyhydroxyalkanoates (PHA), citrate, acetate, and pyruvate are mutually competing. Recognizing the dynamic nature of H. bluephagenesis TD01, we further developed and implemented the hyper-cube-shrink-analysis (HCSA) framework to predict effects of nutrient availabilities and metabolic reactions in the model on biomass and PHA accumulation. We then, based on the analysis results, proposed and validate multi-step feeding strategies tailored to different fermentation stages. This integrated approach yielded remarkable results, with fermentation culminating in a cell dry weight of 100.4 g/L and 70% PHA content, surpassing previous benchmarks. Our findings exemplify the powerful potential of system-level tools in the design and optimization of industrial microorganisms, paving the way for more efficient and sustainable bio-based processes.

PMID:39655187 | PMC:PMC11626823 | DOI:10.1016/j.mec.2024.e00251

Front Plant Sci. 2024 Nov 25;15:1433358. doi: 10.3389/fpls.2024.1433358. eCollection 2024.

ABSTRACT

Seed-based restoration methods are increasingly recognized as a relevant tool contributing to halt and reverse the loss of seagrass meadows while providing genetic and evolutionary benefit for the conservation of these habitats. Ad-hoc protocols aimed at maximizing the survival of plantlets obtained from seeds in cultivation systems are therefore required. Previous trials of seedling culture of Posidonia oceanica, the dominant seagrass of the Mediterranean Sea, recorded up to 40% loss due to mould development. In this study we aim to (i) identify the putative causal agents of seed decay and (ii) test the efficacy of copper sulphate (0.2 and 2 ppm) and of tannin-based products derived from chestnut, tara and quebracho in reducing seed and seedling decay, while assessing possible phytotoxic effects on plant development. Halophytophthora lusitanica, H. thermoambigua and a putative new Halophytophtora species were identified as possible causal agents of seed loss. The antimicrobial agents (copper and tannins) reduced seed contamination by 20%, although copper sulphate at 2 ppm strongly inhibited the root growth. Among tannins, chestnut and tara reduced seeds germination by up to 75% and decreased shoot and root development, while quebracho showed a less severe phytotoxic effect. The use of copper sulphate at 0.2 ppm is therefore recommended to prevent P. oceanica seedling loss in culture facilities since it reduces seed contamination with no phytotoxic effects. Our results contribute to improving the seedling culture of one the key species of the Mediterranean Sea, increasing propagule availability for restoration purposes.

PMID:39654965 | PMC:PMC11625593 | DOI:10.3389/fpls.2024.1433358

Medicine (Baltimore). 2024 Dec 6;103(49):e40820. doi: 10.1097/MD.0000000000040820.

ABSTRACT

Endometrial cancer (EC) is the most common gynecologic malignancy with increasing incidence and mortality. The tumor immune microenvironment significantly impacts cancer prognosis. Weighted Gene Co-Expression Network Analysis (WGCNA) is a systems biology approach that analyzes gene expression data to uncover gene co-expression networks and functional modules. This study aimed to use WGCNA to develop a prognostic prediction model for EC based on immune cell infiltration, and to identify new potential therapeutic targets. WGCNA was performed using the Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma dataset to identify hub modules associated with T-lymphocyte cell infiltration. Prognostic models were developed using LASSO regression based on genes in these hub modules. The Search Tool for the Retrieval of Interacting Genes/Proteins was used for protein-protein interaction network analysis of the hub module. Gene Set Variation Analysis identified differential gene enrichment analysis between high- and low-risk groups. The relationship between the model and microsatellite instability, tumor mutational burden, and immune cell infiltration was analyzed using The Cancer Genome Atlas data. The model's correlation with chemotherapy and immunotherapy resistance was examined using the Genomics of Drug Sensitivity in Cancer and Cancer Immunome Atlas databases. Immunohistochemical staining of EC tissue microarrays was performed to analyze the relationship between the expression of key genes and immune infiltration. The green-yellow module was identified as a hub module, with 4 genes (ARPC1B, BATF, CCL2, and COTL1) linked to CD8+ T cell infiltration. The prognostic model constructed from these genes showed satisfactory predictive efficacy. Differentially expressed genes in high- and low-risk groups were enriched in tumor immunity-related pathways. The model correlated with EC-related phenotypes, indicating its potential to predict immunotherapeutic response. Basic leucine zipper activating transcription factor-like transcription factor(BATF) expression in EC tissues positively correlated with CD8+ T cell infiltration, suggesting BATF's crucial role in EC development and antitumor immunity. The prognostic model comprising ARPC1B, BATF, CCL2, and COTL1 can effectively identify high-risk EC patients and predict their response to immunotherapy, demonstrating significant clinical potential. These genes are implicated in EC development and immune infiltration, with BATF emerging as a potential therapeutic target for EC.

