Microbiol Resour Announc. 2024 Oct 29:e0093724. doi: 10.1128/mra.00937-24. Online ahead of print.

ABSTRACT

Bulleidia sp. is a non-spore-forming, obligatory anaerobic, Gram-positive bacterium isolated from the stool samples of human colon cancer patients. We report the complete genome sequence of Bulleidia sp. 10714-15, comprising a single linear chromosome of 2,218,984 bp with a G + C content of 36.6%.

PMID:39470244 | DOI:10.1128/mra.00937-24

Antimicrob Agents Chemother. 2024 Oct 29:e0116524. doi: 10.1128/aac.01165-24. Online ahead of print.

ABSTRACT

Cephalosporin resistance in Neisseria gonorrhoeae has severely compromised the efficacy of World Health Organization (WHO)-recommended therapies. This study aimed to methodologically evaluate the optimized Six-CodonPlus assay, and additionally conducted a multicenter evaluation to assess its clinical application, especially for predicting antimicrobial resistance (AMR). For methodological evaluation, 397 sequence-known N. gonorrhoeae isolates were evaluated for specificity, 17 nongonococcal isolates were assessed for cross-reactivity, 159 uncultured urogenital swabs and urine samples were evaluated for sensitivity at the clinical level. For multicenter evaluation, 773 isolates with confirmed phenotypic data and 718 clinical urogenital swabs collected from four geographical cities were, respectively, utilized for the evaluation of AMR-prediction strategies and the clinical application of the assay. The assay accurately identified specific single-nucleotide polymorphisms in resistance-associated genes, the detection limits dropped to 10 copies/reaction for individual targets. The specificity reached 100% and no cross-reactivity occurred with double-target confirmation. The assay could be directly applied to clinical samples containing over 20 copies/reaction. Multicenter evaluation formulated two optimal strategies for decreased susceptibility prediction in specific scenarios, and one tactic for prediction of resistance and identification of FC428-like strains. High sensitivity of 86.84% (95% CI, 71.11-95.05) and specificity of 99.59% (95% CI, 98.71-99.89) for resistance prediction were demonstrated for ceftriaxone (CRO). Regarding N. gonorrhoeae identification among multicenter swabs, specificity reached 97.53% (95% CI, 95.49-98.69), and sensitivity reached 93.77% (95% CI, 90.04-96.22). The Six-CodonPlus assay exhibited excellent detection performance and formulated optimal AMR-related prediction strategy with regional adaptability, providing critical information for population screening and clinical treatment.

PMID:39470197 | DOI:10.1128/aac.01165-24

Front Pharmacol. 2024 Oct 14;15:1474898. doi: 10.3389/fphar.2024.1474898. eCollection 2024.

ABSTRACT

INTRODUCTION: The resolution of the skin's inflammatory response is only possible if its barrier function is restored. TRPV1 channel activation plays an important role during inflammation but the effect of this activation on the skin barrier under inflammatory conditions has not been clarified. We hypothesize that it could potentially aid the keratinocyte barrier by reducing inflammatory cytokine release and promoting tight junction development.

METHODS: To explore the role of TRPV1 activation in inflammation, we designed and optimized an in vitro model of keratinocytes with basal epidermal layer characteristics using HaCaT cells and TNFα to induce inflammation.

RESULTS: TNFα increased the gene expression of tight junction protein claudin 1 (CLDN1) by at least 2.60 ± 0.16-fold, in a concentration-dependent manner, over a 48 h period. The administration of a capsaicin pre-treatment reduced the CLDN1 expression to 1.51 ± 0.16-fold during the first 6 h after TNFα induction, whereas IL-8 cytokine release was reduced 0.64 ± 0.17-fold. After 48 h, CLDN1 protein levels increased by a factor of 6.57 ± 1.39 compared to cells only treated with TNFα.

DISCUSSION: These results suggest that activation of TRPV1 by capsaicin can potentiate the increase in CLDN1 expression and CLDN1 protein synthesis induced by TNFα in cultured keratinocytes, while reducing the release of IL-8.

PMID:39469627 | PMC:PMC11513304 | DOI:10.3389/fphar.2024.1474898

Front Physiol. 2024 Oct 9;15:1341649. doi: 10.3389/fphys.2024.1341649. eCollection 2024.

