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At least 793 records · Page 44Linked to original sources
PubMed2005

Inhibition of lipopolysaccharide-induced inflammatory responses by an apolipoprotein AI mimetic peptide.

Previous studies suggest that high-density lipoprotein and apoAI inhibit lipopolysaccharide (LPS)-induced inflammatory responses. The goal of the current study was to test the hypothesis that the apoAI mimetic peptide L-4F exerts antiinflammatory effects similar to apoAI. Pretreatment of human umbilical vein endothelial cells (HUVECs) with LPS induced the adhesion of THP-1 monocytes. Incubation of cells with LPS and L-4F (1 to 50 microg/mL) reduced THP-1 adhesion in a concentration-dependent manner. This response was associated with a significant reduction in the synthesis of cytokines, chemokines, and adhesion molecules. L-4F reduced vascular cell adhesion molecule-1 expression induced by LPS or lipid A, whereas a control peptide (Sc-4F) showed no effect. In contrast to LPS treatment, L-4F did not inhibit IL-1beta- or tumor necrosis factor-alpha-induced vascular cell adhesion molecule-1 expression. The inhibitory effect of L-4F on LPS induction of inflammatory markers was associated with reduced binding of LPS to its plasma carrier molecule, lipopolysaccharide binding protein, and decreased binding of LPS to HUVEC monolayers. LPS and L-4F in HUVEC culture medium were fractionated by fast protein liquid chromatography and were localized to the same fractions, suggesting a physical interaction between these molecules. Proinflammatory responses to LPS are associated with the binding of lipid A to cell surface receptors. The current studies demonstrate that L-4F reduces the expression of inflammatory markers induced by LPS and lipid A and suggest that apoAI peptide mimetics may be useful in the treatment of inflammation associated with endotoxemia.

Acute-Phase Proteins↗
PubMed1995

Intestinal apolipoprotein AI gene transcription is regulated by multiple distinct DNA elements and is synergistically activated by the orphan nuclear receptor, hepatocyte nuclear factor 4.

We have used apolipoprotein genes to investigate the signal transduction mechanisms involved in the control of intestinal specific gene expression. The human apoAI, apoCIII, and apoAIV genes are tandemly organized within a 15-kb DNA segment and are expressed predominantly in the liver and intestine. Transient transfection of various human apoAI gene plasmid constructs into human hepatoma (HepG2) and colon carcinoma (Caco-2) cells showed that apoAI gene transcription is under the control of two separate and distinct cell-specific promoters. The region between nucleotides -192 and -41 is essential for expression in HepG2 cells, whereas the region from -595 to -192 is essential for expression in Caco-2 cells. A third 0.6 kb DNA fragment in the apoCIII gene promoter region, approximately 5 kb down-stream from the human apoAI gene, enhances transcription mediated by either of these two tissue-specific apoAI promoters. In Caco-2 cells, expression of the apoAI gene and activation by the distal enhancer required the presence of a nuclear hormone receptor response element (NHRRE) located in the -214 to -192 apoAI promoter region. Overexpression of the orphan receptor hepatocyte nuclear factor 4 (HNF-4), which binds to the NHRRE, dramatically stimulates apoAI gene expression in Caco-2 cells but not in HepG2 cells. Maximal stimulation of transcription by HNF-4 in Caco-2 cells required the presence of both the intestinal specific promoter, the NHRRE, and distal enhancer elements. Transactivation by HNF-4 thus appears to result from functional synergy between the NHRRE binding HNF-4 and distal DNA elements containing intestinal-specific DNA binding activities. The apoAI gene provides a model system to define the mechanism(s) governing intestinal cell specific gene regulation and the role of nuclear hormone receptors in the establishment and regulation of enterocytic gene transcription.

Apolipoprotein A-I↗
PubMed2026

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

Artificial Intelligence↗
PubMed1995

Retinoid X receptor-alpha and apolipoprotein AI regulatory protein 1 differentially modulate 3,5,3'-triiodothyronine-induced transcription.

The formation of heterodimers with thyroid hormone receptor (TR) and other nuclear hormone receptors on thyroid hormone response elements (TREs) is important for T3-regulated gene transcription. The potential roles of retinoid X receptor-alpha (RXR alpha) and apolipoprotein regulatory protein-1 (ARP-1) in T3-mediated transcription were studied on a well characterized TRE, a direct repeat of AGGTCA separated by four nucleotides (DR4), using electrophoretic mobility shift assays and transient transfection in CV-1 cells. It was shown that RXR alpha blocked liganded TR alpha-induced transcription. However, ARP-1 did not block T3/TR alpha-mediated transcription, although both TR alpha/RXR alpha and TR alpha/ARP-1 heterodimers had similar affinities for the TRE-DR4. In contrast, wild type ARP-1 reversed the blocking effect of RXR alpha on the liganded TR alpha-induced transcription, whereas a dimerization domain-deleted ARP-1 mutant (ARP-1 delta C) did not, suggesting that ARP-1 modulates T3-induced transcription through a dimerization process. Thus, the TRE-DR4 differentially responds to T3 at the transcriptional level by accommodating a battery of TR heterodimeric complexes.

