Expert Opin Drug Discov. 2024 May 15:1-10. doi: 10.1080/17460441.2024.2355330. Online ahead of print.

ABSTRACT

INTRODUCTION: The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED: This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION: The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

PMID:38747562 | DOI:10.1080/17460441.2024.2355330

J Physiol. 2024 May 15. doi: 10.1113/JP285120. Online ahead of print.

ABSTRACT

All new drugs must go through preclinical screening tests to determine their proarrhythmic potential. While these assays effectively filter out dangerous drugs, they are too conservative, often misclassifying safe compounds as proarrhythmic. In this study, we attempt to address this shortcoming with a novel, medium-throughput drug-screening approach: we use an automated patch-clamp system to acquire optimized voltage clamp (VC) and action potential (AP) data from human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at several drug concentrations (baseline, 3×, 10× and 20× the effective free plasma concentrations). With our novel method, we show correlations between INa block and upstroke slowing after treatment with flecainide or quinine. Additionally, after quinine treatment, we identify significant reductions in current during voltage steps designed to isolate If and IKs. However, we do not detect any IKr block by either drug, and upon further investigation, do not see any IKr present in the iPSC-CMs when prepared for automated patch experiments (i.e. in suspension) - this is in contrast to similar experiments we have conducted with these cells using the manual patch setup. In this study, we: (1) present a proof-of-concept demonstration of a single-cell medium-throughput drug study, and (2) characterize the non-canonical electrophysiology of iPSC-CMs when prepared for experiments in a medium-throughput setting. KEY POINTS: Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer potential as an in vitro model to study the proarrhythmic potential of drugs, but insights from these cells are often limited by the low throughput of manual patch-clamp. In this study, we use a medium-throughput automated patch-clamp system to acquire action potential (AP) and complex voltage clamp (VC) data from single iPSC-CMs at multiple drug concentrations. A correlation between AP upstroke and INa transients was identified and drug-induced changes in ionic currents found. We also characterize the substantially altered physiology of iPSC-CMs when patched in an automated system, suggesting the need to investigate differences between manual and automated patch experiments.

PMID:38747042 | DOI:10.1113/JP285120

iScience. 2024 Apr 22;27(5):109789. doi: 10.1016/j.isci.2024.109789. eCollection 2024 May 17.

ABSTRACT

Mitochondrial function relies on the coordinated transcription of mitochondrial and nuclear genomes to assemble respiratory chain complexes. Across species, the SIN3 coregulator influences mitochondrial functions, but how its loss impacts mitochondrial homeostasis and metabolism in the context of a whole organism is unknown. Exploring this link is important because SIN3 haploinsufficiency causes intellectual disability/autism syndromes and SIN3 plays a role in tumor biology. Here we show that loss of C. elegans SIN-3 results in transcriptional deregulation of mitochondrial- and nuclear-encoded mitochondrial genes, potentially leading to mito-nuclear imbalance. Consistent with impaired mitochondrial function, sin-3 mutants show extensive mitochondrial fragmentation by transmission electron microscopy (TEM) and in vivo imaging, and altered oxygen consumption. Metabolomic analysis of sin-3 mutant animals revealed a mitochondria stress signature and deregulation of methionine flux, resulting in decreased S-adenosyl methionine (SAM) and increased polyamine levels. Our results identify SIN3 as a key regulator of mitochondrial dynamics and metabolic flux, with important implications for human pathologies.

PMID:38746662 | PMC:PMC11091686 | DOI:10.1016/j.isci.2024.109789

Ecol Evol. 2024 May 13;14(5):e11285. doi: 10.1002/ece3.11285. eCollection 2024 May.

ABSTRACT

Estimating demographic parameters for wide-ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south-central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7-month sampling campaign in 2020-2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non-invasive genetic samples (NGS). The model comprised (i) a single-season, multiple data-source, multi-event occupancy model and (ii) a spatially explicit capture-recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation-based assessment, to estimate the best conditions for optimizing sub-sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south-central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence-absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide-ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.