ABSTRACT

An overactive orexin (OX) system is associated with neurogenic hypertension and an exaggerated chemoreflex in spontaneously hypertensive rats (SHRs). However, the chronology and mechanism of this association is unclear. We hypothesized that increased postnatal neurogenesis of OX neurons in SHRs precedes and contributes to the aberrant increase in mean arterial blood pressure (MAP) and the exaggerated response to hypercapnia during postnatal development. Using immunohistochemical methods and bromodeoxyuridine, we mapped the timeline of orexin neuron neurogenesis and maturation during early postnatal development. We then used whole-body plethysmography with EEG and EMG to map the development of mean arterial pressure (MAP) and state regulation. Finally, we used OX-targeted saporin toxin to determine the effects of eliminating excess OX neurons on the elevated MAP and exaggerated chemoreflex in adult SHRs. We found that both SHRs and Wistar-Kyoto (WKY) rats experienced postnatal increases in OX neurons. However, SHRs experienced a greater increase than WKY rats before P15, which led to significantly more OX neurons in SHRs than age-matched WKY controls by P15-16 (3,720 ± 780 vs. 2,406 ± 363, p = 0.005). We found that neurogenesis, as evidenced by BrdU staining in OX-positive neurons, was the primary contributor to the excess OX neurons in SHRs during early postnatal development. While SHRs develop more OX neurons by P15-16, SHRs and normotensive WKY control rats have similar MAP during postnatal development until P25 in wakefulness (81.6 ± 6.6 vs. 67.5 ± 6.8 mmHg, p = 0.006) and sleep (79.3 ± 6.1 vs. 66.6 ± 6.5, p = 0.009), about 10 days after the surge of OX neurons. By selectively eliminating excess (∼30%) OX neurons in SHRs, we saw a significantly lowered MAP and hypercapnic ventilatory chemoreflex compared to non-lesioned SHRs at P40. Additionally, we found unique signatures in state indicative of the attention defecit phenotype commonly associated with this model. We suggest that the postnatal increase of OX neurons, primarily attributed to exaggerated postnatal OX neurogenesis, may be necessary for the development of higher MAP and exaggerated chemoreflex in SHRs, and modulation of the overactive OX system may have a potential therapeutic effect during the pre-hypertensive period.

PMID:39469444 | PMC:PMC11513569 | DOI:10.3389/fphys.2024.1341649

Bioinformatics. 2024 Oct 28:btae646. doi: 10.1093/bioinformatics/btae646. Online ahead of print.

ABSTRACT

MOTIVATION: Knowledge of the specific cell types affected by genetic alterations in rare diseases is crucial for advancing diagnostics and treatments. Despite significant progress, the cell types involved in the majority of rare disease manifestations remain largely unknown. In this study, we integrated scRNA-seq data from non-diseased samples with known genetic disorder genes and phenotypic information to predict the specific cell types disrupted by pathogenic mutations for 482 disease phenotypes.

RESULTS: We found significant phenotype-cell type associations focusing on differential expression and co-expression mechanisms. Our analysis revealed that 13% of the associations documented in the literature were captured through differential expression, while 42% were elucidated through co-expression analysis, also uncovering potential new associations. These findings underscore the critical role of cellular context in disease manifestation and highlight the potential of single-cell data for the development of cell-aware diagnostics and targeted therapies for rare diseases.

AVAILABILITY: All code generated in this work is available at https://github.com/SergioAlias/sc-coex.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39468724 | DOI:10.1093/bioinformatics/btae646

Genome Med. 2024 Oct 28;16(1):124. doi: 10.1186/s13073-024-01393-6.

ABSTRACT

BACKGROUND: Cancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile.

METHODS: We develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes.

RESULTS: Our strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses.

CONCLUSIONS: Our study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications.

PMID:39468667 | DOI:10.1186/s13073-024-01393-6

Biol Res. 2024 Oct 28;57(1):76. doi: 10.1186/s40659-024-00558-2.

ABSTRACT

BACKGROUND: There are many gaps in our understanding of the mechanisms involved in ovarian follicular development in cattle, particularly regarding follicular deviation, acquisition of ovulatory capacity, and preovulatory changes. Molecular evaluations of ovarian follicular cells during follicular development in cattle, especially serial transcriptomic analyses across key growth phases, have not been reported. This study aims to address this gap by analyzing gene expression using RNA-seq in granulosa and antral cells recovered from ovarian follicular fluid during critical phases of ovarian follicular development in Holstein cows.