Animals↗
PubMed2025

Chromatin structures from integrated AI and polymer physics model.

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure from indirect measures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

Chromatin↗
PubMed1986

Semen assessment, fertility and the selection of Hereford bulls for use in AI.

Nineteen young Hereford bulls were used to study the relationship between semen characteristics and fertility in artificial insemination following 15 320 inseminations. Seven measures of sperm motility, morphological abnormalities, the release of hyaluronidase, ATP content and sperm head measurements were examined as predictors of fertility (49-day fixed-interval non-return rate). Two assessments of motility, three categories of abnormal spermatozoa, acrosomal changes and the release of hyaluronidase had predictive power. Multiple regression analysis showed that a combination of sperm motility after dilution in saline, motility after thawing and the proportion of coiled tails and proximal protoplasmic droplets provided the best prediction of fertility and allowed bulls to be ranked in order of observed non-return rate (%) with a Spearman correlation better than +0.80.

Adenosine Triphosphate↗
PubMed2003

The use of Geographic Information System (GIS) in the frame of the contingency plan implemented during the 1999-2001 avian influenza (AI) epidemic in Italy.

A Geographic Information System (GIS) is a very powerful and flexible software tool for effective management of spatially referenced data (e.g., geodata). Coupling database and GIS technology provides the tools for a detailed analysis of spatial patterns and distributions in veterinary applications. A specific veterinary GIS (VetGIS) toolbox was developed to perform the calculation of indices such as Lorenz curve, GINI index, and a kernel-based animal density estimation. This software was employed for the analysis and management of avian influenza in Italy during the 1999-2000 epidemic.

Animals↗
PubMed1988

Deoxyribonucleic acid (DNA) polymorphism in the apolipoprotein AI gene: a study in a Japanese population.

A Japanese group comprising 54 hyperlipidemic and 73 normolipidemic subjects were genotyped for DNA restriction fragment length polymorphisms (RFLPs) at the apoAI-CIII gene locus. The polymorphisms with Sstl were present at allelic frequencies of 0.63 (S1 allele) and 0.37 (S2 allele). Unlike the previously reported associations of the S2 allele with hypertriglycedemia found in Caucasians there was no difference in the frequency of S2 allele between normolipidemic and hyperlipidemic Japanese subjects. No significant association of genotypes were observed with serum levels of lipids and apolipoproteins, except for the association of the S2 allele with the higher mean level of HDL-cholesterol. In addition, among 26 patients who had coronary artery disease (CAD), as documented by angiography, there was no significant difference in the distribution of polymorphic alleles between CAD patients and normolipidemic control subjects.

Alleles↗
PubMed2026

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

BACKGROUND: Head and neck squamous cell carcinomas (HNSCCs) cause considerable morbidity and mortality. Multimodal treatment strategies can cause significant toxicity, and therapy options are limited for recurrent disease. Immunotherapy has emerged as a promising approach. However, patient response variability underscores the need for better predictive markers. OBJECTIVE: This study aims to use artificial intelligence to develop two predictive models in patients with HNSCC to assess (1) progression or recurrence following primary curative treatment and (2) long-term survival after immunotherapy schemes in recurrent and metastatic disease. This study will also describe the characteristics of patients with early, locally advanced, and recurrent or metastatic cancers. METHODS: This is a retrospective, observational study of data captured in electronic health records (EHRs) from participating hospitals between January 1, 2014, and December 31, 2021. This study's population comprises adults diagnosed with HNSCC at any stage. Study variables, including demographics, comorbidities, clinical variables, treatments, and outcomes, will be extracted using EHRead, a technology that applies natural language processing and machine learning to extract and analyze structured and unstructured clinical information in deidentified EHRs. Predictive models based on dynamic risk stratification for treatment response and progression or recurrence will be developed using multivariable logistic regressions, decision tree classifiers, and random forest approaches. Descriptive and outcome analyses will be shown for different anatomic subsites and stratified by stage and treatment. RESULTS: This study began enrolling sites in July 2021 and is currently ongoing. By December 2025, data from 10 centers has been collected, comprising a total of 151,934,990 EHRs from 2,159,719 patients. CONCLUSIONS: Development of predictive models using artificial intelligence will advance clinical understanding of HNSCC to improve patient outcomes.

Humans↗