PMID:38746543 | PMC:PMC11091487 | DOI:10.1002/ece3.11285

bioRxiv [Preprint]. 2024 Apr 28:2024.04.25.591018. doi: 10.1101/2024.04.25.591018.

ABSTRACT

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

PMID:38746391 | PMC:PMC11091946 | DOI:10.1101/2024.04.25.591018

Mol Ther Nucleic Acids. 2024 Apr 23;35(2):102193. doi: 10.1016/j.omtn.2024.102193. eCollection 2024 Jun 11.

ABSTRACT

Use of tumor-suppressive microRNAs (miRNAs) as anti-cancer agents is hindered by the lack of effective delivery vehicles, entrapment of the miRNA within endocytic compartments, and rapid degradation of miRNA by nucleases. To address these issues, we developed a miRNA delivery strategy that includes (1) a targeting ligand, (2) an endosomal escape agent, nigericin and (3) a chemically modified miRNA. The delivery ligand, DUPA (2-[3-(1,3-dicarboxy propyl) ureido] pentanedioic acid), was selected based on its specificity for prostate-specific membrane antigen (PSMA), a receptor routinely upregulated in prostate cancer-one of the leading causes of cancer death among men. DUPA was conjugated to the tumor suppressive miRNA, miR-34a (DUPA-miR-34a) based on the ability of miR-34a to inhibit prostate cancer cell proliferation. To mediate endosomal escape, nigericin was incorporated into the complex, resulting in DUPA-nigericin-miR-34a. Both DUPA-miR-34a and DUPA-nigericin-miR-34a specifically bound to, and were taken up by, PSMA-expressing cells in vitro and in vivo. And while both DUPA-miR-34a and DUPA-nigericin-miR-34a downregulated miR-34a target genes, only DUPA-nigericin-miR-34a decreased cell proliferation in vitro and delayed tumor growth in vivo. Tumor growth was further reduced using a fully modified version of miR-34a that has significantly increased stability.

PMID:38745855 | PMC:PMC11091501 | DOI:10.1016/j.omtn.2024.102193

Nat Immunol. 2024 May 14. doi: 10.1038/s41590-024-01828-7. Online ahead of print.

ABSTRACT

The etiology and effect of age-related immune dysfunction in cancer is not completely understood. Here we show that limited priming of CD8+ T cells in the aged tumor microenvironment (TME) outweighs cell-intrinsic defects in limiting tumor control. Increased tumor growth in aging is associated with reduced CD8+ T cell infiltration and function. Transfer of T cells from young mice does not restore tumor control in aged mice owing to rapid induction of T cell dysfunction. Cell-extrinsic signals in the aged TME drive a tumor-infiltrating age-associated dysfunctional (TTAD) cell state that is functionally, transcriptionally and epigenetically distinct from canonical T cell exhaustion. Altered natural killer cell-dendritic cell-CD8+ T cell cross-talk in aged tumors impairs T cell priming by conventional type 1 dendritic cells and promotes TTAD cell formation. Aged mice are thereby unable to benefit from therapeutic tumor vaccination. Critically, myeloid-targeted therapy to reinvigorate conventional type 1 dendritic cells can improve tumor control and restore CD8+ T cell immunity in aging.

PMID:38745085 | DOI:10.1038/s41590-024-01828-7

Sci Rep. 2024 May 14;14(1):11020. doi: 10.1038/s41598-024-61597-z.

