Search

Background: While T cell-activating immunotherapies against recurrent head and neck squamous cell carcinoma (HNSCC) have shown impressive results in clinical trials, they are often ineffective in the majority of patients. NK cells are potential targets for immunotherapeutic intervention; however, the setback in monalizumab-based therapy in HNSCC highlights the need for an alternative treatment to enhance their antitumor activity., Methods: Single-cell RNA sequencing (scRNA-seq) and TCGA HNSCC datasets were used to identify key molecular alterations in NK cells. Representative HPV-positive ( +) and HPV-negative ( -) HNSCC cell lines and orthotopic mouse models were used to validate the bioinformatic findings. Changes in immune cells were examined by flow cytometry and immunofluorescence., Results: Through integration of scRNA-seq data with TCGA data, we found that the impact of IL6/IL6R and CCL2/CCR2 signaling pathways on evasion of immune attack by NK cells is more pronounced in the HPV - HNSCC cohort compared to the HPV + HNSCC cohort. In orthotopic mouse models, blocking IL6 with a neutralizing antibody suppressed HPV - but not HPV + tumors, which was accompanied by increased tumor infiltration and proliferation of CD161 + NK cells. Notably, combining the CCR2 chemokine receptor antagonist RS504393 with IL6 blockade resulted in a more pronounced antitumor effect that was associated with more activated intratumoral NK cells in HPV - HNSCC compared to either agent alone., Conclusions: These findings demonstrate that dual blockade of IL6 and CCR2 pathways effectively enhances the antitumor activity of NK cells in HPV-negative HNSCC, providing a novel strategy for treating this type of cancer., (© 2024. The Author(s).)

Background: Targeting mitochondrial oncoproteins presents a new concept in the development of effective cancer therapeutics. ATAD3A is a nuclear-encoded mitochondrial enzyme contributing to mitochondrial dynamics, cholesterol metabolism, and signal transduction. However, its impact and underlying regulatory mechanisms in cancers remain ill-defined., Methods: We used head and neck squamous cell carcinoma (HNSCC) as a research platform and achieved gene depletion by lentiviral shRNA and CRISPR/Cas9. Molecular alterations were examined by RNA-sequencing, phospho-kinase profiling, Western blotting, RT-qPCR, immunohistochemistry, and immunoprecipitation. Cancer cell growth was assessed by MTT, colony formation, soft agar, and 3D cultures. The therapeutic efficacy in tumor development was evaluated in orthotopic tongue tumor NSG mice., Results: ATAD3A is highly expressed in HNSCC tissues and cell lines. Loss of ATAD3A expression suppresses HNSCC cell growth and elicits tumor regression in orthotopic tumor-bearing mice, whereas gain of ATAD3A expression produces the opposite effects. From a mechanistic perspective, the tumor suppression induced by the overexpression of the Walker A dead mutant of ATAD3A (K358) produces a potent dominant-negative effect due to defective ATP-binding. Moreover, ATAD3A binds to ERK1/2 in the mitochondria of HNSCC cells in the presence of VDAC1, and this interaction is essential for the activation of mitochondrial ERK1/2 signaling. Most importantly, the ATAD3A-ERK1/2 signaling axis drives HNSCC development in a RAS-independent fashion and, thus, tumor suppression is more effectively achieved when ATAD3A knockout is combined with RAS inhibitor treatment., Conclusions: These findings highlight the novel function of ATAD3A in regulating mitochondrial ERK1/2 activation that favors HNSCC development. Combined targeting of ATAD3A and RAS signaling may potentiate anticancer activity for HNSCC therapeutics., (© 2022. The Author(s).)