PMID:39654198 | DOI:10.1097/MD.0000000000040820

J Chem Inf Model. 2024 Dec 9. doi: 10.1021/acs.jcim.4c01429. Online ahead of print.

ABSTRACT

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure-based methods to identify hits to a target with no known ligands. We utilized two methods, pharmacophore search and molecular docking, to identify our initial hit list and compounds for the hit expansion phase. Unlike many other participants, we limited ourselves to using docking scores in identifying and ranking hits. Our resulting best hit series tied for first place when evaluated by a panel of expert judges. Here, we report our top-performing open-source workflow and results.

PMID:39654129 | DOI:10.1021/acs.jcim.4c01429

Mol Syst Biol. 2024 Dec 9. doi: 10.1038/s44320-024-00077-y. Online ahead of print.

ABSTRACT

Advancements in genomic and proteomic technologies have powered the creation of large gene and protein networks ("interactomes") for understanding biological systems. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 45 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP, Reactome, and SIGNOR demonstrate stronger performance in interaction prediction. Our study provides a benchmark for interactomes across diverse biological applications and clarifies factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

PMID:39653848 | DOI:10.1038/s44320-024-00077-y

Nat Chem Biol. 2024 Dec 9. doi: 10.1038/s41589-024-01810-2. Online ahead of print.

NO ABSTRACT

PMID:39653787 | DOI:10.1038/s41589-024-01810-2

Nat Commun. 2024 Dec 9;15(1):10649. doi: 10.1038/s41467-024-55167-0.

NO ABSTRACT

PMID:39653705 | DOI:10.1038/s41467-024-55167-0

Gut. 2024 Dec 9:gutjnl-2024-333530. doi: 10.1136/gutjnl-2024-333530. Online ahead of print.

ABSTRACT

BACKGROUND: Natural killer/T-cell lymphoma (NKTCL) is a highly aggressive malignancy with a dismal prognosis, and gaps remain in understanding the determinants influencing disease outcomes.

OBJECTIVE: To characterise the gut microbiota feature and identify potential probiotics that could ameliorate the development of NKTCL.

DESIGN: This cross-sectional study employed shotgun metagenomic sequencing to profile the gut microbiota in two Chinese NKTCL cohorts, with validation conducted in an independent Korean cohort. Univariable and multivariable Cox proportional hazards analyses were applied to assess associations between identified marker species and patient outcomes. Tumour-suppressing effects were investigated using comprehensive in vivo and in vitro models. In addition, metabolomics, RNA sequencing, chromatin immunoprecipitation sequencing, Western blot analysis, immunohistochemistry and lentiviral-mediated gene knockdown system were used to elucidate the underlying mechanisms.

RESULTS: We first unveiled significant gut microbiota dysbiosis in NKTCL patients, prominently marked by a notable reduction in Faecalibacterium prausnitzii which correlated strongly with shorter survival among patients. Subsequently, we substantiated the antitumour properties of F. prausnitzii in NKTCL mouse models. Furthermore, F. prausnitzii culture supernatant demonstrated significant efficacy in inhibiting NKTCL cell growth. Metabolomics analysis revealed butyrate as a critical metabolite underlying these tumour-suppressing effects, validated in three human NKTCL cell lines and multiple tumour-bearing mouse models. Mechanistically, butyrate suppressed the activation of Janus kinase-signal transducer and activator of transcription pathway through enhancing histone acetylation, promoting the expression of suppressor of cytokine signalling 1.

CONCLUSION: These findings uncover a distinctive gut microbiota profile in NKTCL and provide a novel perspective on leveraging the therapeutic potential of F. prausnitzii to ameliorate this malignancy.

PMID:39653411 | DOI:10.1136/gutjnl-2024-333530

Am J Physiol Endocrinol Metab. 2024 Dec 9. doi: 10.1152/ajpendo.00256.2024. Online ahead of print.

ABSTRACT

Inorganic nitrate (NO3-) has been proposed to be of therapeutic use as a dietary supplement in obesity and related conditions including the Metabolic Syndrome (MetS), type-II diabetes and metabolic dysfunction associated steatotic liver disease (MASLD). Administration of NO3- to endothelial nitric oxide synthase-deficient mice reversed aspects of MetS, however the impact of NO3- supplementation in diet-induced obesity is not well understood. Here we investigated the whole-body metabolic phenotype and cardiac and hepatic metabolism in mice fed a high-fat high-sucrose (HFHS) diet for up to 12-months of age, supplemented with 1 mM NaNO3 (or NaCl) in their drinking water. HFHS-feeding was associated with a progressive obesogenic and diabetogenic phenotype, which was not ameliorated by NO3-. Furthermore, HFHS-fed mice supplemented with NO3- showed elevated levels of cardiac fibrosis, and accelerated progression of MASLD including development of hepatocellular carcinoma in comparison with NaCl-supplemented mice. NO3- did not enhance mitochondrial b-oxidation capacity in any tissue assayed and did not suppress hepatic lipid accumulation, suggesting it does not prevent lipotoxicity. We conclude that NO3- is ineffective in preventing the metabolic consequences of an obesogenic diet and may instead be detrimental to metabolic health against the background of HFHS-feeding. This is the first report of an unfavorable effect of long-term nitrate supplementation in the context of the metabolic challenges of overfeeding, warranting urgent further investigation into the mechanism of this interaction.

