Toxins (Basel). 2024 Dec 2;16(12):519. doi: 10.3390/toxins16120519.

ABSTRACT

The intricate combination of organic and inorganic compounds found in snake venom includes proteins, peptides, lipids, carbohydrates, nucleotides, and metal ions. These components work together to immobilise and consume prey through processes such as paralysis and hypotension. Proteins, both enzymatic and non-enzymatic, form the primary components of the venom. Based on the effects they produce, venom can be classified as neurotoxic, hemotoxic, and cytotoxic. Studies have shown that, after envenomation, proteins in snake venom also contribute significantly to the induction of inflammatory responses which can either have systemic or localized consequences. This review delves into the mechanisms by which snake venom proteins trigger inflammatory responses, focusing on key families such as phospholipase A2, metalloproteinases, serine proteases, C-type lectins, cysteine-rich secretory proteins, and L-amino acid oxidase. In addition, the role of venom proteins in activating various inflammatory pathways, including the complement system, inflammasomes, and sterile inflammation are also summarized. The available therapeutic options are examined, with a focus on antivenom therapy and its side effects. In general, this review offers a comprehensive understanding of the inflammatory mechanisms that are triggered by snake venom proteins and the side effects of antivenom treatment. All these emphasize the need for effective strategies to mitigate these detrimental effects.

PMID:39728777 | DOI:10.3390/toxins16120519

Metabolites. 2024 Dec 19;14(12):714. doi: 10.3390/metabo14120714.

ABSTRACT

Background: As the burden of Alzheimer's disease (AD) escalates with an ageing population, the demand for early and accessible diagnostic methods becomes increasingly urgent. Saliva, with its non-invasive and cost-effective nature, presents a promising alternative to cerebrospinal fluid and plasma for biomarker discovery. Methods: In this study, we conducted a comprehensive multi-omics analysis of saliva samples (n = 20 mild cognitive impairment (MCI), n = 20 Alzheimer's disease and age- and n = 40 gender-matched cognitively normal individuals), from the South Australian Neurodegenerative Disease (SAND) cohort, integrating proteomics, metabolomics, and microbiome data with plasma measurements, including pTau181. Results: Among the most promising findings, the protein Stratifin emerged as a top candidate, showing a strong negative correlation with plasma pTau181 (r = -0.49, p < 0.001) and achieving an AUC of 0.95 in distinguishing AD and MCI combined from controls. In the metabolomics analysis, 3-chlorotyrosine and L-tyrosine exhibited high correlations with disease severity progression, with AUCs of 0.93 and 0.96, respectively. Pathway analysis revealed significant alterations in vitamin B12 metabolism, with Transcobalamin-1 levels decreasing in saliva as AD progressed despite an increase in serum vitamin B12 levels (p = 0.008). Microbiome analysis identified shifts in bacterial composition, with a microbiome cluster containing species such as Lautropia mirabilis showing a significant decrease in abundance in MCI and AD samples. The overall findings were reinforced by weighted correlation network analysis, which identified key hubs and enriched pathways associated with AD. Conclusions: Collectively, these data highlight the potential of saliva as a powerful medium for early AD diagnosis, offering a practical solution for large-scale screening and monitoring.

PMID:39728495 | DOI:10.3390/metabo14120714

Metabolites. 2024 Dec 2;14(12):670. doi: 10.3390/metabo14120670.

