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Journal articles on the topic 'Genetic characterization, proteomics'
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Yihunie, Fanuel Bizuayehu, Mequanint Addisu Belete, Gizachew Fentahun, Solomon Getachew, and Teshager Dubie. "Diagnostic and Therapeutic Application of Proteomics in Infectious Disease." Advances in Cell and Gene Therapy 2023 (August 24, 2023): 1–6. http://dx.doi.org/10.1155/2023/5510791.
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The study of an organism’s genome, often known as “genomics,” has advanced quickly, producing a wealth of publicly accessible genetic data. Despite how valuable the genome is; proteins essentially control most aspects of cell function. Proteomics, or the comprehensive study of proteins, has emerged as an important technology for disease characterization, diagnosis, prognosis, drug development, and therapy. Proteomics technologies are now used to support the diagnosis and treatment of both infectious and noninfectious diseases. Nevertheless, it is more difficult to describe a proteomic profile since a single gene product may result in a number of unique proteins, and proteins have a wider range of chemical configurations. The proteome profiles of a particular organism, tissue, or cell are impacted by a variety of environmental factors, including those triggered by infectious agents. This review intends to highlight the applications of proteomics in the study of disease diagnosis and treatment. In this review, the different technologies used in proteomics studies, like two-dimensional gel electrophoresis, mass spectrometry, and protein microarray as well as biomarker discovery and drug target identification using proteomics, have also been focused on.
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Van Damme, Petra, Joel Vandekerckhove, and Kris Gevaert. "Disentanglement of protease substrate repertoires." Biological Chemistry 389, no. 4 (April 1, 2008): 371–81. http://dx.doi.org/10.1515/bc.2008.043.
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Abstract Identification of protease substrates and detailed characterization of processed sites are essential for understanding the biological function of proteases. Because of inherent complexity reasons, this however remains a formidable analytical challenge, illustrated by the fact that the majority of the more than 500 human proteases are uncharacterized to date. Recently, in addition to conventional genetic and biochemical approaches, diverse quantitative peptide-centric proteomics approaches, some of which selectively recover N-terminal peptides, have emerged. These latter proteomic technologies in particular allow the identification of natural protease substrates and delineation of cleavage sites in a complex, natural background of thousands of different proteins. We here review current biochemical, genetic and proteomic methods for global analysis of substrates of proteases and discuss selected applications.
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Agregán, Rubén, Noemí Echegaray, María López-Pedrouso, Radwan Kharabsheh, Daniel Franco, and José M. Lorenzo. "Proteomic Advances in Milk and Dairy Products." Molecules 26, no. 13 (June 23, 2021): 3832. http://dx.doi.org/10.3390/molecules26133832.
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Proteomics is a new area of study that in recent decades has provided great advances in the field of medicine. However, its enormous potential for the study of proteomes makes it also applicable to other areas of science. Milk is a highly heterogeneous and complex fluid, where there are numerous genetic variants and isoforms with post-translational modifications (PTMs). Due to the vast number of proteins and peptides existing in its matrix, proteomics is presented as a powerful tool for the characterization of milk samples and their products. The technology developed to date for the separation and characterization of the milk proteome, such as two-dimensional gel electrophoresis (2DE) technology and especially mass spectrometry (MS) have allowed an exhaustive characterization of the proteins and peptides present in milk and dairy products with enormous applications in the industry for the control of fundamental parameters, such as microbiological safety, the guarantee of authenticity, or the control of the transformations carried out, aimed to increase the quality of the final product.
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Chantada-Vázquez, Maria del Pilar, Susana B. Bravo, Sofía Barbosa-Gouveia, José V. Alvarez, and María L. Couce. "Proteomics in Inherited Metabolic Disorders." International Journal of Molecular Sciences 23, no. 23 (November 25, 2022): 14744. http://dx.doi.org/10.3390/ijms232314744.
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Inherited metabolic disorders (IMD) are rare medical conditions caused by genetic defects that interfere with the body’s metabolism. The clinical phenotype is highly variable and can present at any age, although it more often manifests in childhood. The number of treatable IMDs has increased in recent years, making early diagnosis and a better understanding of the natural history of the disease more important than ever. In this review, we discuss the main challenges faced in applying proteomics to the study of IMDs, and the key advances achieved in this field using tandem mass spectrometry (MS/MS). This technology enables the analysis of large numbers of proteins in different body fluids (serum, plasma, urine, saliva, tears) with a single analysis of each sample, and can even be applied to dried samples. MS/MS has thus emerged as the tool of choice for proteome characterization and has provided new insights into many diseases and biological systems. In the last 10 years, sequential window acquisition of all theoretical fragmentation spectra mass spectrometry (SWATH-MS) has emerged as an accurate, high-resolution technique for the identification and quantification of proteins differentially expressed between healthy controls and IMD patients. Proteomics is a particularly promising approach to help obtain more information on rare genetic diseases, including identification of biomarkers to aid early diagnosis and better understanding of the underlying pathophysiology to guide the development of new therapies. Here, we summarize new and emerging proteomic technologies and discuss current uses and limitations of this approach to identify and quantify proteins. Moreover, we describe the use of proteomics to identify the mechanisms regulating complex IMD phenotypes; an area of research essential to better understand these rare disorders and many other human diseases.
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Arauz-Garofalo, Gianluca, Meritxell Jodar, Mar Vilanova, Alberto de la Iglesia Rodriguez, Judit Castillo, Ada Soler-Ventura, Rafael Oliva, Marta Vilaseca, and Marina Gay. "Protamine Characterization by Top-Down Proteomics: Boosting Proteoform Identification with DBSCAN." Proteomes 9, no. 2 (April 30, 2021): 21. http://dx.doi.org/10.3390/proteomes9020021.
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Protamines replace histones as the main nuclear protein in the sperm cells of many species and play a crucial role in compacting the paternal genome. Human spermatozoa contain protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or the P1/P2 ratio may be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteoforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down proteomics permits a more in-depth analysis of protamine PTMs and proteoforms, thereby opening up new perspectives to unravel their impact on male fertility. We report on the analysis of two normozoospermic semen samples by top-down proteomics. We discuss the difficulties encountered with the data analysis and propose solutions as this step is one of the current bottlenecks in top-down proteomics with the bioinformatics tools currently available. Our strategy for the data analysis combines two software packages, ProSight PD (PS) and TopPIC suite (TP), with a clustering algorithm to decipher protamine proteoforms. We identified up to 32 protamine proteoforms at different levels of characterization. This in-depth analysis of the protamine proteoform landscape of normozoospermic individuals represents the first step towards the future study of sperm pathological conditions opening up the potential personalized diagnosis of male infertility.
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Sun, Claire, Paul Daniel, Nicole Chew, Hui Shi, Melissa Loi, Sarah Parackal, Mateusz Koptyra, et al. "BIOL-01. GENERATION AND MULTI-OMICS CHARACTERIZATION OF 203 PEDIATRIC CNS TUMOUR MODELS REVEALS NEW THERAPEUTIC VULNERABILITIES." Neuro-Oncology 25, Supplement_1 (June 1, 2023): i5—i6. http://dx.doi.org/10.1093/neuonc/noad073.020.
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Abstract Pediatric Central Nervous System (CNS) tumors are the leading cause of cancer-related death among children. Identifying new targeted therapies necessitates the use of pediatric cancer models that faithfully recapitulate the patient’s disease. However, the generation and characterization of pediatric cancer models has significantly lagged adult cancers, underscoring the urgent need to develop and characterize pediatric CNS models of disease. Herein, we establish a single-site collection of 233 CNS tumour cell lines, representing 14 distinct brain childhood tumor types. We subjected >200 cell lines to multi-omics analyses (DNA-sequencing, RNA-sequencing, DNA methylation, proteomics, phospho-proteomics), and in parallel performed pharmacological and genetic CRISPR-Cas9 loss of function screens to identify pediatric-specific treatment opportunities and biomarkers. Our work provides insight into specific pathway vulnerabilities in molecularly defined pediatric tumor classes and uncovers biomarker-linked therapeutic opportunities of clinical relevance. Cell line data and resources are provided in an open access portal (vicpcc.org.au/dashboard).