RESULTS: Integrated analysis of gene ontology (GO), gene set enrichment (GSEA), protein-protein interaction (PPI), and gene topology identified that differentially expressed genes (DEGs) in the largest ovarian follicles at deviation (Dev) were primarily involved in FSH-negative feedback, steroidogenesis, cell proliferation, apoptosis, and the prevention of early follicle rupture. In contrast, DEGs in the second largest follicles (DevF2) were mainly related to loss of cell viability, apoptosis, and immune cell invasion. In the dominant (PostDev) and preovulatory (PreOv) follicles, DEGs were associated with vascular changes and inflammatory responses.

CONCLUSIONS: The transcriptome of ovarian follicular fluid cells had a predominance of granulosa cells in the dominant follicle at deviation, with upregulation of genes involved in cell viability, steroidogenesis, and apoptosis prevention, whereas in the non-selected follicle there was upregulation of cell death-related transcripts. Immune cell transcripts increased significantly after deviation, particularly in preovulatory follicles, indicating strong intrafollicular chemotactic activity. We inferred that immune cell invasion occurred despite an intact basal lamina, contributing to follicular maturation.

PMID:39468655 | DOI:10.1186/s40659-024-00558-2

Acta Neuropathol Commun. 2024 Oct 28;12(1):170. doi: 10.1186/s40478-024-01874-0.

ABSTRACT

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

PMID:39468647 | DOI:10.1186/s40478-024-01874-0

J Cheminform. 2024 Oct 28;16(1):118. doi: 10.1186/s13321-024-00913-1.

ABSTRACT

The evaluation of compound-target interactions (CTIs) is at the heart of drug discovery efforts. Given the substantial time and monetary costs of classical experimental screening, significant efforts have been dedicated to develop deep learning-based models that can accurately predict CTIs. A comprehensive comparison of these models on a large, curated CTI dataset is, however, still lacking. Here, we perform an in-depth comparison of 12 state-of-the-art deep learning architectures that use different protein and compound representations. The models were selected for their reported performance and architectures. To reliably compare model performance, we curated over 300 thousand binding and non-binding CTIs and established several gold-standard datasets of varying size and information. Based on our findings, DeepConv-DTI consistently outperforms other models in CTI prediction performance across the majority of datasets. It achieves an MCC of 0.6 or higher for most of the datasets and is one of the fastest models in training and inference. These results indicate that utilizing convolutional-based windows as in DeepConv-DTI to traverse trainable embeddings is a highly effective approach for capturing informative protein features. We also observed that physicochemical embeddings of targets increased model performance. We therefore modified DeepConv-DTI to include normalized physicochemical properties, which resulted in the overall best performing model Phys-DeepConv-DTI. This work highlights how the systematic evaluation of input features of compounds and targets, as well as their corresponding neural network architectures, can serve as a roadmap for the future development of improved CTI models.Scientific contributionThis work features comprehensive CTI datasets to allow for the objective comparison and benchmarking of CTI prediction algorithms. Based on this dataset, we gained insights into which embeddings of compounds and targets and which deep learning-based algorithms perform best, providing a blueprint for the future development of CTI algorithms. Using the insights gained from this screen, we provide a novel CTI algorithm with state-of-the-art performance.

PMID:39468635 | DOI:10.1186/s13321-024-00913-1

Cell Commun Signal. 2024 Oct 28;22(1):522. doi: 10.1186/s12964-024-01821-5.

ABSTRACT

BACKGROUND: Transforming growth factor beta (TGFβ) is important for the morphogenesis and secretory function of the mammary gland. It is one of the main activators of the epithelial-mesenchymal transition (EMT), a process important for tissue remodeling and regeneration. It also provides cells with the plasticity to form metastases during tumor progression. Noncancerous and cancer cells respond differently to TGFβ. However, knowledge of the cellular signaling cascades triggered by TGFβ in various cell types is still limited.

METHODS: MCF10A (noncancerous, originating from fibrotic breast tissue) and MCF7 (cancer, estrogen receptor-positive) breast epithelial cells were treated with TGFB1 directly or through conditioned media from stimulated cells. Transcriptional changes (via RNA-seq) were assessed in untreated cells and after 1-6 days of treatment. Differentially expressed genes were detected with DESeq2 and the hallmark collection was selected for gene set enrichment analysis.