ABSTRACT

Background: Diabetic encephalopathy (DE) is a neurological complication of diabetes marked by cognitive decline and complex metabolic disturbances. Salidroside (SAL), a natural compound with antioxidant and neuroprotective properties, has shown promise in alleviating diabetic complications. Exploring the spatial metabolic reprogramming in DE and elucidating SAL's metabolic effects are critical for deepening our understanding of its pathogenesis and developing effective therapeutic strategies. Methods: Air-flow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) was employed to investigate spatial metabolic alterations in the brains of db/db mice, a spontaneous DE model. The mice were treated with SAL (30 and 150 mg/kg, orally) for 12 weeks. Differential metabolites were identified and characterized using high-resolution mass spectrometry and validated against public databases. Results: Our AFADESI-MSI analysis revealed significant changes in 26 metabolites in the brains of DE mice compared to the controls. These metabolic changes indicated disruptions in glucose, glutamate-glutamine, nucleotide, lipid, choline, aspartate, and L-carnitine metabolism. Notably, glucose 6-phosphate (G6P), glutamine, adenosine, L-carnitine, and choline exhibited similar trends in both db/db mice and STZ-induced rat models of DE, suggesting their potential as reliable biomarkers. Twelve weeks of SAL treatment demonstrated a positive regulatory effect on glucose metabolism, the glutamate-glutamine cycle, and lipid metabolism. Conclusions: This study identifies key metabolic alterations in DE and demonstrates the therapeutic potential of SAL in modulating these disturbances, offering valuable insights for targeted interventions in diabetic complications.

PMID:39728451 | DOI:10.3390/metabo14120670

Curr Issues Mol Biol. 2024 Nov 29;46(12):13583-13606. doi: 10.3390/cimb46120812.

ABSTRACT

Neurological disorders such as Autism Spectrum Disorder (ASD), Schizophrenia (SCH), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) affect millions of people worldwide, yet their molecular mechanisms remain poorly understood. This study describes the application of the Comparative Analysis of Shapley values (CASh) to transcriptomic data from nine datasets associated with these complex disorders, demonstrating its effectiveness in identifying differentially expressed genes (DEGs). CASh, which combines Game Theory with Bootstrap resampling, offers a robust alternative to traditional statistical methods by assessing the contribution of each gene in the broader context of the complete dataset. Unlike conventional approaches, CASh is highly effective at detecting subtle but meaningful molecular patterns that are often missed. These findings highlight the potential of CASh to enhance the precision of transcriptomic analysis, providing a deeper understanding of the molecular mechanisms underlying these disorders and establishing a solid basis to improve diagnostic techniques and developing more targeted therapeutic interventions.

PMID:39727940 | DOI:10.3390/cimb46120812

Microbiol Resour Announc. 2024 Dec 27:e0100124. doi: 10.1128/mra.01001-24. Online ahead of print.

ABSTRACT

Here, we present amplicon sequences from sourdough starter cultures that have been treated with a chlorine concentration gradient mirroring public water distribution systems. Data derived present insights into the effect of important environmental factors that may influence the formation of microbial communities in food biomes.

PMID:39727394 | DOI:10.1128/mra.01001-24

Genetics. 2024 Dec 27:iyae208. doi: 10.1093/genetics/iyae208. Online ahead of print.

ABSTRACT

Expansion of nucleotide repeat sequences is associated with more than 40 human neuromuscular disorders. The different pathogenic mechanisms associated with the expression of nucleotide repeats are not well understood. We use a Caenorhabditis elegans model that expresses expanded CUG repeats only in cells of the body wall muscle and recapitulate muscle dysfunction and impaired organismal motility to identify the basis by which expression of RNA repeats is toxic to muscle function. Here, we performed 2 consecutive RNA interference screens and uncovered coenzyme Q metabolism and mitochondrial dysfunction as critical genetic modifiers of the motility phenotype. Furthermore, coenzyme Q supplementation reduced the toxic phenotypes, ameliorating the motility impairment and mitochondrial phenotypes. Together our data show how the expression of expanded RNA repeats can be toxic to mitochondrial homeostasis.

PMID:39727349 | DOI:10.1093/genetics/iyae208

CPT Pharmacometrics Syst Pharmacol. 2024 Dec 27. doi: 10.1002/psp4.13296. Online ahead of print.

ABSTRACT

Type 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood-brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K-AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K-AKT and MEK-ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with in vitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.

PMID:39727246 | DOI:10.1002/psp4.13296

Front Nutr. 2024 Dec 12;11:1479501. doi: 10.3389/fnut.2024.1479501. eCollection 2024.