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Di Narzo, Antonio F., Shannon E. Telesco, Carrie Brodmerkel, Carmen Argmann, Lauren A. Peters, Katherine Li, Brian Kidd, et al. "High-Throughput Characterization of Blood Serum Proteomics of IBD Patients with Respect to Aging and Genetic Factors." PLOS Genetics 13, no. 1 (January 27, 2017): e1006565. http://dx.doi.org/10.1371/journal.pgen.1006565.
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Gajadhar, Aaron S., Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, and Serafim Batzoglou. "Abstract 6348: A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6348. http://dx.doi.org/10.1158/1538-7445.am2022-6348.
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Abstract Comprehensive assessment of the flow of genetic information through multi-omic data integration can reveal the molecular consequences of genetic variation underlying human disease. Next generation sequencing (NGS) is used to identify genetic variants and characterize gene function (e.g. transcriptome and epigenome), while mass spectrometry is used to assess the proteome through characterization of protein abundances, modifications, and interactions. A new plasma profiling platform, the Proteograph࣪ Product Suite, leverages multiple nanoparticles with distinct physiochemical properties to enable deep plasma proteome analyses at scale. Here, we present a cloud-based, data analysis software platform called Proteograph Analysis Suite (PAS) for proteogenomic data analyses through the integration of proteomics data derived from the Proteograph with genomic variant information derived from NGS experiments. PAS features include an experiment data management system, analysis protocols, an analysis setup wizard, and tools for reviewing and visualizing results. PAS can support both Data Independent Analysis (DIA) and Data Dependent Analysis (DDA) proteomics workflows and is compatible with variant call format (vcf) files from NGS workflows to enable personalized database searches. To assess quality of the resulting data PAS includes various metrics like peptide/protein group intensity, protein sequence coverage, relative protein abundance distribution, peptide and protein group counts. Visualizations including principal component analysis, hierarchical clustering, and heatmaps allow intuitive identification of experimental trends. To enable biological insights, differential expression analyses results are reported with interactive visualizations such as volcano plots, protein interaction maps, and protein-set enrichment. From data to insight, PAS provides an easy-to-use and efficient suite of functionality to enable proteogenomic data analysis. Integration of proteomics and genomics data require a variety of tools, many of which require command-line interfaces and operating system-specific requirements that can act as a barrier for researchers to adapt new data analysis tools. Here, we demonstrate the utility of PAS by analyzing samples from the Proteograph NSCLC plasma dataset1. PAS can analyze VCF files generated from NGS pipelines in combination with mass spec data to identify peptide variants using personalized libraries. Using the cloud-based architecture computational tasks are distributed for rapid analysis. The integrated proteogenomics viewers allow variant IDs to be interpreted in the context of genomic coordinates, protein sequence, functional domains and features. Together, these results show the utility of PAS for seamless and fast proteomic data analysis. Reference: 1 Blume, J. E. et al. Nature Communications, 2020 Citation Format: Aaron S. Gajadhar, Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, Serafim Batzoglou. A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6348.
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Peck Justice, Sarah A., Monica P. Barron, Guihong D. Qi, H. R. Sagara Wijeratne, José F. Victorino, Ed R. Simpson, Jonah Z. Vilseck, Aruna B. Wijeratne, and Amber L. Mosley. "Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome." Journal of Biological Chemistry 295, no. 48 (September 2, 2020): 16219–38. http://dx.doi.org/10.1074/jbc.ra120.014576.
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Temperature-sensitive (TS) missense mutants have been foundational for characterization of essential gene function. However, an unbiased approach for analysis of biochemical and biophysical changes in TS missense mutants within the context of their functional proteomes is lacking. We applied MS-based thermal proteome profiling (TPP) to investigate the proteome-wide effects of missense mutations in an application that we refer to as mutant thermal proteome profiling (mTPP). This study characterized global impacts of temperature sensitivity–inducing missense mutations in two different subunits of the 26S proteasome. The majority of alterations identified by RNA-Seq and global proteomics were similar between the mutants, which could suggest that a similar functional disruption is occurring in both missense variants. Results from mTPP, however, provide unique insights into the mechanisms that contribute to the TS phenotype in each mutant, revealing distinct changes that were not obtained using only steady-state transcriptome and proteome analyses. Computationally, multisite λ-dynamics simulations add clear support for mTPP experimental findings. This work shows that mTPP is a precise approach to measure changes in missense mutant–containing proteomes without the requirement for large amounts of starting material, specific antibodies against proteins of interest, and/or genetic manipulation of the biological system. Although experiments were performed under permissive conditions, mTPP provided insights into the underlying protein stability changes that cause dramatic cellular phenotypes observed at nonpermissive temperatures. Overall, mTPP provides unique mechanistic insights into missense mutation dysfunction and connection of genotype to phenotype in a rapid, nonbiased fashion.
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Sudaric, Aleksandra, Marija Vrataric, Snezana Mladenovic-Drinic, and Maja Matosa. "Biotechnology in soybean breeding." Genetika 42, no. 1 (2010): 91–102. http://dx.doi.org/10.2298/gensr1001091s.
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Biotechnology can be defined broadly as a set of tools that allows scientists to genetically characterize or improve living organisms. Several emerging technologies, such as molecular characterization and genetic transformation, are already being used extensively for the purpose of plant improvement. Other emerging sciences, including genomics and proteomics, are also starting to impact plant improvement. Tools provided by biotechnology will not replace classical breeding methods, but rather will help provide new discoveries and contribute to improved nutritional value and yield enhancement through greater resistance to disease, herbicides and abiotic factors. In soybeans, biotechnology has and will continue to play a valuable role in public and private soybean breeding programs. Based on the availability and combination of conventional and molecular technologies, a substantial increase in the rate of genetic gain for economically important soybean traits can be predicted in the next decade. In this paper, a short review of technologies for molecular markers analysis in soybean is given as well as achievements in the area of genetic transformation in soybean.
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Milton, Ali, Dennis Muhanguzi, Allan Male, Ali Kajubi, Stephen Buah, Jerome Kubiriba, and Robooni Tumuhimbise. "Analysis of Genetic Diversity of Banana Weevils (Cosmopolites sordidus) (Coleoptera: Curculionidae) Using Transcriptome-Derived Simple Sequence Repeat Markers." Journal of Economic Entomology 115, no. 2 (January 11, 2022): 637–46. http://dx.doi.org/10.1093/jee/toab213.
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Abstract The banana weevil, Cosmopolites sordidus (Germar) (Coleoptera: Curculionidae) is an economically important insect pest of bananas. It causes up to 100% yield losses and substantial lifespan reduction in bananas. Advances in genomics, proteomics, and sequencing technologies have provided powerful pathways to genotyping disastrous pests such as C. sordidus. However, such technologies are often not available to the majority of rural subtropical African banana growers and pest control managers. This study was therefore motivated by the need to create cheap and easily accessible C. sordidus genotyping methods that could be deployed by banana pest control managers to the benefit of C. sordidus control programs in the tropics where such advanced technologies are not readily accessible. We used an in-house C. sordidus transcriptome from the an-ongoing study from which we mined an array of simple sequence repeat (SSR) markers. Of these, six highly polymorphic transcriptome-derived SSR markers were used to successfully genotype within and among banana weevil population genetic diversity of 12 C. sordidus populations collected from four banana-growing agro-ecological zones (AEZs) in Uganda. The developed transcriptome-derived SSR markers can be used by researchers in population genetics for characterization of the C. sordidus and identification of new genes that are linked to traits of particular interest. The significant genetic diversity revealed in C. sordidus provides pertinent information for integrated pest management strategies.
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La Rosa, Sandra, Chiara Guglielmo, Alessandra Ocello, Concetto Sessa, Giuseppe Seminara, and Antonio Granata. "Dalla medicina reattiva alla medicina di precisione." Giornale di Clinica Nefrologica e Dialisi 33 (September 18, 2021): 112–19. http://dx.doi.org/10.33393/gcnd.2021.2316.
