Functional & Integrative Genomics最新文献

Fanconi anemia (FA) is a rare genetic disease characterized by congenital abnormalities and increased risk for bone marrow failure and cancer. Central nervous system defects, including acute and irreversible loss of neurological function and white matter lesions with calcifications, have become increasingly recognized among FA patients, and are collectively referred to as Fanconi Anemia Neurological Syndrome or FANS. The molecular etiology of FANS is poorly understood. In this study, we have used a functional integrative genomics approach to further define the function of the FANCD2 protein and FA pathway. Combined analysis of new and existing FANCD2 ChIP-seq datasets demonstrates that FANCD2 binds nonrandomly throughout the genome with binding enriched at transcription start sites and in broad regions spanning protein-coding gene bodies. FANCD2 demonstrates a strong preference for large neural genes involved in neuronal differentiation, synapse function, and cell adhesion, with many of these genes implicated in neurodevelopmental and neuropsychiatric disorders. Furthermore, FANCD2 binds to regions of the genome that replicate late, undergo mitotic DNA synthesis (MiDAS) under conditions of replication stress, and are hotspots for copy number variation. Our analysis describes an important targeted role for FANCD2 and the FA pathway in the maintenance of large neural gene stability.

The Asteraceae family, particularly the Artemisia genus, presents taxonomic challenges due to limited morphological characteristics and frequent natural hybridization. Molecular tools, such as chloroplast genome analysis, offer solutions for accurate species identification. In this study, we sequenced and annotated the chloroplast genome of Artemisia littoricola sourced from Dokdo Island, employing comparative analyses across six diverse Artemisia species. Our findings reveal conserved genome structures with variations in repeat sequences and junction boundaries. Notably, the chloroplast genome of A. littoricola spans 150,985 bp, consistent with other Artemisia species, and comprises 131 genes, including 86 protein-coding, 37 tRNA, and 8 rRNA genes. Among these genes, 16 possess a single intron, while clpP and ycf3 exhibit two introns each. Furthermore, 18 genes display duplicated copies within the IR regions. Moreover, the genome possesses 42 Simple Sequence Repeats (SSRs), predominantly abundant in A/T content and located within intergenic spacer regions. The analysis of codon usage revealed that the codons for leucine were the most frequent, with a preference for ending with A/U. While the chloroplast genome exhibited conservation overall, non-coding regions showed lower conservation compared to coding regions, with the Inverted Repeat (IR) region displaying higher conservation than single-copy regions. Phylogenetic analyses position A. littoricola within subgenus Dracunculus, indicating a close relationship with A. scoparia and A. desertorum. Additionally, biogeographic reconstructions suggest ancestral origins in East Asia, emphasizing Mongolia, China (North East and North Central and South Central China), and Korea. This study underscores the importance of chloroplast genomics in understanding Artemisia diversity and evolution, offering valuable insights into taxonomy, evolutionary patterns, and biogeographic history. These findings not only enhance our understanding of Artemisia’s intricate biology but also contribute to conservation efforts and facilitate the development of molecular markers for further research and applications in medicine and agriculture.

This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML’s revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML’s potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.

Lipoproteinassociated phospholipase A2 (Lp-PLA2), encoded by the phospholipase A2 group VII (Pla2g7) gene, has been pertinent to inflammatory responses. This study investigates the correlation between Lp-PLA2 and inflammatory injury in septic mice and explores its regulatory mechanism. Lp-PLA2 was found to be upregulated in the serum of septic mice induced by cecal ligation and puncture and in the culture supernatant of RAW264.7 cells following lipopolysaccharide and adenosine triphosphate treatments. The contents of Lp-PLA2 were positively correlated with increased concentrations of proinflammatory cytokines in patients with sepsis. Both animal and cellular models showed increased concentrations of proinflammatory cytokines. Spi-1 proto-oncogene (Spi1), highly expressed in these models, was found to activate Pla2g7 transcription. Knockdown of Pla2g7 or Spi1 reduced the proinflammatory cytokine production, mitigated organ damage in mice, and suppressed macrophage migration in vitro. Retinoblastoma binding protein 6 (Rbbp6), poorly expressed in both models, was found to reduce Spi1 protein stability through ubiquitination modification. Rbbp6 overexpression similarly suppressed inflammatory activation of RAW264.7 cells, which was counteracted by Pla2g7 or Spi1 upregulation. In summary, this study demonstrates that the Pla2g7 loss and Spi1 upregulation participate in inflammatory responses in sepsis by elevating the Lp-PLA2 levels.