PMID:39653040 | DOI:10.1152/ajpendo.00256.2024

Elife. 2024 Dec 9;13:RP94296. doi: 10.7554/eLife.94296.

ABSTRACT

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch and pitch-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

PMID:39652382 | DOI:10.7554/eLife.94296

JAMA Netw Open. 2024 Dec 2;7(12):e2449795. doi: 10.1001/jamanetworkopen.2024.49795.

NO ABSTRACT

PMID:39652350 | DOI:10.1001/jamanetworkopen.2024.49795

J Assist Reprod Genet. 2024 Dec 9. doi: 10.1007/s10815-024-03338-9. Online ahead of print.

ABSTRACT

PURPOSE: Y chromosome microdeletions (YCMD) are a common cause of azoospermia and oligozoospermia in men. Herein, we developed a machine learning-based web tool to predict sperm retrieval rates and success rates of assisted reproduction (ART) in men with YCMD.

METHODS: Data on ART outcomes of men with YCMD who underwent ART were extracted from published studies by performing a systematic review. This data was used to develop a web-based predictive algorithm using machine learning.

RESULTS: FertilitY Predictor classifies the type of YCMD into AZFa, AZFb, AZFc, their combinations, and gr/gr deletions based on the genetic markers as input. Further, it predicts the probability of sperm retrieval, fertilization rate, clinical pregnancy rate, and live birth rate based on the type of YCMD. Validation studies demonstrated its high accuracy and predictability for sperm retrieval, clinical pregnancy rates, and live birth rates. The tool predicts that men with deletions have a chance of sperm retrieval that varies with type of deletions, the clinical pregnancy rates and live birth rates are lower in men with AZF deletions. A trial version of the tool is available at http://fertilitypredictor.sbdaresearch.in .

CONCLUSIONS: FertilitY Predictor allows users to classify AZFa, AZFb, AZFc, and gr/gr deletions and also predict the outcomes of ART based on the type of deletions.

TRIAL REGISTRATION: PROSPERO (CRD42022311738).

PMID:39652237 | DOI:10.1007/s10815-024-03338-9

Microbiol Spectr. 2024 Dec 9:e0366023. doi: 10.1128/spectrum.03660-23. Online ahead of print.

ABSTRACT

Diplosphaera chodatii, a unicellular terrestrial microalga found either free-living or in association with lichenized fungi, protects itself from desiccation by synthesizing and accumulating low-molecular-weight carbohydrates such as sorbitol. The metabolism of this algal species and the interplay of sorbitol biosynthesis with its growth, light absorption, and carbon dioxide fixation are poorly understood. Here, we used a recently available genome assembly for D. chodatii to develop a metabolic flux model and analyze the alga's metabolic capabilities, particularly, for sorbitol biosynthesis. The model contains 151 genes, 155 metabolites, and 194 unique metabolic reactions participating in 12 core metabolic pathways and five compartments. Both photoautotrophic and mixotrophic growths of D. chodatii were supported by the metabolic model. In the presence of glucose, mixotrophy led to higher biomass and sorbitol yields. Additionally, the model predicted increased starch biosynthesis at high light intensities during photoautotrophic growth, an indication that the "overflow hypothesis-stress-driven metabolic flux redistribution" could be applied to D. chodatii. Furthermore, the newly developed metabolic model of D. chodatii, iDco_core, captures both linear and cyclic electron flow schemes characterized in photosynthetic microorganisms and suggests a possible adaptation to fluctuating water availability during periods of desiccation. This work provides important new insights into the predicted metabolic capabilities of D. chodatii, including a potential biotechnological opportunity for industrial sorbitol biosynthesis.IMPORTANCELichenized green microalgae are vital components for the survival and growth of lichens in extreme environmental conditions. However, little is known about the metabolism and growth characteristics of these algae as individual microbes. This study aims to provide insights into some of the metabolic capabilities of Diplosphaera chodatii, a lichenized green microalgae, using a recently assembled and annotated genome of the alga. For that, a metabolic flux model was developed simulating the metabolism of this algal species and allowing for studying the algal growth, light absorption, and carbon dioxide fixation during both photoautotrophic and mixotrophic growth, in silico. An important capability of the new metabolic model of D. chodatii is capturing both linear and cyclic electron flow mechanisms characterized in several other microalgae. Moreover, the model predicts limits of the metabolic interplay between sorbitol biosynthesis and algal growth, which has potential applications in assisting the design of bio-based sorbitol production processes.