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In recent years, there has been increased awareness of a concept of medicine based on individual differences taking into consideration genetic variability, environment, characteristics of the microbiome and individual lifestyles. It makes use of genomics, transcriptomics, proteomics and metabolomics techniques, obtaining a large amount of information which enables a more precise characterization of the patient. This model expands to the principles of prediction, prevention, personalization and participation, including all medical specialties. In nephrology, the application of precision medicine could play a central role, thanks to the information available today in multiple fields: for example the impact of alterations in the intestinal microbiota on the progression of chronic renal failure, in polycystic disease, in diabetic nephropathy and in the personalized approach to the transition period before the beginning of hemodialysis therapy.
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Tucholski, Trisha, Wenxuan Cai, Zachery R. Gregorich, Elizabeth F. Bayne, Stanford D. Mitchell, Sean J. McIlwain, Willem J. de Lange, et al. "Distinct hypertrophic cardiomyopathy genotypes result in convergent sarcomeric proteoform profiles revealed by top-down proteomics." Proceedings of the National Academy of Sciences 117, no. 40 (September 23, 2020): 24691–700. http://dx.doi.org/10.1073/pnas.2006764117.
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Hypertrophic cardiomyopathy (HCM) is the most common heritable heart disease. Although the genetic cause of HCM has been linked to mutations in genes encoding sarcomeric proteins, the ability to predict clinical outcomes based on specific mutations in HCM patients is limited. Moreover, how mutations in different sarcomeric proteins can result in highly similar clinical phenotypes remains unknown. Posttranslational modifications (PTMs) and alternative splicing regulate the function of sarcomeric proteins; hence, it is critical to study HCM at the level of proteoforms to gain insights into the mechanisms underlying HCM. Herein, we employed high-resolution mass spectrometry–based top-down proteomics to comprehensively characterize sarcomeric proteoforms in septal myectomy tissues from HCM patients exhibiting severe outflow track obstruction (n = 16) compared to nonfailing donor hearts (n = 16). We observed a complex landscape of sarcomeric proteoforms arising from combinatorial PTMs, alternative splicing, and genetic variation in HCM. A coordinated decrease of phosphorylation in important myofilament and Z-disk proteins with a linear correlation suggests PTM cross-talk in the sarcomere and dysregulation of protein kinase A pathways in HCM. Strikingly, we discovered that the sarcomeric proteoform alterations in the myocardium of HCM patients undergoing septal myectomy were remarkably consistent, regardless of the underlying HCM-causing mutations. This study suggests that the manifestation of severe HCM coalesces at the proteoform level despite distinct genotype, which underscores the importance of molecular characterization of HCM phenotype and presents an opportunity to identify broad-spectrum treatments to mitigate the most severe manifestations of this genetically heterogenous disease.
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Lin, Caijin, Xi Jin, Ding Ma, Chao Chen, Xin Hu, Yi-Zhou Jiang, and Zhi-Ming Shao. "Abstract PO1-15-02: Comprehensive characterization of genetic interactions in breast cancer reveals therapeutic vulnerabilities." Cancer Research 84, no. 9_Supplement (May 2, 2024): PO1–15–02—PO1–15–02. http://dx.doi.org/10.1158/1538-7445.sabcs23-po1-15-02.
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Abstract Background: Genome-informed and genome-targeted precision treatment for breast cancer have achieved remarkable progress in improving clinical outcomes for patients with specific genetic alterations. However, treatment efficacy is compromised by the current practice of basing treatment decision-making solely on single driver alterations, without considering the role of genetic interactions. Consequently, it is of great necessity to conduct systematic investigations to determine the clinical relevance of genetic interactions. Methods: We established a large-scale multi-omics cohort (N=873) and a real-world clinical sequencing cohort (N=4,421) representing the Asian breast cancer population. Detailed treatment records were collected. We then investigated the prognostic and predictive effects of genetic interactions based on multivariate Cox proportional hazards model and logistic regression model. To validate our findings, we utilized patient-derived organoids and tumor fragments to confirm the associations observed between genetic interactions and drug response. Results: Through integrated analysis of genomics, transcriptomics, proteomics, and metabolomics, we constructed a network comprising 54 co-occurring events and 38 mutually exclusive events, elucidating their association with dysregulated biological processes. External validations were performed in TCGA-BRCA, MSK-IMPACT, METABRIC, and AACR-GENIE datasets, respectively. Furthermore, we systematically revealed the prognostic effects of genetic interactions across distinct clinical subtypes. In triple-negative breast cancer, we found that the co-occurrence of PIK3CAmut-FOXA1mut was associated with unfavorable distant metastasis-free survival while TP53mut-MYBamp and TP53mut-CCNE1amp were associated with decreased overall survival. Additionally, we characterized the genetic interactions that impact the clinical outcomes of patients undergoing specific treatments in the neoadjuvant, adjuvant, and advanced settings. Notably, we identified associations such as TP53mut-AURKAamp with tamoxifen resistance, ERBB2amp-PAK1amp with resistance to trastuzumab-pertuzumab combinations, germline BRCA1mut-MYCamp with sensitivity to PARP1 inhibitors, and TP53mut-MYBamp with immunotherapy resistance. Conclusion: Overall, the consideration of genetic interactions may enhance our understanding of the heterogeneity in treatment response and complement ongoing efforts in precision oncology. Our study suggests that decision-making regarding genome-informed and genome-targeted treatment should extend beyond the scope of single driver alterations. Citation Format: Caijin Lin, Xi Jin, Ding Ma, Chao Chen, Xin Hu, Yi-Zhou Jiang, Zhi-Ming Shao. Comprehensive characterization of genetic interactions in breast cancer reveals therapeutic vulnerabilities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-02.
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Ramos-Lopez, Omar. "Genotype-based precision nutrition strategies for the prediction and clinical management of type 2 diabetes mellitus." World Journal of Diabetes 15, no. 2 (February 15, 2024): 142–53. http://dx.doi.org/10.4239/wjd.v15.i2.142.
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Globally, type 2 diabetes mellitus (T2DM) is one of the most common metabolic disorders. T2DM physiopathology is influenced by complex interrelationships between genetic, metabolic and lifestyle factors (including diet), which differ between populations and geographic regions. In fact, excessive consumptions of high fat/high sugar foods generally increase the risk of developing T2DM, whereas habitual intakes of plant-based healthy diets usually exert a protective effect. Moreover, genomic studies have allowed the characterization of sequence DNA variants across the human genome, some of which may affect gene expression and protein functions relevant for glucose homeostasis. This comprehensive literature review covers the impact of gene-diet interactions on T2DM susceptibility and disease progression, some of which have demonstrated a value as biomarkers of personal responses to certain nutritional interventions. Also, novel genotype-based dietary strategies have been developed for improving T2DM control in comparison to general lifestyle recommendations. Furthermore, progresses in other omics areas (epigenomics, metagenomics, proteomics, and metabolomics) are improving current understanding of genetic insights in T2DM clinical outcomes. Although more investigation is still needed, the analysis of the genetic make-up may help to decipher new paradigms in the pathophysiology of T2DM as well as offer further opportunities to personalize the screening, prevention, diagnosis, management, and prognosis of T2DM through precision nutrition.
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PAPPAS (Φ. ΠΑΠΠΑΣ), F., and M. STEFANIDOU (Μ. ΣΤΕΦΑΝΙΔΟΥ). "Genetically modified food." Journal of the Hellenic Veterinary Medical Society 57, no. 3 (November 29, 2017): 231. http://dx.doi.org/10.12681/jhvms.15047.
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International consensus has been reached on the principles regarding evaluation of the food safety of genetically modified plants. The concept of substantial equivalence has been developed as part of a safety evaluation framework, based on the idea that existing foods can serve as a basis for comparing the properties of genetically modified foods with the appropriate counterpart. Substantial equivalence is a starting point in the safety evaluation, rather than an endpoint of the assessment. The development and validation of new profiling methods, such as DNA microarray technology, proteomics and metabonomics for the identification and characterization of unintended effects, which may occur as a result of the genetic modification, is recommended. The assessment of the allergenicity of newly inserted proteins and of marker genes is discussed. Also, the post-marketing surveillance of the foods derived from genetically modified crops is imperative.