RESULTS: TGFB1 induces EMT in both the MCF10A and MCF7 cell lines but via slightly different mechanisms (signaling through SMAD3 is more active in MCF7 cells). Many EMT-related genes are expressed in MCF10A cells at baseline. Both cell lines respond to TGFB1 by decreasing the expression of genes involved in cell proliferation: through the repression of MYC (and the protein targets) in MCF10A cells and the activation of p63-dependent signaling in MCF7 cells (CDKN1A and CDKN2B, which are responsible for the inhibition of cyclin-dependent kinases, are upregulated). In addition, estrogen receptor signaling is inhibited and caspase-dependent cell death is induced only in MCF7 cells. Direct incubation with TGFB1 and treatment of cells with conditioned media similarly affected transcriptional profiles. However, TGFB1-induced protein secretion is more pronounced in MCF10A cells; therefore, the signaling is propagated through conditioned media (bystander effect) more effectively in MCF10A cells than in MCF7 cells.

CONCLUSIONS: Estrogen receptor-positive breast cancer patients may benefit from high levels of TGFB1 expression due to the repression of estrogen receptor signaling, inhibition of proliferation, and induction of apoptosis in cancer cells. However, some TGFB1-stimulated cells may undergo EMT, which increases the risk of metastasis.

PMID:39468555 | DOI:10.1186/s12964-024-01821-5

bioRxiv [Preprint]. 2024 Oct 15:2024.10.11.617911. doi: 10.1101/2024.10.11.617911.

ABSTRACT

Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles. Despite the important role of protein dynamics, their complexity and the absence of efficient representation methods have limited their integration into studies on protein function and mutation fitness, especially in deep learning applications. To address this, we present SeqDance, a protein language model designed to learn representation of protein dynamic properties directly from sequence alone. SeqDance is pre-trained on dynamic biophysical properties derived from over 30,400 molecular dynamics trajectories and 28,600 normal mode analyses. Our results show that SeqDance effectively captures local dynamic interactions, co-movement patterns, and global conformational features, even for proteins lacking homologs in the pre-training set. Additionally, we showed that SeqDance enhances the prediction of protein fitness landscapes, disorder-to-order transition binding regions, and phase-separating proteins. By learning dynamic properties from sequence, SeqDance complements conventional evolution- and static structure-based methods, offering new insights into protein behavior and function.

PMID:39464109 | PMC:PMC11507661 | DOI:10.1101/2024.10.11.617911

bioRxiv [Preprint]. 2024 Oct 14:2024.10.11.617898. doi: 10.1101/2024.10.11.617898.

ABSTRACT

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of non-primary cell systems, overreliance on single-cell gene expression profiling that not ultimately reflect protein activity and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathway as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically-relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells, represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

PMID:39464067 | PMC:PMC11507692 | DOI:10.1101/2024.10.11.617898

J Microbiol Biotechnol. 2024 Oct 11;34(12):1-9. doi: 10.4014/jmb.2408.08053. Online ahead of print.

ABSTRACT

Natural products, especially isoprenoids have many industrial applications, including medicine, fragrances, food additives, personal care and cosmetics, colorants, and even advanced biofuels. Recent advancements in metabolic engineering with synthetic biology and systems biology have drawn increased interest in microbial-based isoprenoid production. In order to engineer microorganisms to produce a large amount of value-added isoprenoids, great efforts have been made by employing various strategies from synthetic biology and systems biology. We also have engineered E. coli to produce various isoprenoids by targeting and engineering the isoprenoid biosynthetic pathways, methylerythritol phosphate (MEP), and mevalonate (MVA) pathways. Here, we introduced new combinations of the MVA pathway in E. coli with genes from biosafety level 1 (BSL 1) organisms. The reconstituted MVA pathway constructs (pSCS) are not only preferred to the living modified organism (LMO) regulation, but they also improved carotenoid production. In addition, the pSCS constructs resulted in enhanced lycopene production and cell-specific productivity compared to the previous MVA pathway combination (pSNA) in fed-batch fermentation. The pSCS constructs would not only bring an increase in isoprenoid production in E. coli, but they could be an efficient system to be applied for the industrial production of isoprenoids with industry-preferred genetic combinations.

PMID:39467704 | DOI:10.4014/jmb.2408.08053

Life Sci Alliance. 2024 Oct 28;8(1):e202402585. doi: 10.26508/lsa.202402585. Print 2025 Jan.