ABSTRACT

INTRODUCTION: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.

AIM: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).

METHODS: Between 10/2018 and 09/2021, n = 314 patients and controls were prospectively enrolled in a cross-sectional study. A total of n = 230 features (anthropometric data, body composition, handgrip strength, gait speed, laboratory values, dietary habits, physical activity, mental health) were recorded. After data preprocessing (cleaning, feature exploration, imputation of missing data), n = 135 features were included in the ML analyses. Supervised ML models were used to classify malnutrition, and key features were identified using SHapley Additive exPlanations (SHAP).

RESULTS: Supervised ML effectively classified malnourished versus non-malnourished patients and controls. Excluding the existing GLIM criteria and malnutrition risk reduced model performance (sensitivity -19%, specificity -8%, F1-score -10%), highlighting their significance. Besides some GLIM criteria (weight loss, reduced food intake, disease/inflammation), additional anthropometric (hip and upper arm circumference), body composition (phase angle, SMMI), and laboratory markers (albumin, pseudocholinesterase, prealbumin) were key features for malnutrition classification.

CONCLUSION: ML analysis confirmed the clinical applicability of the current GLIM criteria and identified additional features that may improve malnutrition diagnosis and understanding of the pathophysiology of malnutrition in chronic gastrointestinal diseases.

PMID:39726873 | PMC:PMC11670747 | DOI:10.3389/fnut.2024.1479501

J Exp Bot. 2024 Dec 27:erae514. doi: 10.1093/jxb/erae514. Online ahead of print.

ABSTRACT

In aerobic life forms, reactive oxygen species (ROS) are produced by the partial reduction of oxygen during energy-generating metabolic processes. In plants, ROS production increases during periods of both abiotic and biotic stress, severely overloading the antioxidant systems. Hydrogen peroxide (H2O2) plays a central role in cellular redox homeostasis and signaling by oxidising crucial cysteines to sulfenic acid, which is considered a biologically relevant post-translational modification (PTM). Until now, the impact of the nucleus on cellular redox homeostasis has been relatively unexplored. The regulation of histone-modifying enzymes by oxidative PTMs at redox-sensitive cysteine or tyrosine residues is particularly intriguing because it allows the integration of redox signaling mechanisms with chromatin control of transcriptional activity. One of the most extensively studied histone acetyltransferases is the conserved GENERAL CONTROL NONDEPRESSIBLE 5 (GCN5) complex. This study investigated the nuclear sulfenome in Arabidopsis thaliana by expressing a nuclear variant of the Yeast Activation Protein-1 (YAP1) probe and identified 225 potential redox-active proteins undergoing S-sulfenylation. Mass spectrometry analysis further confirmed the S-sulfenylation of GCN5 at cysteines 293, 368, and 400, and their functional significance and impact on the GCN5 protein-protein interaction network were assessed using cysteine-to-serine mutagenesis.

PMID:39726278 | DOI:10.1093/jxb/erae514

BMC Plant Biol. 2024 Dec 27;24(1):1258. doi: 10.1186/s12870-024-05897-6.