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Chu, Bizhu, An He, Yeteng Tian, Wan He, Peizhong Chen, Jintao Hu, Ruilian Xu, et al. "Photoaffinity-engineered protein scaffold for systematically exploring native phosphotyrosine signaling complexes in tumor samples." Proceedings of the National Academy of Sciences 115, no. 38 (September 6, 2018): E8863—E8872. http://dx.doi.org/10.1073/pnas.1805633115.
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Phosphotyrosine (pTyr)-regulated protein complexes play critical roles in cancer signaling. The systematic characterization of these protein complexes in tumor samples remains a challenge due to their limited access and the transient nature of pTyr-mediated interactions. We developed a hybrid chemical proteomics approach, termed Photo-pTyr-scaffold, by engineering Src homology 2 (SH2) domains, which specifically bind pTyr proteins, with both trifunctional chemical probes and genetic mutations to overcome these challenges. Dynamic SH2 domain-scaffolding protein complexes were efficiently cross-linked under mild UV light, captured by biotin tag, and identified by mass spectrometry. This approach was successfully used to profile native pTyr protein complexes from breast cancer tissue samples on a proteome scale with high selectivity, achieving about 100 times higher sensitivity for detecting pTyr signaling proteins than that afforded by traditional immunohistochemical methods. Among more than 1,000 identified pTyr proteins, receptor tyrosine kinase PDGFRB expressed on cancer-associated fibroblasts was validated as an important intercellular signaling regulator with poor expression correlation to ERBB2, and blockade of PDGFRB signaling could efficiently suppress tumor growth. The Photo-pTyr-scaffold approach may become a generic tool for readily profiling dynamic pTyr signaling complexes in clinically relevant samples.
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Sharma, Vinay, Prateek Gupta, Kagolla Priscilla, SharanKumar SharanKumar, Bhagyashree Hangargi, Akash Veershetty, Devade Pandurang Ramrao, et al. "Metabolomics Intervention Towards Better Understanding of Plant Traits." Cells 10, no. 2 (February 7, 2021): 346. http://dx.doi.org/10.3390/cells10020346.
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The majority of the most economically important plant and crop species are enriched with the availability of high-quality reference genome sequences forming the basis of gene discovery which control the important biochemical pathways. The transcriptomics and proteomics resources have also been made available for many of these plant species that intensify the understanding at expression levels. However, still we lack integrated studies spanning genomics–transcriptomics–proteomics, connected to metabolomics, the most complicated phase in phenotype expression. Nevertheless, for the past few decades, emphasis has been more on metabolome which plays a crucial role in defining the phenotype (trait) during crop improvement. The emergence of modern high throughput metabolome analyzing platforms have accelerated the discovery of a wide variety of biochemical types of metabolites and new pathways, also helped in improving the understanding of known existing pathways. Pinpointing the causal gene(s) and elucidation of metabolic pathways are very important for development of improved lines with high precision in crop breeding. Along with other -omics sciences, metabolomics studies have helped in characterization and annotation of a new gene(s) function. Hereby, we summarize several areas in the field of crop development where metabolomics studies have made its remarkable impact. We also assess the recent research on metabolomics, together with other omics, contributing toward genetic engineering to target traits and key pathway(s).
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Bove, Riley, Tanuja Chitnis, Bruce AC Cree, Mar Tintoré, Yvonne Naegelin, Bernard MJ Uitdehaag, Ludwig Kappos, et al. "SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts." Multiple Sclerosis Journal 24, no. 11 (August 29, 2017): 1485–98. http://dx.doi.org/10.1177/1352458517726657.
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Background: There is a pressing need for robust longitudinal cohort studies in the modern treatment era of multiple sclerosis. Objective: Build a multiple sclerosis (MS) cohort repository to capture the variability of disability accumulation, as well as provide the depth of characterization (clinical, radiologic, genetic, biospecimens) required to adequately model and ultimately predict a patient’s course. Methods: Serially Unified Multicenter Multiple Sclerosis Investigation (SUMMIT) is an international multi-center, prospectively enrolled cohort with over a decade of comprehensive follow-up on more than 1000 patients from two large North American academic MS Centers (Brigham and Women’s Hospital (Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women’s Hospital (CLIMB; BWH)) and University of California, San Francisco (Expression/genomics, Proteomics, Imaging, and Clinical (EPIC))). It is bringing online more than 2500 patients from additional international MS Centers (Basel (Universitätsspital Basel (UHB)), VU University Medical Center MS Center Amsterdam (MSCA), Multiple Sclerosis Center of Catalonia-Vall d’Hebron Hospital (Barcelona clinically isolated syndrome (CIS) cohort), and American University of Beirut Medical Center (AUBMC-Multiple Sclerosis Interdisciplinary Research (AMIR)). Results and conclusion: We provide evidence for harmonization of two of the initial cohorts in terms of the characterization of demographics, disease, and treatment-related variables; demonstrate several proof-of-principle analyses examining genetic and radiologic predictors of disease progression; and discuss the steps involved in expanding SUMMIT into a repository accessible to the broader scientific community.
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Bizzarri, Nicolò, Camilla Nero, Francesca Sillano, Francesca Ciccarone, Marika D’Oria, Alfredo Cesario, Simona Maria Fragomeni, et al. "Building a Personalized Medicine Infrastructure for Gynecological Oncology Patients in a High-Volume Hospital." Journal of Personalized Medicine 12, no. 1 (December 21, 2021): 3. http://dx.doi.org/10.3390/jpm12010003.
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Gynecological cancers require complex intervention since patients have specific needs to be addressed. Centralization to high-volume centers improves the oncological outcomes of patients with gynecological cancers. Research in gynecological oncology is increasing thanks to modern technologies, from the comprehensive molecular characterization of tumors and individual pathophenotypes. Ongoing studies are focusing on personalizing therapies by integrating information across genomics, proteomics, and metabolomics with the genetic makeup and immune system of the patient. Hence, several challenges must be faced to provide holistic benefit to the patient. Personalized approaches should also recognize the unmet needs of each patient to successfully deliver the promise of personalized care, in a multidisciplinary effort. This may provide the greatest opportunity to improve patients’ outcomes. Starting from a narrative review on gynecological oncology patients’ needs, this article focuses on the experience of building a research and care infrastructure for personalized patient management.
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Migliozzi, Simona, Kyung-Hee Kim, Harim Koo, Jun-Hee Hong, Seung Min Park, Hyung Joon Kwon, Luciano Garofano, et al. "Abstract 4638: Integrated proteogenomic characterization of longitudinal glioblastoma." Cancer Research 84, no. 6_Supplement (March 22, 2024): 4638. http://dx.doi.org/10.1158/1538-7445.am2024-4638.
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Abstract Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor, with no curative treatment options and median patient survival time of almost one year. Despite multi-modal therapy including surgery, irradiation and chemotherapy, all patients experience tumor progression and recurrence. Our group proposed and validated the first single cell guided functional classification of primary GBM in four tumor-intrinsic cell states which informed clinical outcome and delivered therapeutic options. However, recurrent GBM remains therapeutically unresolved due in part to the diffusely invasive nature and in part to marked cellular heterogeneity of the tumor.The evolutionary trajectory of glioblastoma after therapy is a multifaceted biological process that extends beyond discrete genetic alterations alone. Here, we profiled by multi-omics platforms the largest dataset of matched primary and recurrent GBM including 123 longitudinal glioblastoma pairs, temporally separated by standard-of-care treatment. Genomics, proteomics, and phosphoproteomics all independently captured the loss of proliferative-progenitor characteristics and a significant upregulation of specialized neuronal and synaptic signaling programs in recurrent GBM. Proteomic and phosphoproteomic analyses revealed that the molecular transition from proliferative to neuronal state at recurrence is marked by coherent post-translational activation of the WNT/PCP signaling pathway and the BRAF protein kinase. Multi-omic analysis of Patient-Derived Xenograft (PDX) models mimicked the patterns of evolutionary trajectory, consisting of marked activation of neuronal signaling programs in recurrent patients. Inhibition of the BRAF kinase with small molecule inhibitors impaired both neuronal transition and migration capability of recurrent glioblastoma cells, which are the phenotypic hallmarks of glioblastoma progression after therapy. Accordingly, combinatorial treatment of temozolomide with BRAF inhibitor, vemurafenib, significantly prolonged the survival of mouse PDX models. This work provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance and highlights new therapeutic opportunities to effectively counter them in the clinic. Citation Format: Simona Migliozzi, Kyung-Hee Kim, Harim Koo, Jun-Hee Hong, Seung Min Park, Hyung Joon Kwon, Luciano Garofano, Young Taek Oh, Fulvio D'Angelo, Chan Il Kim, Anna-Luisa Di Stefano, Franck Bielle, Jinlong Yin, Marc Sanson, Do-Hyun Nam, Jason K. Sa, Anna Lasorella, Jong Bae Park, Antonio Iavarone. Integrated proteogenomic characterization of longitudinal glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4638.
