Search

Introduction: Plastic pollution has surged due to increased human consumption and disposal of plastic products. Microbial communities capable of utilizing plastic as a carbon source may play a crucial role in degrading and consuming environmental plastic. In this study, we investigated the potential of a modified Winogradsky column (WC) to enrich Connecticut landfill soil for plastic-degrading bacteria and genes. Methods: By filling WCs with landfill soil and inorganic Bushnell Haas medium, and incorporating polyethylene (PE) strips at different soil layers, we aimed to identify bacterial taxa capable of degrading PE. We employed high-throughput 16S rRNA sequencing to identify the microbes cultivated on the plastic strips and the intervening landfill soil. We used PICRUSt2 to estimate the functional attributes of each community from 16S rRNA sequences. Results and discussion: After 12 months of incubation, distinct colors were observed along the WC layers, indicating successful cultivation. Sequencing revealed significant differences in bacterial communities between the plastic strips and the intervening landfill-soil habitats, including increased abundance of the phyla Verrucomicrobiota and Pseudomonadota (ne' e' Proteobacteria) on the strips. Based on inferred genomic content, the most highly abundant proteins in PE strip communities tended to be associated with plastic degradation pathways. Phylogenetic analysis of 16S rRNA sequences showed novel unclassified phyla and genera enriched on the plastic strips. Our findings suggest PE-supplemented Winogradsky columns can enrich for plastic-degrading microbes, offering insights into bioremediation strategies. [ABSTRACT FROM AUTHOR]

Dramatic genomic alterations, either inducible or in a pathological state, dismantle the core regulatory networks, leading to the activation of normally silent genes. Despite possessing immense therapeutic potential, accurate detection of these transcripts is an ever-challenging task, as it requires prior knowledge of the physiological gene expression levels. Here, we introduce EcTracker, an R-/Shiny-based single-cell data analysis web server that bestows a plethora of functionalities that collectively enable the quantitative and qualitative assessments of bona fide cell types or tissue-specific transcripts and, conversely, the ectopically expressed genes in the single-cell ribonucleic acid sequencing datasets. Moreover, it also allows regulon analysis to identify the key transcriptional factors regulating the user-selected gene signatures. To demonstrate the EcTracker functionality, we reanalyzed the CRISPR interference (CRISPRi) dataset of the human embryonic stem cells differentiated into endoderm lineage and identified the prominent enrichment of a specific gene signature in the SMAD2 knockout cells whose identity was ambiguous in the original study. The key distinguishing features of EcTracker lie within its processing speed, availability of multiple add-on modules, interactive graphical user interface and comprehensiveness. In summary, EcTracker provides an easy-to-perform, integrative and end-to-end single-cell data analysis platform that allows decoding of cellular identities, identification of ectopically expressed genes and their regulatory networks, and therefore, collectively imparts a novel dimension for analyzing single-cell datasets. [ABSTRACT FROM AUTHOR]

Olfactory receptors are primarily known to be expressed in the olfactory epithelium of the nasal cavity and therefore assist in odor perception. With the advent of high‐throughput omics technologies such as tissue microarray or RNA sequencing, a large number of olfactory receptors have been reported to be expressed in the nonolfactory tissues. Although these technologies uncovered the expression of these olfactory receptors in the nonchemosensory tissues, unfortunately, they failed to reveal the information about their cell type of origin. Accurate characterization of the cell types should be the first step towards devising cell type‐specific assays for their functional evaluation. Single‐cell RNA‐sequencing technology resolved some of these apparent limitations and opened new means to interrogate the expression of these extranasal olfactory receptors at the single‐cell resolution. Moreover, the availability of large‐scale, multi‐organ/species single‐cell expression atlases offer ample resources for the systematic reannotation of these receptors in a cell type‐specific manner. In this Viewpoint article, we discuss some of the technical limitations that impede the in‐depth understanding of these extranasal olfactory receptors, with a special focus on odorant receptors. Moreover, we also propose a list of single cell‐based omics technologies that could further promulgate the opportunity to decipher the regulatory network that drives the odorant receptors expression at atypical locations. [ABSTRACT FROM AUTHOR]

A prominent clinical symptom of 2019-novel coronavirus (nCoV) infection is hyposmia/anosmia (decrease or loss of sense of smell), along with general symptoms such as fatigue, shortness of breath, fever and cough. The identity of the cell lineages that underpin the infection-associated loss of olfaction could be critical for the clinical management of 2019-nCoV-infected individuals. Recent research has confirmed the role of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) as key host-specific cellular moieties responsible for the cellular entry of the virus. Accordingly, the ongoing medical examinations and the autopsy reports of the deceased individuals indicate that organs/tissues with high expression levels of ACE2 , TMPRSS2 and other putative viral entry-associated genes are most vulnerable to the infection. We studied if anosmia in 2019-nCoV-infected individuals can be explained by the expression patterns associated with these host-specific moieties across the known olfactory epithelial cell types, identified from a recently published single-cell expression study. Our findings underscore selective expression of these viral entry-associated genes in a subset of sustentacular cells (SUSs), Bowman's gland cells (BGCs) and stem cells of the olfactory epithelium. Co-expression analysis of ACE2 and TMPRSS2 and protein–protein interaction among the host and viral proteins elected regulatory cytoskeleton protein-enriched SUSs as the most vulnerable cell type of the olfactory epithelium. Furthermore, expression, structural and docking analyses of ACE2 revealed the potential risk of olfactory dysfunction in four additional mammalian species, revealing an evolutionarily conserved infection susceptibility. In summary, our findings provide a plausible cellular basis for the loss of smell in 2019-nCoV-infected patients. [ABSTRACT FROM AUTHOR]

Summary Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds. Availability and implementation MOA is available for Windows, Mac and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-by-step guide and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]