ABSTRACT

Pre-harvest sprouting (PHS) in groundnut leads to substantial yield losses and reduced seed quality, resulting in reduced market value of groundnuts. Breeding cultivars with 14-21 days of fresh seed dormancy (FSD) holds promise for precisely mitigating the yield and quality deterioration. In view of this, six multi-locus genome-wide association study (ML-GWAS) models alongside a single-locus GWAS (SL-GWAS) model were employed on a groundnut mini-core collection using multi season phenotyping and 58 K "Axiom_Arachis" array genotyping data. A total of 9 significant SNP-trait associations (STAs) for FSD were detected on A01, A04, A08, A09, B02, B04, B05, B07 and B09 chromosomes using six ML-GWAS models. Additionally, the SL-GWAS model identified 38 STAs across 14 chromosomes of groundnut. A single STA on chromosome B02 (qFSD-B02-1) was consistently identified in both ML-GWAS and SL-GWAS models. Furthermore, candidate gene mining identified nine high confidence genes viz., Cytochrome P450 705 A, Dormancy/auxin associated family protein, WRKY family transcription factor, Protein kinase superfamily protein, serine/threonine protein phosphatase, myb transcription factor, transcriptional regulator STERILE APETALA-like, ethylene-responsive transcription factor 7-like and F-box protein interaction domain protein as prime regulators involved in Abscisic acid/Gibberellic acid signaling pathways regulating dormancy/germination. In addition, three of the allele-specific markers developed from the identified STAs were validated across a diverse panel. These markers hold potential for increasing dormancy in groundnut through marker-assisted selection (MAS). Thus, this research offers insights into genetic and molecular mechanisms underlying groundnut seed dormancy in addition to providing markers and donors for breeding future varieties with 2-3 weeks of FSD.

PMID:39725911 | DOI:10.1186/s12870-024-05897-6

PDA J Pharm Sci Technol. 2024 Dec 26;78(6):753-754. doi: 10.5731/pdajpst.2024.99902.

ABSTRACT

In pharmaceutical manufacturing, benefit is conferred in detection of specified microorganism (i.e., Burkholderia cepacia complex (BCC), E. coli, Pseudomonas aeruginosa, Salmonella enterica) not readily identified by culture-dependent methods. It's logical to test for the presence of "specified microorganism" using metagenomic analysis before culturing a "specified organism", especially when the organism isn't easy to culture. We developed a metagenomic analysis during enrichment to identify specified organisms. The enriched bacterial community consisted predominantly of Bacillus spp. and Stenotrophomonas spp., each contributing about 97-99% to total taxon abundance in TSB and 1/10× TSB. The specified microorganisms that were observed were Clostridium spp., Burkholderia spp., and Staphylococcus spp. (0.04 - 0.07%) in TSB, otherwise Burkholderia spp., Pseudomonas spp., Salmonella spp., Staphylococcus spp. and Escherichia spp. (0.01 - 1.73%) in 1/10× TSB. PreQ0 biosynthesis (PWY-6703) and guanosine ribonucleotides de novo biosynthesis (PWY-7221) were the most abundant pathways in 1/10× TSB-24 h. BCC chiefly contributed to the toluene degradation (PWY-5180 and PWY-5182) pathways. Initial results demonstrate the potential of the metagenomic approach during enrichment in water-based environments. These results indicate that a metagenomic enrichment approach to evaluating water samples can be useful to monitor specified organisms over time, including oligotrophs such as BCC in 1/10× TSB.

PMID:39725489 | DOI:10.5731/pdajpst.2024.99902

BMJ Open. 2024 Dec 26;14(12):e091884. doi: 10.1136/bmjopen-2024-091884.

ABSTRACT

INTRODUCTION: Children represent a large and vulnerable patient group. However, the evidence base for most paediatric diagnostic and therapeutic procedures remains limited or is often inferred from adults. There is an urgency to improve paediatric healthcare provision based on real-world evidence generation. Digital transformation is a unique opportunity to shape a data-driven, agile, learning healthcare system and deliver more efficient and personalised care to children and their families. The goal of Paediatric Personalized Research Network Switzerland (SwissPedHealth) is to build a sustainable and scalable infrastructure to make routine clinical data from paediatric hospitals in Switzerland interoperable, standardised, quality-controlled, and ready for observational research, quality assurance, trials and health-policy creation. This study describes the design, aims and current achievements of SwissPedHealth.