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Li, Jinna, Bing Yu, Chunquan Ma, Hongli Li, Desheng Jiang, Jingdong Nan, Meng Xu, et al. "Functional Characterization of Sugar Beet M14 Antioxidant Enzymes in Plant Salt Stress Tolerance." Antioxidants 12, no. 1 (December 27, 2022): 57. http://dx.doi.org/10.3390/antiox12010057.
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Salt stress can cause cellular dehydration, which induces oxidative stress by increasing the production of reactive oxygen species (ROS) in plants. They may play signaling roles and cause structural damages to the cells. To overcome the negative impacts, the plant ROS scavenging system plays a vital role in maintaining the cellular redox homeostasis. The special sugar beet apomictic monosomic additional M14 line (BvM14) showed strong salt stress tolerance. Comparative proteomics revealed that six antioxidant enzymes (glycolate oxidase (GOX), peroxiredoxin (PrxR), thioredoxin (Trx), ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase3 (DHAR3)) in BvM14 were responsive to salt stress. In this work, the full-length cDNAs of genes encoding these enzymes in the redox system were cloned from the BvM14. Ectopic expression of the six genes reduced the oxidative damage of transgenic plants by regulating the contents of hydrogen peroxide (H2O2), malondialdehyde (MDA), ascorbic acid (AsA), and glutathione (GSH), and thus enhanced the tolerance of transgenic plants to salt stress. This work has charecterized the roles that the antioxidant enzymes play in the BvM14 response to salt stress and provided useful genetic resources for engineering and marker-based breeding of crops that are sensitive to salt stress.
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Yan, Yuting, Xinzhou Ge, Jian Sun, Lei Yu, Qunling Zhang, Ying Yu, Zhenyu Jia, et al. "Proteogenomic Features Define Novel Subtypes of Mantle Cell Lymphoma." Blood 142, Supplement 1 (November 28, 2023): 4368. http://dx.doi.org/10.1182/blood-2023-190838.
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Introductions Recent advancements in mass spectrometry-based proteomics have unveiled novel translational and post-translational aspects of tumor biology. The joint characterization of tumor proteomics with genomics and transcriptomics enables proteogenomic analysis, improving our understanding of the molecular mechanisms, identifying new proteome-specific markers associated with clinical outcomes, and discovering novel treatment approaches. However, proteogenomic features remain largely uncharted due to a scarcity of information on MCL proteome. Methods We conducted a proteogenomic profiling study with 4 normal and 27 MCL B cells derived from healthy donors and MCL patients, integrating whole-exon sequencing (WES), transcriptome sequencing and mass spectrometry-based proteomics with data-independent acquisition (DIA) mode. We assessed the relationship among genetic lesions, RNA and protein expression, and further classified MCL based on omics information using k-means clustering. Results To this end, we first identified 1107 differentially expressed proteins between MCL and normal B cells (FDR<0.05) (329 upregulated and 778 downregulated in MCLs). The up-regulated proteins are highly enriched for RNA splicing and DNA repair pathway while the down-regulated proteins are primarily involved in cytoskeleton and endosomal transport. When integrated with 33 recurrent genetic lesions we previously reported, we found that mutations in CCND1 and NOTCH1 had significant impact on protein expression. Key genes including ATM and BCL10 were differentially expressed between samples with and without NOTCH1 mutation. Next, we computed Pearson correlation coefficients between RNA and protein levels for each gene to define the relationships between them. RB1 (0.88), PML (0.81) and CDK2 (0.81) showed high correlation efficiency, however, the overall correlation between RNA and protein abundance was low (0.26), suggesting that proteome abundance profiles may provide unseen information not previously discovered from WES and transcriptome data. Last, we evaluated protein expression level and their associations with progress free survival and overall survival to identify proteins potentially influencing clinical outcomes. Our comprehensive analysis led us to identify 40 proteins that could play a crucial role in disease progression. To assess the prognostic value of the omics data, we performed multi-omics clustering by incorporating genomics, transcriptomics and proteomics. This enabled us to identify four distinct clusters, each characterized by key driver genetic lesions as well as RNA and protein signatures. Notably, patients in these four clusters exhibited significantly different median overall survival (C1 to C4: not reach, 28.9, 15.8 and 7.9 months; log-rank test, P<0.001). Importantly, the protein signature remained significant even when considering the MIPI stage as a covariate (P<0.001), suggesting its potential as an independent prognostic factor. We then conducted an in-depth protein feature analysis and found that cluster 1 exhibited a significant enrichment of genes involved in ubiquitin-mediated proteolysis pathway. Cluster 2 showed a remarkable upregulation in DNA replication and cell cycle pathways. C3 displayed a significant upregulation of genes associated with BCR pathway and oxidative phosphorylation. Cluster 4 had an upregulation of RNA polymerase, pyrimidine metabolism, and RNA splicing, along with a notable downregulation in the BCR pathway. Our cluster analysis not only stratified MCL patients but also uncovered critical cellular pathways that could serve as potential therapeutic targets. Conclusions In this study, we investigated proteome changes and identified protein signatures associated with overall survival in MCL. Our integrative analysis unveiled four clusters, each characterized with unique genetic features, distinct gene and protein signatures, and different clinical outcomes. This study emphasizes the significance of protein abundance data as a nonredundant layer of information in MCL biology. Moreover, we have provided a protein expression reference map for MCL, offering valuable insights for future research and clinical applications.
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Pino, James C., Camilo Posso, Setareh Sharzehi, Sara Gosline, Chelsea Hutchinson-Bunch, Elie Traer, Paul D. Piehowski, et al. "Abstract 1844: Proteomic characterization of decitabine resistance in acute myeloid leukemia reveals signaling pathway crosstalk dampens the effectiveness of combination therapy." Cancer Research 84, no. 6_Supplement (March 22, 2024): 1844. http://dx.doi.org/10.1158/1538-7445.am2024-1844.
