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Archives of Microbiology Jan 2024A Gram-positive, aerobic, rod-shaped, spore-forming bacterium, designated NE201, was isolated from a freshwater pond in Village Nerur, India. Growth was observed in the...
A Gram-positive, aerobic, rod-shaped, spore-forming bacterium, designated NE201, was isolated from a freshwater pond in Village Nerur, India. Growth was observed in the range of 15-45 °C temperature with optimum at 30 °C, pH range of 5-9 (optimum at 7.0), and at concentrations of NaCl ranging between 0 and 14% (optimum 0%, w/v). The 16S rRNA gene sequence showed the highest similarity with Fictibacillus enclensis NIO-1003 (JF893461) at 99.01% followed by F. rigui WPCB074 (EU939689) at 98.9% and F. solisalsi CGMCC 1.6854 (EU046268) at 98.66%. The digital DNA-DNA hybridization (dDDH) and orthoANI values for strain NE201 against F. enclensis NIO-1003 (GCA_900094955.1) were 33.7% and 87.68%, respectively. The phylogenetic analysis based on the 16S rRNA gene, 92 core genes derived from the genome, and 20 proteins involving over 20,236 amino acid positions revealed the distinct phylogenetic position of strain NE201 and the formation of a clearly defined monophyletic clade with F. enclensis. The strain NE201 showed a unique carbon utilization and assimilation pattern that differentiated it from F. enclensis NIO-1003. The major fatty acids were anteiso -C (51.42%) and iso-C (18.88%). The major polar lipids were phosphatidylglycerol (PG), phosphatidylethanolamine (PE, and diphosphatidylglycerol (DPG). The antiSMASH analyzed genome of NE201 highlighted its diverse biosynthetic potential, unveiling regions associated with terpene, non-ribosomal peptide synthetases (NRPS), lassopeptides, NI-siderophores, lanthipeptides (LAP), and Type 3 Polyketide Synthases (T3PKS). The overall phenotypic, genotypic, and chemotaxonomic characters strongly suggested that the strain NE201 represents a novel species of genus Fictibacillus for which the name Fictibacillus fluitans sp. nov. is proposed. The type strain is NE201 (= MCC 5285 = JCM 36474).
Topics: Ponds; Phylogeny; RNA, Ribosomal, 16S; Fresh Water; DNA
PubMed: 38252164
DOI: 10.1007/s00203-023-03794-4 -
Science Advances Jan 2024Merkel cell carcinoma (MCC) is a rare and aggressive skin cancer. Inhibitors targeting the programmed cell death 1 (PD-1) immune checkpoint have improved MCC patient...
Merkel cell carcinoma (MCC) is a rare and aggressive skin cancer. Inhibitors targeting the programmed cell death 1 (PD-1) immune checkpoint have improved MCC patient outcomes by boosting antitumor T cell immunity. Here, we identify PD-1 as a growth-promoting receptor intrinsic to MCC cells. In human MCC lines and clinical tumors, RT-PCR-based sequencing, immunoblotting, flow cytometry, and immunofluorescence analyses demonstrated PD-1 gene and protein expression by MCC cells. MCC-PD-1 ligation enhanced, and its inhibition or silencing suppressed, in vitro proliferation and in vivo tumor xenograft growth. Consistently, MCC-PD-1 binding to PD-L1 or PD-L2 induced, while antibody-mediated PD-1 blockade inhibited, protumorigenic mTOR signaling, mitochondrial (mt) respiration, and ROS generation. Last, pharmacologic inhibition of mTOR or mtROS reversed MCC-PD-1:PD-L1-dependent proliferation and synergized with PD-1 checkpoint blockade in suppressing tumorigenesis. Our results identify an MCC-PD-1-mTOR-mtROS axis as a tumor growth-accelerating mechanism, the blockade of which might contribute to clinical response in patients with MCC.
Topics: Humans; B7-H1 Antigen; Carcinoma, Merkel Cell; Programmed Cell Death 1 Receptor; Reactive Oxygen Species; Skin Neoplasms; TOR Serine-Threonine Kinases
PubMed: 38241371
DOI: 10.1126/sciadv.adi2012 -
Scientific Reports Jan 2024Asynchronously cycling cells pose a challenge to the accurate characterization of phase-specific gene expression. Current strategies, including RNAseq, survey the steady...
Asynchronously cycling cells pose a challenge to the accurate characterization of phase-specific gene expression. Current strategies, including RNAseq, survey the steady state gene expression across the cell cycle and are inherently limited by their inability to resolve dynamic gene regulatory networks. Single cell RNAseq (scRNAseq) can identify different cell cycle transcriptomes if enough cycling cells are present, however some cells are not amenable to scRNAseq. Therefore, we merged two powerful strategies, the CDT1 and GMNN degrons used in Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) cell cycle sensors and the ribosomal protein epitope tagging used in RiboTrap/Tag technologies to isolate cell cycle phase-specific mRNA for sequencing. The resulting cell cycle dependent, tagged ribosomal proteins (ccTaggedRP) were differentially expressed during the cell cycle, had similar subcellular locations as endogenous ribosomal proteins, incorporated into ribosomes and polysomes, and facilitated the recovery of cell cycle phase-specific RNA for sequencing. ccTaggedRP has broad applications to investigate phase-specific gene expression in complex cell populations.