METHODS AND ANALYSIS: SwissPedHealth was started in September 2022 as one of four national data streams co-funded by the Swiss Personalized Health Network (SPHN) and the Personalized Health and Related Technologies (PHRT). SwissPedHealth develops modular governance and regulatory strategies and harnesses SPHN automatisation procedures in collaboration with clinical data warehouses, the Data Coordination Center, Biomedical Information Technology Network, and other SPHN institutions and funded projects. The SwissPedHealth consortium is led by a multisite, multidisciplinary Steering Committee, incorporating patient and family representatives. The data stream contains work packages focusing on (1) governance and implementation of standardised data collection, (2) nested projects to test the feasibility of the data stream, (3) a lighthouse project that enriches the data stream by integrating multi-omics data, aiming to improve diagnoses of rare diseases and 4) engagement with families through patient and public involvement activities and bioethics interviews.

ETHICS AND DISSEMINATION: The health database regulation of SwissPedHealth was approved by the ethics committee (AO_2022-00018). Research findings will be disseminated through national and international conferences and publications in peer-reviewed journals, and in lay language via online media and podcasts.

PMID:39725440 | DOI:10.1136/bmjopen-2024-091884

J Biol Chem. 2024 Dec 24:108130. doi: 10.1016/j.jbc.2024.108130. Online ahead of print.

ABSTRACT

The formation of biomolecular condensates contributes to intracellular compartmentalization, and plays an important role in many cellular processes. The characterization of condensates is however challenging, requiring advanced biophysical or biochemical methods that are often less suitable for in vivo studies. A particular need for easily accessible yet thorough methods that enable the characterization of condensates across different experimental systems thus remains. To address this, we present PhaseMetrics, a semi-automated FIJI-based image analysis pipeline tailored for quantifying particle properties from microscopy data. Tested using the FG-domain of yeast nucleoporin Nup100, PhaseMetrics accurately assesses particle properties across diverse experimental setups, including particles formed in vitro in chemically defined buffers or in Xenopus egg extracts, and in cellular systems. Comparing the results with biochemical assays, we conclude that PhaseMetrics reliably detects changes induced by various conditions, including the presence of polyethylene glycol, 1,6-hexanediol, or a salt gradient, as well as the activity of the molecular chaperone DNAJB6b and the protein disaggregase Hsp104. Given the flexibility in its analysis parameters, the pipeline should also be applicable to other condensate-forming systems and we show it application for detecting TDP-43 particles. By enabling the accurate representation of the variability within the population and the detection of subtle changes at the single-condensate level, the method complements conventional biochemical assays. Combined, PhaseMetrics is an easily accessible, customizable pipeline that enables imaging-based quantitative assessment of biomolecular condensates in vitro and in cells, providing a valuable addition to the current toolbox.

PMID:39725032 | DOI:10.1016/j.jbc.2024.108130

Plant Physiol Biochem. 2024 Dec 20;219:109430. doi: 10.1016/j.plaphy.2024.109430. Online ahead of print.

ABSTRACT

To explore the bio-effects during Moon exploration missions, we utilized the Chang'E 5 probe to carry the seeds of Oryza. Sativa L., which were later returned to Earth after 23 days in lunar orbit and planted in an artificial climate chamber. Compared to the control group, rice seeds that underwent spaceflight showed inhibited growth and development when planted on the ground. Then we collected samples and employed RNA sequencing (RNA-Seq) and whole-genome bisulfite sequencing (WGBS) in the tillering and heading stages of rice. To gain a comprehensive understanding of the dysregulation in molecular interaction patterns during Moon exploration, a bioinformatics pipeline based on mRNA-meth-miRNA Single-Sample Networks (SSNs) was developed. Specifically, we constructed four SSNs for each sample at the mRNA, DNA methylation (promoter and gene bodies), and miRNA levels. By combining with the Protein-Protein Interaction (PPI) network, SSNs can character individual-specific gene interaction patterns. Under spaceflight conditions, distinct interaction patterns emerge across various omics levels. However, the molecules driving changes at each omics level predominantly regulate consistent biological functions, such as metabolic processes, DNA damage and repair, cell cycle, developmental processes, etc. In the tillering stage, pathways such as ubiquitin mediated proteolysis, nucleotide excision repair, and nucleotide metabolism are significantly enriched. Moreover, we identified 18 genes that played key/hub roles in the dysregulation of multi-omics molecular interaction patterns, and observed their involvement in regulating the above biological processes. As aforementioned, our multi-omics SSNs method can reveal the molecular interaction patterns under deep space exploration.