ABSTRACT

Actin and microtubules are essential cytoskeletal components and coordinate their dynamics through multiple coupling and decoupling mechanisms. However, how actin and microtubule dynamics are decoupled remains incompletely understood. Here, we identified TBC1D3C as a new regulator that can decouple actin filament assembly from microtubule disassembly. We showed that TBC1D3C induces the release of GEF-H1 from microtubules into the cytosol without perturbing microtubule arrays, leading to RhoA activation and actin filament assembly. Mechanistically, we found that TBC1D3C directly binds to GEF-H1, disrupting its interaction with the Tctex-DIC-14-3-3 complex and thereby displacing GEF-H1 from microtubules independently of microtubule disassembly. Super-resolution microscopy and live-cell imaging further confirmed that TBC1D3C triggers GEF-H1 release and actin filament assembly while maintaining microtubule integrity. Therefore, our findings demonstrated that TBC1D3C functions as a direct GEF activator and a novel regulator in decoupling actin assembly from microtubule disassembly, providing new insights into cytoskeletal regulation.

PMID:39467635 | DOI:10.26508/lsa.202402585

J Integr Plant Biol. 2024 Oct 28. doi: 10.1111/jipb.13791. Online ahead of print.

ABSTRACT

The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding.

PMID:39467106 | DOI:10.1111/jipb.13791

Elife. 2024 Oct 28;12:RP91994. doi: 10.7554/eLife.91994.

ABSTRACT

Genes are often regulated by multiple enhancers. It is poorly understood how the individual enhancer activities are combined to control promoter activity. Anecdotal evidence has shown that enhancers can combine sub-additively, additively, synergistically, or redundantly. However, it is not clear which of these modes are more frequent in mammalian genomes. Here, we systematically tested how pairs of enhancers activate promoters using a three-way combinatorial reporter assay in mouse embryonic stem cells. By assaying about 69,000 enhancer-enhancer-promoter combinations we found that enhancer pairs generally combine near-additively. This behaviour was conserved across seven developmental promoters tested. Surprisingly, these promoters scale the enhancer signals in a non-linear manner that depends on promoter strength. A housekeeping promoter showed an overall different response to enhancer pairs, and a smaller dynamic range. Thus, our data indicate that enhancers mostly act additively, but promoters transform their collective effect non-linearly.

PMID:39466837 | DOI:10.7554/eLife.91994

Clin Rev Allergy Immunol. 2024 Oct 28. doi: 10.1007/s12016-024-09009-y. Online ahead of print.

ABSTRACT

Regulatory T (Treg) cells are crucial for maintaining immune tolerance by suppressing response to self-antigens and harmless antigens to prevent autoimmune diseases and uncontrolled immune responses. Therefore, using Treg cells is considered a therapeutic strategy treating inflammatory diseases. Based on their origin, Treg cells are classified into thymus-derived, peripherally induced, and in vitro induced Treg cells. Our group discovered a novel Treg cell subset, namely, Treg-of-B (Treg/B) cells, generated by culturing CD4+CD25- T cells with B cells, including Peyer's patch B cells, splenic B cells and peritoneal B1a cells, for 3 days. Treg/B cells express CD44, OX40 (CD134), cytotoxic T-lymphocyte-associated antigen-4 (CD152), glucocorticoid-induced tumor necrosis factor receptor family-related protein (CD357), interleukin-10 receptor, lymphocyte activation gene-3 (CD223), inducible co-stimulator (CD278), programmed-death 1 (CD279), tumor necrosis factor receptor II, and high levels of IL-10, but not forkhead box protein P3, similar to type 1 Treg (Tr1) cells. However, unlike Tr1 cells, Treg/B cells do not express CD103, CD226, and latency-associated peptide. Treg/B cells have been applied for the treatment of some murine models of inflammatory diseases, including allergic asthma, inflammatory bowel disease, collagen-induced arthritis, gout, psoriasis and primary biliary cholangitis. This review summarizes the current knowledge of Treg/B cells.

PMID:39465485 | DOI:10.1007/s12016-024-09009-y

Mol Cancer. 2024 Oct 28;23(1):240. doi: 10.1186/s12943-024-02162-0.

ABSTRACT

BACKGROUND: In the ongoing battle against BCR-ABL+ leukemia, despite significant advances with tyrosine kinase inhibitors (TKIs), the persistent challenges of drug resistance and the enduring presence of leukemic stem cells (LSCs) remain formidable barriers to achieving a cure.

METHODS: In this study, we demonstrated that Disulfiram (DSF) induces ferroptosis to synergize with TKIs in inhibiting BCR-ABL+ cells, particularly targeting resistant cells and LSCs, using cell models, mouse models, and primary cells from patients. We elucidated the mechanism by which DSF promotes GPX4 degradation to induce ferroptosis through immunofluorescence, co-immunoprecipitation (CO-IP), RNA sequencing, lipid peroxidation assays, and rescue experiments.