Topics: Cell Cycle Proteins; Transcriptome; Cell Cycle; Ribosomal Proteins; Ribosomes
PubMed: 38238470
DOI: 10.1038/s41598-024-52085-5 -
Current Opinion in Microbiology Feb 2024Early life represents a critical window for metabolic, cognitive and immune system development, which is influenced by the maternal microbiome as well as the infant gut... (Review)
Review
Early life represents a critical window for metabolic, cognitive and immune system development, which is influenced by the maternal microbiome as well as the infant gut microbiome. Antibiotic exposure, mode of delivery and breastfeeding practices modulate the gut microbiome and the reservoir of antibiotic resistance genes (ARGs). Vertical and horizontal microbial gene transfer during early life and the mechanisms behind these transfers are being uncovered. In this review, we aim to provide an overview of the current knowledge on the transfer of antibiotic resistance in the mother-infant dyad through vertical and horizontal transmission and to highlight the main gaps and challenges in this area.
Topics: Infant; Humans; Anti-Bacterial Agents; Drug Resistance, Bacterial; Gastrointestinal Microbiome; Microbiota; Gene Transfer, Horizontal
PubMed: 38237429
DOI: 10.1016/j.mib.2023.102424 -
Current Stem Cell Research & Therapy Jan 2024Retinal aging is one of the common public health problems caused by population aging and has become an important cause of acquired vision loss in adults. The aim of this...
BACKGROUND
Retinal aging is one of the common public health problems caused by population aging and has become an important cause of acquired vision loss in adults. The aim of this study was to determine the role of human umbilical cord mesenchymal stem cells (hUCMSCs) in delaying retinal ganglion cell (RGC) aging and part of the network of molecular mechanisms involved.
METHODS
A retinal ganglion cell senescence model was established in vitro and treated with UCMSC. Successful establishment of the senescence system was demonstrated using β- galactosidase staining. The ameliorative effect of MSC on senescence was demonstrated using CCK8 cell viability and Annexin V-PI apoptosis staining. The relevant targets of RGC, MSC, and senescence were mainly obtained by searching the GeneCards database. The protein interaction network among the relevant targets was constructed using the String database and Cytoscape, and 10 key target genes were calculated based on the MCC algorithm, based on which Gene ontologies (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were performed. Changes in relevant target genes were detected using real-time fluorescence quantitative PCR and the mechanism of action of UCMSC was determined by RNA interference.
RESULTS
β-galactosidase staining showed that UCMSC significantly reduced the positive results of RGC. The retinal aging process was alleviated. The bioinformatics screen yielded 201 shared genes. 10 key genes were selected by the MCC algorithm, including vascular endothelial growth factor A (VEGFA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), albumin (ALB), interleukin- 6 (IL6), tumor necrosis factor (TNF), tumor protein P53 (TP53), insulin (INS), matrix metalloproteinase 9 (MMP9), epidermal growth factor (EGF), interleukin-1β (IL1B), and enrichment to related transferase activity and kinase activity regulated biological processes involved in oxidative stress and inflammation related pathways. In addition, PCR results showed that all the above molecules were altered in expression after UCMSC involvement.
CONCLUSION
This experiment demonstrated the role of UCMSC in delaying retinal ganglion cell senescence and further elucidated that UCMSC may be associated with the activation of VEGFA, TP53, ALB, GAPDH, IL6, IL1B, MMP9 genes and the inhibition of INS, EGF, and TNF in delaying retinal senescence.
PubMed: 38204243
DOI: 10.2174/011574888X277276231215110316 -
Computational and Structural... Dec 2024Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are...
Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and Las bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.
PubMed: 38192372
DOI: 10.1016/j.csbj.2023.12.010 -
Frontiers in Cell and Developmental... 2023Therapy resistance is a major challenge in colorectal cancer management. Epigenetic changes, such as DNA methylation, in tumor cells are involved in the development of...