PMID:39724765 | DOI:10.1016/j.plaphy.2024.109430

Clin Chem. 2024 Dec 26:hvae187. doi: 10.1093/clinchem/hvae187. Online ahead of print.

ABSTRACT

BACKGROUND: Nucleic acid amplification tests (NAATs) assist in the diagnosis of numerous infectious diseases. They are typically sensitive and specific and can be quickly developed and adapted. Far more challenging is the development of standards to ensure NAATs are performing within specification; reference materials take time to develop and suitable reference measurement procedures (RMPs) have not been available. This study investigated digital PCR (dPCR) RMP delivery of traceability for NAAT external quality assessment (EQA).

METHODS: Three National Metrology Institutes (NMIs) applied reverse transcription (RT)-dPCR as a candidate RMP to estimate the RNA quantity in 32 independent severe acute respiratory syndrome coronavirus 2 materials. The results were combined to value assign the respective materials: 21 materials were used in 6 rounds of EQA over 17 months for 61 laboratories for COVID-19 testing results compared with reference values.

RESULTS: The agreement between the 3 NMIs showed <2-fold difference between laboratories. EQA laboratory reverse transcription quantitative PCR (RT-qPCR) values estimation of viral RNA quantity showed good median agreement with RT-dPCR reference value; however, RT-qPCR differences were generally between 10- and 50-fold between laboratories.

CONCLUSION: This work demonstrates how RT-dPCR can provide reference values for whole virus materials for NAAT quality assurance. RT-dPCR values guided EQA control material selection and provided EQA participants with traceability to RNA copy number delivered through the RMP. This approach can be used to support routine reference material use as well as to standardize quality assurance for NAATs where established reference materials are not available, such as in disease outbreaks.

PMID:39724302 | DOI:10.1093/clinchem/hvae187

J Exp Bot. 2024 Dec 26:erae507. doi: 10.1093/jxb/erae507. Online ahead of print.

ABSTRACT

Our current agricultural system faces a perfect storm-climate change, burgeoning population, and unpredictable outbreaks like COVID-19 disrupt food production, particularly for vulnerable populations in developing countries. A paradigm shift in agriculture practices is needed to tackle these issues. One solution is the diversification of crop production. While ~56% of the protein consumed from plants stems from three major cereal crops (rice, wheat and maize), underutilized crops such as millets, legumes and other cereals are highly neglected by farmers and the research community. Millets are one of the most ancient and versatile orphan crops with attributes like fast-growing, high-yielding, withstanding harsh environments, and rich in micronutrients such as iron and zinc, making them appealing to achieve agronomic sustainability. Here, we highlight the contribution of millet to agriculture and pay attention to the latest research on the genetic diversity of millet, genomic resources, and next-generation omics and their applications under various stress conditions. Additionally, integrative omics technologies could identify and develop millets with desirable phenotypes having high agronomic value and mitigating climate change. Here, we emphasize that biotechnological interventions, such as genome-wide association, genomic selection, genome editing, and artificial intelligence/machine learning, can improve and breed millets more effectively.

PMID:39724286 | DOI:10.1093/jxb/erae507

J Cancer Res Clin Oncol. 2024 Dec 26;151(1):17. doi: 10.1007/s00432-024-06015-7.

ABSTRACT

OBJECTIVE: To investigate the mechanism by which heterogeneity in breast cancer developed and acted in single-cell transcriptomes.

METHODS: The composition of breast cancer based on the single-cell transcriptomes of 54,055 high-quality cells from clinical specimens of 4 malignant and 4 non-malignant patients were investigated.