Objective: The majority of sequence variants identified by Genome-wide association studies (GWASs) fall outside of the protein-coding regions. Unlike coding variants, it is challenging to connect these noncoding variants to the pathophysiology of complex diseases/traits due to the lack of functional annotations in the non-coding regions. To overcome this, by leveraging the rich collection of genomic and epigenomic profiles, we have developed DIVAN, or Disease/trait-specific Variant ANnotation, which enables the assignment of a measurement (D-score) for each base of the human genome in a disease/trait-specific manner. To facilitate the utilization of DIVAN, we pre-computed D-scores for every base of the human genome (hg19) for 45 different diseases/traits., Results: In this work, we present a detailed protocol on how to utilize DIVAN software toolkit to retrieve D-scores either by variant identifiers or by genomic regions for a disease/trait of interest. We also demonstrate the utilities of the D-scores using real data examples. We believe that the pre-computed D-scores for 45 diseases/traits is a useful resource to follow up on the discoveries made by GWASs, and the DIVAN software toolkit provides a convenient way to access this resource. DIVAN is freely available at https://sites.google.com/site/emorydivan/software .

Background: Nucleosome plays a role in transcriptional regulation through occluding the binding of proteins to DNA sites. Nucleosome occupancy varies among different cell types. Identification of such variation will help to understand regulation mechanism. The previous researches focused on the methods for two-sample comparison. However, a multiple-sample comparison (n ≥ 3) is necessary, especially in studying development and cancer. METHODS: Here, we proposed a Chi-squared test-based approach, named as Dimnp, to identify differential nucleosome regions (DNRs) in multiple samples. Dimnp is designed for sequenced reads data and includes the modules of both calling nucleosome occupancy and identifying DNRs., Results: We validated Dimnp on dataset of the mutant strains in which the modifiable histone residues are mutated into alanine in Saccharomyces cerevisiae. Dimnp shows a good capacity (area under the curve > 0.87) compared with the manually identified DNRs. Just by one time, Dimnp is able to identify all the DNRs identified by two-sample method Danpos. Under a deviation of 40 bp, the matched DNRs are above 60% between Dimnp and Danpos. With Dimnp, we found that promoters and telomeres are highly dynamic upon mutating the modifiable histone residues., Conclusions: We developed a tool of identifying the DNRs in multiple samples and cell types. The tool can be applied in studying nucleosome variation in gradual change in development and cancer.

Background: The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level., Results: The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists., Conclusions: The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.

Background: Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method., Results: Here we introduce HPeak, a Hidden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage., Conclusions: Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.

Several studies have been conducted to assess the influence of genetic variation on genome-wide gene expression profiles measured by the microarray technologies. Due to substantial noise in microarray-based experiments, it has long been recognized that proper normalization is a crucial step to ensure sensitive and reliable downstream analyses. This is especially true when large number of samples were collected and analyzed. In this study, we investigated the impact of different normalization strategies on genome wide linkage analyses, in particular, the estimation of heritability of gene expression traits. We used the Genetics Analysis Workshop 15 Problem 1 data. We found that there are significant differences in the estimated number of genes showing heritability when different normalization strategies were used. RMA (robust multiarray average) and dChip identify 45% and 13% more genes showing heritability than MAS 5.0, respectively. Our study also reveals that a large number of genes show strong "family effect" in their expression levels but no significant heritability. Analysis of their annotation indicates different types of genes were enriched in this group compared to genes showing strong heritability.

Background: There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life., Results: Three groups of topological properties were used in this study: network indices, degree distribution measures and motif profile measure. All of which are higher-level topological properties except for the marginal degree distribution. Metabolic networks in Archaeal species are found to be different from those in S. cerevisiae and the six Bacterial species in almost all measured higher-level topological properties. Our findings also indicate that the metabolic network in Archaeal species is similar to the exponential random network., Conclusion: If these metabolic network properties of the organisms studied can be extended to other species in their respective domains (which is likely), then the design principle(s) of Archaea are fundamentally different from those of Bacteria and Eukaryote. Furthermore, the functional mechanisms of Archaeal metabolic networks revealed in this study differentiate significantly from those of Bacterial and Eukaryotic organisms, which warrant further investigation.