After COVID-19, a turning point in the way of pharmaceutical technology is gene therapy with beneficial potential to start a new medical era. However, commercialization of such pharmaceuticals would never be possible without the help of nanotechnology. Nanomedicine can fulfill the growing needs linked to safety, efficiency, and site-specific targeted delivery of Gene therapy-based pharmaceuticals. This review's goal is to investigate how nanomedicine may be used to transfer nucleic acids by getting beyond cellular and physicochemical barriers. Firstly, we provide a full description of types of gene therapy, their mechanism, translation, transcription, expression, type, and details of diseases with possible mechanisms that can only be treated with genes-based pharmaceuticals. Additionally, we also reviewed different types of physicochemical barriers, physiological and cellular barriers in nucleic acids (DNA/RNA) based drug delivery. Finally, we highlight the need and importance of cationic lipid-based nanomedicine/nanocarriers in gene-linked drug delivery and how nanotechnology can help to overcome the above-discussed barrier in gene therapy and their biomedical applications.

Graphical abstract

Gastric cancer (GC) remains a major public health challenge worldwide. Long non-coding RNAs (lncRNAs) play important roles in the development, progression, and resistance to the treatment of GC, as shown by recent developments in molecular characterization. Still, an in-depth investigation of the lncRNA landscape in GC is absent. However, The objective of this systematic review is to evaluate our present understanding of the role that lncRNA dysregulation plays in the etiology of GC and treatment resistance, with a focus on the underlying mechanisms and clinical implications. Research that described the functions of lncRNA in angiogenesis, stemness, epigenetics, metastasis, apoptosis, development, and resistance to key treatments was given priority. In GC, it has been discovered that a large number of lncRNAs, including MALAT1, HOTAIR, H19, and ANRIL, are aberrantly expressed and are connected with disease-related outcomes. Through various methods such as chromatin remodeling, signal transduction pathways, and microRNA sponging, they modulate hallmark cancer capabilities. Through the activation of stemness programs, epithelial-mesenchymal transition (EMT), and survival signaling, LncRNAs also control resistance to immunotherapy, chemotherapy, and targeted therapies. By clarifying their molecular roles further, we may be able to identify new treatment targets and ways to overcome resistance. This article aims to explore the interplay between lncRNAs, and GC. Specifically, the focus is on understanding how lncRNAs contribute to the etiology of GC and influence treatment resistance in patients with this disease.

Background

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disease in women of childbearing age, making it imperative to explore more biomarkers for PCOS. Furthermore, previous studies have reported that cyclin dependent kinase inhibitor 1 C (CDKN1C) might be associated with PCOS progression. However, the molecular mechanism of CDKN1C involved in PCOS is poorly defined.

Methods

CDKN1C and Yin-Yang-1 (YY1) expression levels were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot assay. Cell viability, proliferation, cell cycle progression, and cell apoptosis were analyzed using 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT), 5-ethynyl-2’-deoxyuridine (EdU), and flow cytometry assays. Caspase 3 activity was examined using a commercial kit. Binding between YY1 and CDKN1C promoter was predicted by JASPAR and verified using Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays.

Results

CDKN1C and YY1 were highly expressed in PCOS granulosa cells (GCs). Furthermore, CDKN1C silencing could promote cell proliferation and cell cycle process and repress cell apoptosis in human ovarian granulosa cell line KGN cells. For mechanistic analysis, YY1 is directly bound to the promoter of CDKN1C and transcriptional-regulated CDKN1C expression.

Conclusion

YY1-activated CDKN1C might block KGN cell proliferation and induce cell apoptosis, providing a possible therapeutic target for PCOS treatment.