RESULTS: We identified six common expression programs and six subtype-specific expression programs form malignant epithelial cells. The expression program of malignant epithelial cells exhibited activated EMT (Epithelial Mesenchymal Transition) in TME, which might indicate EMT intervention have a general therapeutic effect on various subtypes. Gene set enrichment analysis (GSEA) based on the top 50 highly NMF (non-negative matrix factorization) score genes in each program depicted the distinct function of each program in breast cancer progression. Moreover, we revealed the profound cellular heterogeneity of myeloid cell lineages in breast cancer. In macrophages, two mainly tumor associated macrophages (TAMs), TAM1 and TAM2, were also detected and the highly variable genes in TAM2 were strongly enriched in IFN-α and IFN-γ. The changes of lipid metabolism pathways in macrophages are closely related to the microenvironment of breast cancer.

CONCLUSION: We constructed a comprehensive single-cell transcriptome atlas of 54,055 cells from 4 malignant and 4 nonmalignant patients, providing insights into the mechanisms underlying breast cancer progression and the development of potential therapeutic strategies in breast cancer.

PMID:39724260 | DOI:10.1007/s00432-024-06015-7

PLoS One. 2024 Dec 26;19(12):e0313302. doi: 10.1371/journal.pone.0313302. eCollection 2024.

ABSTRACT

Autophagy-dependent survival relies on a crucial oscillatory response during cellular stress. Although oscillatory behaviour is typically associated with processes like the cell cycle or circadian rhythm, emerging experimental and theoretical evidence suggests that such periodic dynamics may explain conflicting experimental results in autophagy research. In this study, we demonstrate that oscillatory behaviour in the regulation of the non-selective, stress-induced macroautophagy arises from a series of interlinked negative and positive feedback loops within the mTORC1-AMPK-ULK1 regulatory triangle. While many of these interactions have been known for decades, recent discoveries have revealed how mTORC1, AMPK, and ULK1 are truly interconnected. Although these new findings initially appeared contradictory to established models, additional experiments and our systems biology analysis clarify the updated regulatory structure. Through computational modelling of the autophagy oscillatory response, we show how this regulatory network governs autophagy induction. Our results not only reconcile previous conflicting experimental observations but also offer insights for refining autophagy regulation and advancing understanding of its mechanisms of action.

PMID:39724154 | DOI:10.1371/journal.pone.0313302

PLoS Genet. 2024 Dec 26;20(12):e1011456. doi: 10.1371/journal.pgen.1011456. Online ahead of print.

ABSTRACT

Inhibitory killer cell immunoglobulin-like receptors (iKIRs) are a family of inhibitory receptors that are expressed by natural killer (NK) cells and late-stage differentiated T cells. There is accumulating evidence that iKIRs regulate T cell-mediated immunity. Recently, we reported that T cell-mediated control was enhanced by iKIRs in chronic viral infections. We hypothesized that in the context of autoimmunity, where an enhanced T cell response might be considered detrimental, iKIRs would have an opposite effect. We studied Type 1 diabetes (T1D) as a paradigmatic example of autoimmunity. In T1D, variation in the Human Leucocyte Antigen (HLA) genes explains up to 50% of the genetic risk, indicating that T cells have a major role in T1D etiopathogenesis. To investigate if iKIRs affect this T cell response we asked whether HLA associations were modified by iKIR genes. We conducted an immunogenetic analysis of a case-control T1D dataset (N = 11,961) and found that iKIR genes, in the presence of genes encoding their ligands, have a consistent and significant effect on protective HLA class II genetic associations. Our results were validated in an independent data set. We conclude that iKIRs significantly decrease HLA class II protective associations and suggest that iKIRs regulate CD4+ T cell responses in T1D.

PMID:39724143 | DOI:10.1371/journal.pgen.1011456

Cell Reprogram. 2024 Dec 26. doi: 10.1089/cell.2024.54625. Online ahead of print.

NO ABSTRACT

PMID:39723957 | DOI:10.1089/cell.2024.54625