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Abstract Acute myeloid leukemia (AML) is a deadly form of blood cancer primarily characterized by genetic abnormalities that guide treatment strategies. In recent years, several new therapies have emerged to target these genetic abnormalities such as gilteritinib or quizartinib, which target an internal tandem duplication (ITD) of the FLT3 gene. Additional drugs have been developed to target core cellular processes, such as venetoclax and the decitabine. Despite initial positive responses to these treatments, patients eventually relapse as they develop resistance to the individual drugs. One common approach to drug resistance is to add an additional drug to the treatment protocol. However, patients then develop further resistance to not just a single drug but multiple. As such, understanding the origins of both single and multi-drug resistance is crucial for improving treatment efficacy and clinical outcomes. Here, we present a collection of cell lines that have developed resistance to single, double, and triple drug combinations of gilteritinib, venetoclax, and decitabine. Through comprehensive global and phospho proteomics analyses under multiple conditions together with computational pathway interrogation, we unravel the underlying molecular mechanisms responsible for resistance. Leveraging our previous model of early and late drug resistance, we investigate the emergence of resistance to drug combinations involving gilteritinib alone, gilteritinib with venetoclax, gilteritinib with decitabine, and gilteritinib with venetoclax and decitabine. Through the characterization of molecular signatures for each drug combination, we identify molecular signals that are specific to combination treatments. Specifically, we find that the emergences of decitabine resistance alters the cellular pathways to effectively downregulate targets of venetoclax and gilteritinib, ultimately dampening the response to these drugs, regardless of co-administration. These findings provide a potential explanation for the limited success of combination treatments involving hypomethylating agents, as observed in recent clinical trials. Our results underscore the importance of monitoring proteome-level changes in single and combination drug treatments and shed light on the simple and intuitive reasons behind the lack of synergy often observed in combination therapies. In conclusion, we establish cell line models of resistance to various combinations of venetoclax, gilteritinib, and decitabine. By integrating global and phospo proteomic measurements, we gain a comprehensive understanding of the intricate molecular mechanisms underlying resistance. Citation Format: James C. Pino, Camilo Posso, Setareh Sharzehi, Sara Gosline, Chelsea Hutchinson-Bunch, Elie Traer, Paul D. Piehowski, Karin D. D. Rodland, Jeffrey W. Tyner, Tao Liu, Anupriya Agarwal. Proteomic characterization of decitabine resistance in acute myeloid leukemia reveals signaling pathway crosstalk dampens the effectiveness of combination therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1844.
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Abarrategui-Garrido, Cynthia, Rubén Martínez-Barricarte, Margarita López-Trascasa, Santiago Rodríguez de Córdoba, and Pilar Sánchez-Corral. "Characterization of complement factor H–related (CFHR) proteins in plasma reveals novel genetic variations of CFHR1 associated with atypical hemolytic uremic syndrome." Blood 114, no. 19 (November 5, 2009): 4261–71. http://dx.doi.org/10.1182/blood-2009-05-223834.
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Abstract The factor H–related protein family (CFHR) is a group of minor plasma proteins genetically and structurally related to complement factor H (fH). Notably, deficiency of CFHR1/CFHR3 associates with protection against age-related macular degeneration and with the presence of anti-fH autoantibodies in atypical hemolytic uremic syndrome (aHUS). We have developed a proteomics strategy to analyze the CFHR proteins in plasma samples from controls, patients with aHUS, and patients with type II membranoproliferative glomerulonephritis. Here, we report on the identification of persons carrying novel deficiencies of CFHR1, CFHR3, and CFHR1/CFHR4A, resulting from point mutations in CFHR1 and CFHR3 or from a rearrangement involving CFHR1 and CFHR4. Remarkably, patients with aHUS lacking CFHR1, but not those lacking CFHR3, present anti-fH autoantibodies, suggesting that generation of these antibodies is specifically related to CFHR1 deficiency. We also report the characterization of a novel CFHR1 polymorphism, resulting from a gene conversion event between CFH and CFHR1, which strongly associates with aHUS. The risk allotype CFHR1*B, with greater sequence similarity to fH, may compete with fH, decreasing protection of cellular surfaces against complement damage. In summary, our comprehensive analyses of the CFHR proteins have improved our understanding of these proteins and provided further insights into aHUS pathogenesis.
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Tesema, Zeleke, Mengistie Taye, and Desalegn Ayichew. "The role of phenotypic and genetic basis of livestock selection for climate change adaptation and mitigation: A review." Journal of Applied and Advanced Research 4, no. 2 (March 29, 2019): 66. http://dx.doi.org/10.21839/jaar.2019.v4i2.251.
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Livestock are not only suffering from climate change, but also contribute to climate change through the direct and indirect release of greenhouse gases (CH4, N2O and CO2). Characterization, identification and conservation of heat tolerant livestock breeds are basics for future challenging climate. Properties of the skin, hair, coat color, coat type, sweating, respiration capacity, tissue insulation, surface area relative to body weight, endocrinological profiles and metabolic heat production are important factors involved for heat tolerance. Selection based on these phenotypic characteristics is play indispensible for climate change adaptation and mitigation. Molecular information is used to know the candidate gene for heat tolerance, their action, specific function and location on chromosomes thereby important for modification of gene and selection of heat tolerant breed and feed efficient animals. Genomic information also used to identify genes that regulated during a stressful event can lead to the identification of animals that are genetically superior for coping with stress. Marker assisted selection and proteomics may also be valuable in selection for secondary traits linked to adaptation, such as the genes for high levels of blood urea and ruminal ammonia in certain genotypes, associated with adaptation to low-quality C4 grasses. Scientific research results demonstrated that heat tolerance is heritable trait and variable between/within livestock breeds, thereby variation and heritability of the trait opens the window for selection of heat tolerant animals. Therefore, the combined genomic selection using genome wide DNA markers that predict tolerance to heat stress and phenotypic selection could be accelerated breeding of highly productive and heat tolerant livestock breeds. Further research should be conducted on characterization, identification of indigenous breeds at molecular level and on identification of responsible genes/genomic regions associated with thermoregulation, feed and production efficiency in order to develop suitable adaptive and mitigation strategies to counter environmental stresses.
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Mahmood, Tahir, Shiguftah Khalid, Muhammad Abdullah, Zubair Ahmed, Muhammad Kausar Nawaz Shah, Abdul Ghafoor, and Xiongming Du. "Insights into Drought Stress Signaling in Plants and the Molecular Genetic Basis of Cotton Drought Tolerance." Cells 9, no. 1 (December 31, 2019): 105. http://dx.doi.org/10.3390/cells9010105.
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Drought stress restricts plant growth and development by altering metabolic activity and biological functions. However, plants have evolved several cellular and molecular mechanisms to overcome drought stress. Drought tolerance is a multiplex trait involving the activation of signaling mechanisms and differentially expressed molecular responses. Broadly, drought tolerance comprises two steps: stress sensing/signaling and activation of various parallel stress responses (including physiological, molecular, and biochemical mechanisms) in plants. At the cellular level, drought induces oxidative stress by overproduction of reactive oxygen species (ROS), ultimately causing the cell membrane to rupture and stimulating various stress signaling pathways (ROS, mitogen-activated-protein-kinase, Ca2+, and hormone-mediated signaling). Drought-induced transcription factors activation and abscisic acid concentration co-ordinate the stress signaling and responses in cotton. The key responses against drought stress, are root development, stomatal closure, photosynthesis, hormone production, and ROS scavenging. The genetic basis, quantitative trait loci and genes of cotton drought tolerance are presented as examples of genetic resources in plants. Sustainable genetic improvements could be achieved through functional genomic approaches and genome modification techniques such as the CRISPR/Cas9 system aid the characterization of genes, sorted out from stress-related candidate single nucleotide polymorphisms, quantitative trait loci, and genes. Exploration of the genetic basis for superior candidate genes linked to stress physiology can be facilitated by integrated functional genomic approaches. We propose a third-generation sequencing approach coupled with genome-wide studies and functional genomic tools, including a comparative sequenced data (transcriptomics, proteomics, and epigenomic) analysis, which offer a platform to identify and characterize novel genes. This will provide information for better understanding the complex stress cellular biology of plants.
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Cummings, Steven, Thomas Perls, and Evan Hadley. "Complementary and Integrated Studies of Longevity and Healthy Aging." Innovation in Aging 4, Supplement_1 (December 1, 2020): 851. http://dx.doi.org/10.1093/geroni/igaa057.3126.