ABSTRACT

The absence of stimulator of interferon genes (STING) in 129.B6.Fcgr2b-deficient mice rescue lupus phenotypes. The administration of a STING inhibitor (ISD017) into the young 129.B6.Fcgr2b-deficient mice prevents lupus nephritis development. This study mainly aimed to evaluate the effects of STING inhibition (ISD107) on established SLE in mice to prove that ISD017 could be a good therapeutic drug to reverse the already set-up autoimmunity and kidney impairment. Twenty-four-week-old Fcgr2b-deficient mice were treated with cyclophosphamide (25 mg/kg, intraperitoneal, once per week), ISD017 (10 mg/kg, intraperitoneal, three times per week), or control vehicle for 8 weeks, and were analyzed for phenotypes. Both ISD017 and cyclophosphamide treatment increased long-term survival and reduced the severity of glomerulonephritis in Fcgr2b-deficient mice. While cyclophosphamide reduced activated B cells (B220+GL-7+), ISD017 decreased activated T cells (CD4+CD69+) and neutrophils (Ly6c+Ly6g+) in Fcgr2b-deficient mice. In addition, ISD017 reduced IL-1β and interferon-inducible genes. In summary, ISD017 treatment in symptomatic 129.B6.Fcgr2b-deficient mice reduced the severity of glomerulonephritis and increased long-term survival. ISD017 worked comparably to cyclophosphamide for treating lupus nephritis in 129.B6.Fcgr2b-deficient mice. ISD017 reduced activated T cells and neutrophils, while cyclophosphamide targeted activated B cells. These results suggested that STING inhibitors can potentially be a new therapeutic drug for treating lupus.

PMID:38745067 | DOI:10.1038/s41598-024-61597-z

Nat Methods. 2024 May 14. doi: 10.1038/s41592-024-02272-z. Online ahead of print.

ABSTRACT

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

PMID:38744917 | DOI:10.1038/s41592-024-02272-z

Nat Commun. 2024 May 14;15(1):4051. doi: 10.1038/s41467-024-48267-4.

ABSTRACT

Intestinal homeostasis is maintained by the response of gut-associated lymphoid tissue to bacteria transported across the follicle associated epithelium into the subepithelial dome. The initial response to antigens and how bacteria are handled is incompletely understood. By iterative application of spatial transcriptomics and multiplexed single-cell technologies, we identify that the double negative 2 subset of B cells, previously associated with autoimmune diseases, is present in the subepithelial dome in health. We show that in this location double negative 2 B cells interact with dendritic cells co-expressing the lupus autoantigens DNASE1L3 and C1q and microbicides. We observe that in humans, but not in mice, dendritic cells expressing DNASE1L3 are associated with sampled bacteria but not DNA derived from apoptotic cells. We propose that fundamental features of autoimmune diseases are microbiota-associated, interacting components of normal intestinal immunity.

PMID:38744839 | DOI:10.1038/s41467-024-48267-4

Cell Rep Methods. 2024 May 7:100773. doi: 10.1016/j.crmeth.2024.100773. Online ahead of print.

ABSTRACT

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

PMID:38744288 | DOI:10.1016/j.crmeth.2024.100773

Cell Rep Methods. 2024 May 4:100775. doi: 10.1016/j.crmeth.2024.100775. Online ahead of print.

ABSTRACT

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

PMID:38744286 | DOI:10.1016/j.crmeth.2024.100775

J Plant Physiol. 2024 May 7;298:154264. doi: 10.1016/j.jplph.2024.154264. Online ahead of print.

ABSTRACT

Proper plant growth requires balanced nutrient levels. In this study, we analyzed the relationship between ammonium (NH4+) nutrition and calcium (Ca2+) homeostasis in the leaf tissues of wild-type and mutant Arabidopsis specimens provided with different nitrogen sources (NH4+ and nitrate, NO3-). Providing plants with NH4+ as the sole nitrogen source disrupts Ca2+ homeostasis, which is essential for activating signaling pathways and maintaining the cell wall structure. The results revealed that the lower Ca2+ content in Arabidopsis leaves under NH4+ stress might result from reduced transpiration pull, which could impair root-to-shoot Ca2+ transport. Moreover, NH4+ nutrition increased the expression of genes encoding proteins responsible for exporting Ca2+ from the cytosol of leaf cells. Furthermore, overexpression of the Ca2+/H+ antiporter 1 (CAX1) gene alleviates the effects of NH4+ syndrome, including stunted growth. The oeCAX1 plants, characterized by a lower apoplastic Ca2+ level, grew better under NH4+ stress than wild-type plants. Evaluation of the mechanical properties of the leaf blades, including stiffness, strength, toughness, and extensibility, showed that the wild-type and oeCAX1 plants responded differently to the nitrogen source, highlighting the role of cell wall metabolism in inhibiting the growth of NH4+-stressed plants.