PMID:39651901 | DOI:10.1128/spectrum.03660-23

mSystems. 2024 Dec 9:e0106824. doi: 10.1128/msystems.01068-24. Online ahead of print.

ABSTRACT

A key step toward rational microbiome engineering is in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously capture compositions of host-associated microbiomes and host phenotypes. To that end, we present a generative model based on the mechanistic consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (inferred latent variables), while species' resource preferences remain conserved. Simultaneously, the latent variables are used to model phenotypic states of hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address three salient questions in host-associated microbial ecologies: (i) which host phenotypes maximally constrain the composition of the host-associated microbiomes? (ii) how context-specific are phenotype/microbiome associations, and (iii) what are plausible microbiome compositions that correspond to desired host phenotypes? Our approach aids the analysis and design of microbial communities associated with host phenotypes of interest.

IMPORTANCE: Generative models are extremely popular in modern biology. They have been used to model the variation of protein sequences, entire genomes, and RNA sequencing profiles. Importantly, generative models have been used to extrapolate and interpolate to unobserved regimes of data to design biological systems with desired properties. For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. Host-associated microbiomes play important roles in animal health and disease, as well as the productivity and environmental footprint of livestock species. However, there are no generative models of host-associated microbiomes. One chief reason is that off-the-shelf machine-learning models are data hungry, and microbiome studies usually deal with large variability and small sample sizes. Moreover, microbiome compositions are heavily context dependent, with characteristics of the host and the abiotic environment leading to distinct patterns in host-microbiome associations. Consequently, off-the-shelf generative modeling has not been successfully applied to microbiomes.To address these challenges, we develop a generative model for host-associated microbiomes derived from the consumer/resource (C/R) framework. This derivation allows us to fit the model to readily available cross-sectional microbiome profile data. Using data from three animal hosts, we show that this mechanistic generative model has several salient features: the model identifies a latent space that represents variables that determine the growth and, therefore, relative abundances of microbial species. Probabilistic modeling of variation in this latent space allows us to generate realistic in silico microbial communities. The model can assign probabilities to microbiomes, thereby allowing us to discriminate between dissimilar ecosystems. Importantly, the model predictively captures host-associated microbiomes and the corresponding hosts' phenotypes, enabling the design of microbial communities associated with user-specified host characteristics.

PMID:39651880 | DOI:10.1128/msystems.01068-24

Small Methods. 2024 Dec 9:e2401376. doi: 10.1002/smtd.202401376. Online ahead of print.

ABSTRACT

In recent years, notable advances in nanotechnology-based drug delivery have emerged. A particularly promising platform in this field is DNA origami-based nanoparticles, which offer highly programmable surfaces, providing precise control over the nanoscale spacing and stoichiometry of various cargo. These versatile particles are finding diverse applications ranging from basic molecular biology to diagnostics and therapeutics. This growing interest creates the need for effective methods to quantify cargo on DNA origami nanoparticles. The study consolidates several previously validated methods focusing on gel-based and fluorescence-based techniques, including multiplexed quantification of protein, peptide, and nucleic acid cargo on these nanoparticles. In this work, how gel band intensity and nanodrop fluorescence readings can be used to quantify protein, peptide, and RNA cargo on a DNA origami nanoparticle is demonstrated. This work may serve as a valuable resource for groups of researchers keen on utilizing DNA origami-based nanoparticles in therapeutic applications.

PMID:39651835 | DOI:10.1002/smtd.202401376

Development. 2024 Dec 9:dev.202931. doi: 10.1242/dev.202931. Online ahead of print.

ABSTRACT

Anteroposterior (AP) elongation of the vertebrate body plan is driven by convergence and extension (C&E) gastrulation movements in both the mesoderm and neuroectoderm, but how or whether molecular regulation of C&E differs between tissues remains an open question. Using a zebrafish explant model of AP axis extension, we show that C&E of the neuroectoderm and mesoderm can be uncoupled ex vivo, and that morphogenesis of individual tissues results from distinct morphogen signaling dynamics. Using precise temporal manipulation of BMP and Nodal signaling, we identify a critical developmental window during which high or low BMP/Nodal ratios induce neuroectoderm- or mesoderm-driven C&E, respectively. Increased BMP activity similarly enhances C&E specifically in the ectoderm of intact zebrafish gastrulae, highlighting the in vivo relevance of our findings. Together, these results demonstrate that temporal dynamics of BMP and Nodal morphogen signaling activate distinct morphogenetic programs governing C&E gastrulation movements within individual tissues.

PMID:39651654 | DOI:10.1242/dev.202931