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Abstract Five NIH-funded studies, the Long Life Family Study (LLFS, U19), the Longevity Consortium (LC, U19), Longevity Genomics (U24), and Protective Omics Profiles in Centenarians (UH2) work together to triangulate on mechanisms of extreme longevity and healthy aging with the ultimate goal of discovering predictors and targetable pathways. Linkage analyses by LLFS identified extremely strong genetic linkage peaks for cross-sectional as well as longitudinal trajectory rates-of-change phenotypes. Deep sequencing suggests these peaks are driven by rare, protective variants in selected pedigrees. In cross-species studies (UH2, LC), genomics, metabolomics and proteomics are used to exploit many-fold variances in natural life spans to discover protective mechanisms that explain some of these differences. Proteome analysis reveal several longevity-related proteins such as Cip1/p21, FOXO3, TOP2A, AKT1, RICTOR, INSR and SIRT6 harboring post translational modification sites that preferentially appear in short- versus long-lived species. The U24 effort developed a tool using genetically-mediated gene expression to prioritize genes for longevity translational efforts. We found that BLOC1S1 was associated with longevity and protection from atrial fibrillation and hearing loss without being associated with adverse events. This novel target is undergoing functional characterization. A proteomic assay (4,131 proteins, Somascan) annotated by genome-wide association study results in a total of 1,797 centenarians and 3,685 controls divided into independent discovery and replication sets, discovered significant and replicated over-expression (thus, pro-longevity) of BIRC2 and under-expression of APOB in carriers of the APOE ɛ-2 allele. A novel protein signature of rs2184061 (CDKN2a/CDKN2B in chromosome 9) was also associated with slower aging.
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Yubero, Dèlia, Daniel Natera-de Benito, Jordi Pijuan, Judith Armstrong, Loreto Martorell, Guerau Fernàndez, Joan Maynou, et al. "The Increasing Impact of Translational Research in the Molecular Diagnostics of Neuromuscular Diseases." International Journal of Molecular Sciences 22, no. 8 (April 20, 2021): 4274. http://dx.doi.org/10.3390/ijms22084274.
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The diagnosis of neuromuscular diseases (NMDs) has been progressively evolving from the grouping of clinical symptoms and signs towards the molecular definition. Optimal clinical, biochemical, electrophysiological, electrophysiological, and histopathological characterization is very helpful to achieve molecular diagnosis, which is essential for establishing prognosis, treatment and genetic counselling. Currently, the genetic approach includes both the gene-targeted analysis in specific clinically recognizable diseases, as well as genomic analysis based on next-generation sequencing, analyzing either the clinical exome/genome or the whole exome or genome. However, as of today, there are still many patients in whom the causative genetic variant cannot be definitely established and variants of uncertain significance are often found. In this review, we address these drawbacks by incorporating two additional biological omics approaches into the molecular diagnostic process of NMDs. First, functional genomics by introducing experimental cell and molecular biology to analyze and validate the variant for its biological effect in an in-house translational diagnostic program, and second, incorporating a multi-omics approach including RNA-seq, metabolomics, and proteomics in the molecular diagnosis of neuromuscular disease. Both translational diagnostics programs and omics are being implemented as part of the diagnostic process in academic centers and referral hospitals and, therefore, an increase in the proportion of neuromuscular patients with a molecular diagnosis is expected. This improvement in the process and diagnostic performance of patients will allow solving aspects of their health problems in a precise way and will allow them and their families to take a step forward in their lives.
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Halldorsson, Skarphedinn, Siri Fløgstad Svensson, Henriette Engen Berg, Denise Wolrab, Frode Rise, Alistair Wilkins, Steven Ray Wilson, Michal Holcapek, Kyrre Eeg Emblem, and Einar O. Vik-Mo. "OTEH-7. Molecular characterization of tumor stiffness in glioblastoma." Neuro-Oncology Advances 3, Supplement_2 (July 1, 2021): ii11—ii12. http://dx.doi.org/10.1093/noajnl/vdab070.046.
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Abstract Tumor heterogeneity is one of the hallmarks of glioblastoma multiforme (GBM). Morphology within a given GBM tumor can be extremely variable where some regions of the tumor have a soft, gel-like structure while other areas are dense and fibrous. Abnormal mechanical stress and tissue stiffening caused by cancer proliferation are believed to affect vascularity by compressing structurally weak blood vessels and restricting the supply of nutrients and oxygen to the tissue. These effects contribute to a hypoxic microenvironment that promotes disease progression and chemoresistance. The genetic and molecular mechanisms that govern tissue stiffness within GBM tumors, however, are largely unknown. Magnetic Resonance Elastography (MRE) is an emerging technique for quantifying tissue stiffness non-invasively. We have evaluated 10 GBM patients by MRE imaging obtained prior to surgical resection. During surgery, 2–7 stereotactically navigated biopsies were collected from locations within the tumor with varying degrees of measured stiffness. Biopsies were processed to extract RNA, proteins, polar metabolites and lipids. Biomolecules were analyzed on relevant -omics platforms (RNA sequencing, MS-proteomics and lipidomics, NMR of polar metabolites). Differential expression and gene set enrichment analysis of patient paired biopsies indicate an overall increase in macrophage infiltration and extracellular matrix re-organization associated with increased tumor stiffness. Among the most highly upregulated genes in stiff tumor tissue were lymphatic endothelial hyaluronic acid receptor 1 (LYVE-1) and macrophage receptor with collagenous structure (MARCO), both of which have been associated with immune cell infiltration and tissue stiffness. Our preliminary findings offer novel insights into tumor morphology in GBM that can be inferred from imaging prior to surgery. This can be used to identify tumor regions with high risk of progression and infiltration, thereby informing and guiding surgical strategy and may ultimately lead to novel treatment strategies.
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Faith, Dominick R., Margie Kinnersley, Diane M. Brooks, Dan Drecktrah, Laura S. Hall, Eric Luo, Andrew Santiago-Frangos, Jenny Wachter, D. Scott Samuels, and Patrick R. Secor. "Characterization and genomic analysis of the Lyme disease spirochete bacteriophage ϕBB-1." PLOS Pathogens 20, no. 4 (April 1, 2024): e1012122. http://dx.doi.org/10.1371/journal.ppat.1012122.
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Lyme disease is a tick-borne infection caused by the spirochete Borrelia (Borreliella) burgdorferi. Borrelia species have highly fragmented genomes composed of a linear chromosome and a constellation of linear and circular plasmids some of which are required throughout the enzootic cycle. Included in this plasmid repertoire by almost all Lyme disease spirochetes are the 32-kb circular plasmid cp32 prophages that are capable of lytic replication to produce infectious virions called ϕBB-1. While the B. burgdorferi genome contains evidence of horizontal transfer, the mechanisms of gene transfer between strains remain unclear. While we know that ϕBB-1 transduces cp32 and shuttle vector DNA during in vitro cultivation, the extent of ϕBB-1 DNA transfer is not clear. Herein, we use proteomics and long-read sequencing to further characterize ϕBB-1 virions. Our studies identified the cp32 pac region and revealed that ϕBB-1 packages linear cp32s via a headful mechanism with preferential packaging of plasmids containing the cp32 pac region. Additionally, we find ϕBB-1 packages fragments of the linear chromosome and full-length plasmids including lp54, cp26, and others. Furthermore, sequencing of ϕBB-1 packaged DNA allowed us to resolve the covalently closed hairpin telomeres for the linear B. burgdorferi chromosome and most linear plasmids in strain CA-11.2A. Collectively, our results shed light on the biology of the ubiquitous ϕBB-1 phage and further implicates ϕBB-1 in the generalized transduction of diverse genes and the maintenance of genetic diversity in Lyme disease spirochetes.
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Guzman, Norberto A., and Daniel E. Guzman. "Immunoaffinity Capillary Electrophoresis in the Era of Proteoforms, Liquid Biopsy and Preventive Medicine: A Potential Impact in the Diagnosis and Monitoring of Disease Progression." Biomolecules 11, no. 10 (October 1, 2021): 1443. http://dx.doi.org/10.3390/biom11101443.