Therapy resistance is a major challenge in colorectal cancer management. Epigenetic changes, such as DNA methylation, in tumor cells are involved in the development of acquired resistance during treatment. Here, we characterized the DNA methylation landscape of colon circulating tumor cells (CTCs) during cancer progression and therapy resistance development. To this aim, we used nine permanent CTC lines that were derived from peripheral blood samples of a patient with metastatic colon cancer collected before treatment initiation (CTC-MCC-41) and during treatment and cancer progression (CTC-MCC-41.4 and CTC-MCC-41.5 [A-G]). We analyzed the DNA methylome of these nine CTC lines using EPIC arrays and also assessed the association between DNA methylation and gene expression profiles. We confirmed DNA methylation and gene expression results by pyrosequencing and RT-qPCR, respectively. The global DNA methylation profiles were different in the pre-treatment CTC line and in CTC lines derived during therapy resistance development. These resistant CTC lines were characterized by a more hypomethylated profile compared with the pre-treatment CTC line. Most of the observed DNA methylation differences were localized at CpG-poor regions and some in CpG islands, shore regions and promoters. We identified a distinctive DNA methylation signature that clearly differentiated the pre-treatment CTC line from the others. Of note, the genes involved in this signature were associated with cancer-relevant pathways, including PI3K/AKT, MAPK, Wnt signaling and metabolism. We identified several epigenetically deregulated genes associated with therapy resistance in CTCs, such as . Our results bring new knowledge on the epigenomic landscape of therapy-resistant CTCs, providing novel mechanisms of resistance as well as potential biomarkers and therapeutic targets for advanced CRC management.
PubMed: 38188020
DOI: 10.3389/fcell.2023.1291179 -
Cell Death & Disease Jan 2024Manipulation of the subcellular localization of transcription factors by preventing their shuttling via the nuclear pore complex (NPC) emerges as a novel therapeutic...
Manipulation of the subcellular localization of transcription factors by preventing their shuttling via the nuclear pore complex (NPC) emerges as a novel therapeutic strategy against cancer. One transmembrane component of the NPC is POM121, encoded by a tandem gene locus POM121A/C on chromosome 7. Overexpression of POM121 is associated with metabolic diseases (e.g., diabetes) and unfavorable clinical outcome in patients with colorectal cancer (CRC). Peroxisome proliferator-activated receptor-gamma (PPARγ) is a transcription factor with anti-diabetic and anti-tumoral efficacy. It is inhibited by export from the nucleus to the cytosol via the RAS-RAF-MEK1/2-ERK1/2 signaling pathway, a major oncogenic driver of CRC. We therefore hypothesized that POM121 participates in the transport of PPARγ across the NPC to regulate its transcriptional activity on genes involved in metabolic and tumor control. We found that POM121A/C mRNA was enriched and POM121 protein co-expressed with PPARγ in tissues from CRC patients conferring poor prognosis. Its interactome was predicted to include proteins responsible for tumor metabolism and immunity, and in-silico modeling provided insights into potential 3D structures of POM121. A peptide region downstream of the nuclear localization sequence (NLS) of POM121 was identified as a cytoplasmic interactor of PPARγ. POM121 positivity correlated with the cytoplasmic localization of PPARγ in patients with KRAS mutant CRC. In contrast, POM121A/C silencing by CRISPR/Cas9 sgRNA or siRNA enforced nuclear accumulation of PPARγ and activated PPARγ target genes promoting lipid metabolism and cell cycle arrest resulting in reduced proliferation of human CRC cells. Our data suggest the POM121-PPARγ axis as a potential drugable target in CRC.
Topics: Humans; Nuclear Pore; PPAR gamma; RNA, Guide, CRISPR-Cas Systems; Nuclear Pore Complex Proteins; Transcription Factors; Neoplasms; Membrane Glycoproteins
PubMed: 38177114
DOI: 10.1038/s41419-023-06371-1 -
Heliyon Jan 2024Approximately 50% of Merkel cell carcinoma (MCC) patients facing this highly aggressive skin cancer initially respond positively to PD-1-based immunotherapy....
Approximately 50% of Merkel cell carcinoma (MCC) patients facing this highly aggressive skin cancer initially respond positively to PD-1-based immunotherapy. Nevertheless, the recurrence of MCC post-immunotherapy emphasizes the pressing need for more effective treatments. Recent research has highlighted Cyclin-dependent kinases 4 and 6 (CDK4/6) as pivotal cell cycle regulators gaining prominence in cancer studies. This study reveals that the CDK4/6 inhibitor, palbociclib can enhance PD-L1 gene transcription and surface expression in MCC cells by activating HIF2α. Inhibiting HIF2α with TC-S7009 effectively counteracts palbociclib-induced PD-L1 transcription and significantly intensifies cell death in MCC. Simultaneously, co-targeting CDK4/6 and HIF2α boosts ROS levels while suppressing SLC7A11, a key regulator of cellular redox balance, promoting ferroptosis- a form of immunogenic cell death linked to iron. Considering the rising importance of immunogenic cell death in immunotherapy, this strategy holds promise for improving future MCC treatments, markedly increasing immunogenic cell death various across various MCC cell lines, thus advancing cancer immunotherapy.
PubMed: 38173534
DOI: 10.1016/j.heliyon.2023.e23521 -
Current Computer-aided Drug Design Jan 2024Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.
BACKGROUND
Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development.
AIMS
Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors.
OBJECTIVE
In this study, a disease target identification algorithm based on protein recognition is proposed.
METHODS
In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors.
RESULTS
A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins.
CONCLUSION
In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.
PubMed: 38173214
DOI: 10.2174/0115734099258183230929173855