PMID:38744182 | DOI:10.1016/j.jplph.2024.154264

Diagn Microbiol Infect Dis. 2024 May 11;109(3):116349. doi: 10.1016/j.diagmicrobio.2024.116349. Online ahead of print.

ABSTRACT

Bacterial vaginosis (BV) is a prevalent vaginal illness resulting from a disruption in the vaginal microbial equilibrium. The vaginal microbiota has been shown to have a substantial impact on the development and continuation of BV. This work utilized 16S rRNA sequence analysis of vaginal microbiome samples (Control vs BV samples) utilizing Parallel-Meta 3 to investigate the variations in microbial composition. The unique genes identified were used to determine prospective therapeutic targets and their corresponding inhibitory ligands. Further, molecular docking was conducted and then MD simulations were carried out to confirm the docking outcomes. In the BV samples, we detected several anaerobic bacteria recognized for their ability to generate biofilms, namely Acetohalobium, Anaerolineaceae, Desulfobacteraceae, and others. Furthermore, we identified Dalfopristin, Clorgyline, and Hydrazine as potential therapeutic options for the management of BV. This research provides new insights into the causes of BV and shows the potential effectiveness of novel pharmacological treatments.

PMID:38744093 | DOI:10.1016/j.diagmicrobio.2024.116349

PLoS Comput Biol. 2024 May 14;20(5):e1012118. doi: 10.1371/journal.pcbi.1012118. Online ahead of print.

ABSTRACT

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

PMID:38743803 | DOI:10.1371/journal.pcbi.1012118

Proc Natl Acad Sci U S A. 2024 May 21;121(21):e2400260121. doi: 10.1073/pnas.2400260121. Epub 2024 May 14.

ABSTRACT

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

PMID:38743624 | DOI:10.1073/pnas.2400260121

Mol Divers. 2024 May 14. doi: 10.1007/s11030-024-10879-9. Online ahead of print.

ABSTRACT

Alcoholic liver injury resulting from excessive alcohol consumption is a significant social concern. Alcohol dehydrogenase (ADH) plays a critical role in the conversion of alcohol to acetaldehyde, leading to tissue damage. The management of alcoholic liver injury encompasses nutritional support and, in severe cases liver transplantation, but potential adverse effects exist, and effective medications are currently unavailable. Natural products with their potential benefits and historical use in traditional medicine emerge as promising alternatives. Triphala, a traditional polyherbal formula demonstrates beneficial effects in addressing diverse health concerns, with a notable impact on treating alcoholic liver damage through enhanced liver metabolism. The present study aims to identify potential active phytocompounds in Triphala targeting ADH to prevent alcoholic liver injury. Screening 119 phytocompounds from the Triphala formulation revealed 62 of them showing binding affinity to the active site of the ADH1B protein. Promising lipid-like molecule from Terminalia bellirica, (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid showed high binding efficiency to a competitive ADH inhibitor, 4-Methylpyrazole. Pharmacokinetic analysis further confirmed the drug-likeness and non-hepatotoxicity of the top-ranked compound. Molecular dynamics simulation and MM-PBSA studies revealed the stability of the docked complexes with minimal fluctuation and consistency of the hydrogen bonds throughout the simulation. Together, computational investigations suggest that (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid from the Triphala formulation holds promise as an ADH inhibitor, suggesting an alternative therapy for alcoholic liver injury.

PMID:38743308 | DOI:10.1007/s11030-024-10879-9

J Am Chem Soc. 2024 May 14. doi: 10.1021/jacs.4c02704. Online ahead of print.