RESULTS: Here, we present compelling evidence elucidating the sensitivity of DSF, an USA FDA-approved drug for alcohol dependence, towards BCR-ABL+ cells. Our findings underscore DSF's ability to selectively induce a potent cytotoxic effect on BCR-ABL+ cell lines and effectively inhibit primary BCR-ABL+ leukemia cells. Crucially, the combined treatment of DSF with TKIs selectively eradicates TKI-insensitive stem cells and resistant cells. Of particular note is DSF's capacity to disrupt GPX4 stability, elevate the labile iron pool, and intensify lipid peroxidation, ultimately leading to ferroptotic cell death. Our investigation shows that BCR-ABL expression induces alterations in cellular iron metabolism and increases GPX4 expression. Additionally, we demonstrate the indispensability of GPX4 for LSC development and the initiation/maintenance of BCR-ABL+ leukemia. Mechanical analysis further elucidates DSF's capacity to overcome resistance by reducing GPX4 levels through the disruption of its binding with HSPA8, thereby promoting STUB1-mediated GPX4 ubiquitination and subsequent proteasomal degradation. Furthermore, the combined treatment of DSF with TKIs effectively targets both BCR-ABL+ blast cells and drug-insensitive LSCs, conferring a significant survival advantage in mouse models.

CONCLUSION: In summary, the dual inhibition of GPX4 and BCR-ABL presents a promising therapeutic strategy to synergistically target blast cells and drug-insensitive LSCs in patients, offering potential avenues for advancing leukemia treatment.

PMID:39465372 | DOI:10.1186/s12943-024-02162-0

Microlife. 2024 Sep 14;5:uqae018. doi: 10.1093/femsml/uqae018. eCollection 2024.

ABSTRACT

Bacteria employ a myriad of regulatory mechanisms to adapt to the continuously changing environments that they face. They can, for example, use post-translational modifications, such as Nε-lysine acetylation, to alter enzyme activity. Although a lot of progress has been made, the extent and role of lysine acetylation in many bacterial strains remains uncharted. Here, we applied stable isotope labeling by amino acids in cell culture (SILAC) in combination with the immunoprecipitation of acetylated peptides and LC-MS/MS to measure the first Pseudomonas aeruginosa PAO1 acetylome, revealing 1076 unique acetylation sites in 508 proteins. Next, we assessed interstrain acetylome differences within P. aeruginosa by comparing our PAO1 acetylome with two publicly available PA14 acetylomes, and postulate that the overall acetylation patterns are not driven by strain-specific factors. In addition, the comparison of the P. aeruginosa acetylome to 30 other bacterial acetylomes revealed that a high percentage of transcription related proteins are acetylated in the majority of bacterial species. This conservation could help prioritize the characterization of functional consequences of individual acetylation sites.

PMID:39464744 | PMC:PMC11512479 | DOI:10.1093/femsml/uqae018

Virusdisease. 2024 Sep;35(3):434-445. doi: 10.1007/s13337-024-00882-x. Epub 2024 Jul 16.

ABSTRACT

Research on host-pathogen interactions (HPIs) has evolved rapidly during the past decades. The more humans discover new pathogens, the more challenging it gets to find a cure and prevent infections caused by those pathogens. Many experimental techniques have been proposed to predict the interactions but most of them are highly costly and time-consuming. Fortunately, computational methods have been proven to be efficient in overcoming such limitations. In this study, we propose utilizing Deep Learning methods to predict HPIs using protein sequences. We use the monoMonoKGap (mMKGap) algorithm with K = 2 to extract features from the sequences. We also used the Negatome Database to generate negative interactions. The proposed method was performed on three separate balanced human-pathogen datasets with 10-fold cross-validation. Our method yielded very high accuracies of 99.65%, 99.52%, and 99.66% (mean accuracy of 99.61%). To further evaluate the performance of the deep Network, we compared it with other classification methods, which were the Random Forest (RF) as multiple Decision Tree, the Support Vector Machine (SVM), and Convolutional Neural Network (CNN). We also tested the Dipeptide Composition algorithm as another feature extraction method to compare the results with the mMKGap method. The experimental results prove that the proposed method is very accurate, robust, and practical and could be used as a reliable framework in HPI research.

PMID:39464732 | PMC:PMC11502655 | DOI:10.1007/s13337-024-00882-x