Search

ABSTRACT A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.IMPORTANCEWe have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

This proof-of-concept evaluation demonstrates that next-generation sequencing-based liquid biopsy can detect genomic alterations in the blood of cats with cancer and the absence of such alterations in the blood of presumably cancer-free cats. Two cats with cytologically confirmed lymphoma and nine presumably cancer-free cats were included in this analysis. Whole blood was collected from each subject and samples were subjected to DNA extraction, library preparation, and next-generation sequencing. Both cancer-diagnosed subjects had somatic copy number variants (a “cancer signal”) identified in cell-free DNA, suggesting the current presence of cancer in these subjects. All nine presumably cancer-free subjects had unremarkable genomic profiles, suggesting the absence of cancer in these subjects. Liquid biopsy using next-generation sequencing of cell-free DNA allows for blood-based detection of cancer-associated genomic alterations in cats. Such technology has the potential to offer considerable utility in veterinary medicine, particularly for the non-invasive prioritization of small cell intestinal lymphoma versus inflammatory bowel disease in cats with gastrointestinal signs. This study lays the foundation for future studies to fully validate this type of testing for use in clinical practice.

In the present study, thermoplastic polyurethane was reinforced with GO/Fe3O4/PPy nanocomposites with varying GO:Fe3O4 ratio to develop high-performance electromagnetic interference (EMI) shields. GO/Fe3O4/PPy fillers were prepared by adding polypyrrole (PPy) nanotubes, synthesized by the soft-template method from oxidative polymerization of pyrrole, to graphene oxide decorated with Fe3O4 nanoparticles prepared by a sonochemical method. X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy were performed to reveal the structural and morphological properties of GO/Fe3O4/PPy nanofillers, and the structure and thermal properties of the GO/Fe3O4/PPy/TPU nanocomposites was studied by cryofracture SEM and thermogravimetry (TGA). The effect of GO:Fe3O4 ratio on the electrical conductivity, magnetic permeability, permittivity, and electromagnetic-wave-shielding performance of GO/Fe3O4/PPy/TPU nanocomposites was investigated in the X-band frequency range. The maximum EMI efficiency was 4.81 dB with increasing GO:Fe3O4 ratio in GO/Fe3O4/PPy/TPU composites, with blocking of 66.72 % of the incident electromagnetic wave.

The Hedgehog (Hh) cascade is central to development, tissue homeostasis and cancer. A pivotal step in Hh signal transduction is the activation of glioma-associated (GLI) transcription factors by the atypical G protein-coupled receptor (GPCR) SMOOTHENED (SMO). How SMO activates GLI remains unclear. Here we show that SMO uses a decoy substrate sequence to physically block the active site of the cAMP-dependent protein kinase (PKA) catalytic subunit (PKA-C) and extinguish its enzymatic activity. As a result, GLI is released from phosphorylation-induced inhibition. Using a combination of in vitro, cellular and organismal models, we demonstrate that interfering with SMO-PKA pseudosubstrate interactions prevents Hh signal transduction. The mechanism uncovered echoes one used by the Wnt cascade, revealing an unexpected similarity in how these two essential developmental and cancer pathways signal intracellularly. More broadly, our findings define a mode of GPCR-PKA communication that may be harnessed by a range of membrane receptors and kinases.

BackgroundPatient disability, relapse rate, and age are used for family planning in multiple sclerosis (MS). However, the need for more accurate biomarkers is widely recognized. We aimed to explore the influence of age on neurofilament light chain (sNfL), which reflects acute inflammation; glial fibrillary acidic protein (GFAP), associated with disability progression independent of relapses; and anti-Müllerian hormone (AMH), reflecting ovarian reserve, to provide a tailored family planning strategy.MethodsThis case-control study included 95 MS patients and 61 healthy control women (HCW). sNfL and GFAP levels were measured using a sensitive single-molecule array assay. AMH levels were measured by the automated Elecsys® Anti-Müllerian Hormone Assay.ResultsWe observed no significant differences in AMH values between MS patients and the control group within any of the age-matched categories. Age exhibited a negative correlation with AMH values in both groups, as expected. Nevertheless, our findings suggest a slight tendency toward reduced ovarian reserve in MS patients (rho MS patients = −0.67, p < 0.0001; rho HCW = −0.43, p = 0.0006). Interestingly, among the 76 MS participants under 40 years old, we identified ten individuals (13.1%) with AMH levels below 0.7 ng/ml, indicative of a low ovarian reserve, and an additional six individuals (7.8%) with AMH levels between 0.7 ng/ml and 0.9 ng/ml, suggesting a potential risk of premature ovarian failure. Conversely, sNfL and GFAP levels in the MS group exhibited high variability but showed no significant association with age intervals.ConclusionWe found no significant differences in AMH, sNfL or GFAP values between MS patients and the control group within any of the age-matched categories. The assessment of AMH, sNFL and GFAP levels at MS onset facilitates personalized therapeutic and family planning strategies for childbearing-age women.

Abstract ANR (AraC negative regulators) are a novel class of small regulatory proteins commonly found in enteric pathogens. Aar (AggR-activated regulator), the best-characterized member of the ANR family, regulates the master transcriptional regulator of virulence AggR and the global regulator HNS in enteroaggregative Escherichia coli (EAEC) by protein–protein interactions. On the other hand, Rnr (RegA-negative regulator) is an ANR homolog identified in attaching and effacing (AE) pathogens, including Citrobacter rodentium and enteropathogenic Escherichia coli (EPEC), sharing only 25% identity with Aar. We previously found that C. rodentium lacking Rnr exhibits prolonged shedding and increased gut colonization in mice compared to the parental strain. To gain mechanistic insights into this phenomenon, we characterized the regulatory role of Rnr in the virulence of prototype EPEC strain E2348/69 by genetic, biochemical, and human organoid-based approaches. Accordingly, RNA-seq analysis revealed more than 500 genes differentially regulated by Rnr, including the type-3 secretion system (T3SS). The abundance of EspA and EspB in whole cells and bacterial supernatants confirmed the negative regulatory activity of Rnr on T3SS effectors. We found that besides HNS and Ler, twenty-six other transcriptional regulators were also under Rnr control. Most importantly, the deletion of aar in EAEC or rnr in EPEC increases the adherence of these pathogens to human intestinal organoids. In contrast, the overexpression of ANR drastically reduces bacterial adherence and the formation of AE lesions in the intestine. Our study suggests a conserved regulatory mechanism and a central role of ANR in modulating intestinal colonization by these enteropathogens despite the fact that EAEC and EPEC evolved with utterly different virulence programs.