ABSTRACT

Selective RNA delivery is required for the broad implementation of RNA clinical applications, including prophylactic and therapeutic vaccinations, immunotherapies for cancer, and genome editing. Current polyanion delivery relies heavily on cationic amines, while cationic guanidinium systems have received limited attention due in part to their strong polyanion association, which impedes intracellular polyanion release. Here, we disclose a general solution to this problem in which cationic guanidinium groups are used to form stable RNA complexes upon formulation but at physiological pH undergo a novel charge-neutralization process, resulting in RNA release. This new delivery system consists of guanidinylated serinol moieties incorporated into a charge-altering releasable transporter (GSer-CARTs). Significantly, systematic variations in structure and formulation resulted in GSer-CARTs that exhibit highly selective mRNA delivery to the lung (∼97%) and spleen (∼98%) without targeting ligands. Illustrative of their breadth and translational potential, GSer-CARTs deliver circRNA, providing the basis for a cancer vaccination strategy, which in a murine model resulted in antigen-specific immune responses and effective suppression of established tumors.

PMID:38743019 | DOI:10.1021/jacs.4c02704

mSphere. 2024 May 14:e0019624. doi: 10.1128/msphere.00196-24. Online ahead of print.

ABSTRACT

Environmental enteric dysfunction (EED) is a subclinical syndrome of altered small intestinal function postulated to be an important contributor to childhood undernutrition. The role of small intestinal bacterial communities in the pathophysiology of EED is poorly defined due to a paucity of studies where there has been a direct collection of small intestinal samples from undernourished children. Sixty-three members of a Pakistani cohort identified as being acutely malnourished between 3 and 6 months of age and whose wasting (weight-for-length Z-score [WLZ]) failed to improve after a 2-month nutritional intervention underwent esophagogastroduodenoscopy (EGD). Paired duodenal luminal aspirates and duodenal mucosal biopsies were obtained from 43 children. Duodenal microbiota composition was characterized by sequencing bacterial 16S rRNA gene amplicons. Levels of bacterial taxa (amplicon sequence variants [ASVs]) were referenced to anthropometric indices, histopathologic severity in biopsies, expression of selected genes in the duodenal mucosa, and fecal levels of an immunoinflammatory biomarker (lipocalin-2). A "core" group of eight bacterial ASVs was present in the duodenal samples of 69% of participants. Streptococcus anginosus was the most prevalent, followed by Streptococcus sp., Gemella haemolysans, Streptococcus australis, Granulicatella elegans, Granulicatella adiacens, and Abiotrophia defectiva. At the time of EGD, none of the core taxa were significantly correlated with WLZ. Statistically significant correlations were documented between the abundances of Granulicatella elegans and Granulicatella adiacens and the expression of duodenal mucosal genes involved in immune responses (dual oxidase maturation factor 2, serum amyloid A, and granzyme H). These results suggest that a potential role for members of the oral microbiota in pathogenesis, notably Streptococcus, Gemella, and Granulicatella species, warrants further investigation.IMPORTANCEUndernutrition among women and children is a pressing global health problem. Environmental enteric dysfunction (EED) is a disease of the small intestine (SI) associated with impaired gut mucosal barrier function and reduced capacity for nutrient absorption. The cause of EED is ill-defined. One emerging hypothesis is that alterations in the SI microbiota contribute to EED. We performed a culture-independent analysis of the SI microbiota of a cohort of Pakistani children with undernutrition who had failed a standard nutritional intervention, underwent upper gastrointestinal tract endoscopy, and had histologic evidence of EED in their duodenal mucosal biopsies. The results revealed a shared group of bacterial taxa in their duodenums whose absolute abundances were correlated with levels of the expression of genes in the duodenal mucosa that are involved in inflammatory responses. A number of these bacterial taxa are more typically found in the oral microbiota, a finding that has potential physiologic and therapeutic implications.

PMID:38742887 | DOI:10.1128/msphere.00196-24

Elife. 2024 May 14;13:e85722. doi: 10.7554/eLife.85722. Online ahead of print.

ABSTRACT

Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.

PMID:38742735 | DOI:10.7554/eLife.85722