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Over the years, multiple biomarkers have been used to aid in disease screening, diagnosis, prognosis, and response to therapy. As of late, protein biomarkers are gaining strength in their role for early disease diagnosis and prognosis in part due to the advancements in identification and characterization of a distinct functional pool of proteins known as proteoforms. Proteoforms are defined as all of the different molecular forms of a protein derived from a single gene caused by genetic variations, alternative spliced RNA transcripts and post-translational modifications. Monitoring the structural changes of each proteoform of a particular protein is essential to elucidate the complex molecular mechanisms that guide the course of disease. Clinical proteomics therefore holds the potential to offer further insight into disease pathology, progression, and prevention. Nevertheless, more technologically advanced diagnostic methods are needed to improve the reliability and clinical applicability of proteomics in preventive medicine. In this manuscript, we review the use of immunoaffinity capillary electrophoresis (IACE) as an emerging powerful diagnostic tool to isolate, separate, detect and characterize proteoform biomarkers obtained from liquid biopsy. IACE is an affinity capture-separation technology capable of isolating, concentrating and analyzing a wide range of biomarkers present in biological fluids. Isolation and concentration of target analytes is accomplished through binding to one or more biorecognition affinity ligands immobilized to a solid support, while separation and analysis are achieved by high-resolution capillary electrophoresis (CE) coupled to one or more detectors. IACE has the potential to generate rapid results with significant accuracy, leading to reliability and reproducibility in diagnosing and monitoring disease. Additionally, IACE has the capability of monitoring the efficacy of therapeutic agents by quantifying companion and complementary protein biomarkers. With advancements in telemedicine and artificial intelligence, the implementation of proteoform biomarker detection and analysis may significantly improve our capacity to identify medical conditions early and intervene in ways that improve health outcomes for individuals and populations.
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Roychowdhury, Rajib, Soumya Prakash Das, Amber Gupta, Parul Parihar, Kottakota Chandrasekhar, Umakanta Sarker, Ajay Kumar, Devade Pandurang Ramrao, and Chinta Sudhakar. "Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses." Genes 14, no. 6 (June 16, 2023): 1281. http://dx.doi.org/10.3390/genes14061281.
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The present day’s ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant’s innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the ‘omics’ toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant’s genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence ‘multi-omics’) integrated-omics approaches can decipher the plant’s abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop’s variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
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Ogden, Aaron J., Wardatou Boukari, Alba Nava, Natalia Lucinda, Garry Sunter, Wayne R. Curtis, Joshua N. Adkins, and Jane E. Polston. "Characterization of Local and Systemic Impact of Whitefly (Bemisia tabaci) Feeding and Whitefly-Transmitted Tomato Mottle Virus Infection on Tomato Leaves by Comprehensive Proteomics." International Journal of Molecular Sciences 21, no. 19 (September 30, 2020): 7241. http://dx.doi.org/10.3390/ijms21197241.
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Tomato mottle virus (ToMoV) is a single-stranded DNA (ssDNA) begomovirus transmitted to solanaceous crops by the whitefly species complex (Bemisia tabaci), causing stunted growth, leaf mottling, and reduced yield. Using a genetic repertoire of seven genes, ToMoV pathogenesis includes the manipulation of multiple plant biological processes to circumvent antiviral defenses. To further understand the effects of whitefly feeding and whitefly-transmitted ToMoV infection on tomato plants (Solanum lycopersicum ‘Florida Lanai’), we generated comprehensive protein profiles of leaves subjected to feeding by either viruliferous whiteflies harboring ToMoV, or non-viruliferous whiteflies, or a no-feeding control. The effects of whitefly feeding and ToMoV infection were measured both locally and systemically by sampling either a mature leaf directly from the site of clip-cage confined whitefly feeding, or from a newly formed leaf 10 days post feeding (dpf). At 3 dpf, tomato’s response to ToMoV included proteins associated with translation initiation and elongation as well as plasmodesmata dynamics. In contrast, systemic impacts of ToMoV on younger leaves 10 dpf were more pronounced and included a virus-specific change in plant proteins associated with mRNA maturation and export, RNA-dependent DNA methylation, and other antiviral plant processes. Our analysis supports previous findings and provides novel insight into tomato’s local and systemic response to whitefly feeding and ToMoV infection.
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Martins Rodrigues, Fernanda, Qingsong Gao, Kuan-lin Huang, Adam David Scott, Steven M. Foltz, Justin King, Mark A. Fiala, et al. "Characterization of Germline Variants in Multiple Myeloma." Blood 132, Supplement 1 (November 29, 2018): 4499. http://dx.doi.org/10.1182/blood-2018-99-118673.
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Abstract Multiple myeloma (MM) is an incurable hematological malignancy characterized by the clonal proliferation of malignant plasma cells in the bone marrow. Like other cancers, MM is a genetically complex and heterogeneous disease. One of its distinctive characteristics is that it is preceded by a pre-malignant condition known as monoclonal gammopathy of undetermined significance (MGUS), which then progresses to asymptomatic (smoldering) multiple myeloma (SMM) and, ultimately, to late-stage MM. Its progression through these stages is determined by a sequence of genomic aberrations, starting with germline events that predispose to the disease, followed by early initiating events and the later acquisition of mutations that contribute to disease progression. Although considerable progress has been made in the past 6 years in cataloguing somatic events underlying MM development and progression, little is known about its genetic predisposition. Therefore, large-scale germline genomic variant studies are urgently needed. Recently, our group has published the largest-scale pan-cancer study of >10K adult and >1K pediatric cases that revealed new insights on germline predisposition variants across 33 cancer types (853 pathogenic or likely pathogenic variants) (Huang et al., 2018). Here, we aim to apply a similar strategy to MM cases. The CoMMpass study, promoted by MMRF (Multiple Myeloma Research Foundation) is a longitudinal, prospective observational study involving the collection and analysis of sequencing and clinical data from >1K MM patients at diagnosis and relapse. We performed germline variant calling on 808 normal samples from this dataset using GenomeVIP (https://github.com/ding-lab/GenomeVIP), which integrates multiple tools: VarScan2 and Genome Analysis ToolKit (GATK) for the identification of single nucleotide variants (SNVs) and indels; and Pindel for indel prediction. Variants were limited to coding regions of full length transcripts obtained from Ensembl release 70 plus the additional two base pairs flanking each exon that cover splice donor/acceptor sites. SNVs were based on the union of raw GATK and VarScan calls. Indels were required to be called by at least two out of the three callers (GATK, Pindel, VarScan). Variant calls from all tools were merged, filtered (allelic depth ≥ 5 for the alternative allele; rare variants with allele frequency ≤ 0.01 in 1000 Genomes and ExAC), and annotated using Variant Effect Predictor (VEP), resulting in an average of 1,653 variants per sample. Further, we applied CharGer (Characterization of Germline Variants, https://github.com/ding-lab/CharGer) to classify the identified germline variants as pathogenic, likely pathogenic, and prioritized variants of unknown significance (VUS). CharGer is an automatic variant classification pipeline developed by our group which adopts ACMG-AMP guidelines specifically for rare variants in cancer. Here, we were able to classify a total of 635 germline variants as pathogenic and 150 as likely pathogenic, affecting 90% of samples. Among pathogenic variants, 28 were found in known cancer predisposition genes including BRCA1 and BRCA2 - which have been previously associated with MM risk - BRIP1, CHEK2, TP53, TERT, and PMS2. Ongoing analyses include: functional characterization of these variants, identifying genes with enriched pathogenic or likely pathogenic variants in our dataset; investigation of LOH and two-hit (biallelic) events; gene and protein expression analyses in carriers of pathogenic germline variants of the respective gene; scanning for rare, germline copy number variations (CNVs); and identification of variants in post-translational modification sites that may affect protein signaling. Additionally, we are currently working on improving our CharGer tool by integrating new tumor associated data, such as DNA-Seq, RNA-Seq, Methyl-Seq and MS proteomics data, to improve variant classification. The preliminary results and analysis strategies described here will allow for efficient and cost-effective discovery of genetic changes relevant to MM etiology. Ultimately, we hope this work will impact our overall understanding of the genetics underlying MM predisposition, allowing for the development of better prevention and early detection strategies. Disclosures Vij: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees.
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Pino, James C., Camilo Posso, Sunil K. Joshi, Michael Nestor, Jamie Moon, Joshua R. Hansen, Marina A. Gritsenko, et al. "Abstract 3172: Mapping the molecular landscape of acute myeloid leukemia enables prediction of drug response from proteogenomic data." Cancer Research 83, no. 7_Supplement (April 4, 2023): 3172. http://dx.doi.org/10.1158/1538-7445.am2023-3172.
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Abstract Acute myeloid leukemia (AML) is a deadly blood cancer that remains largely classified by genetic aberrations, which inform therapy stratification. However, therapeutic response cannot be predicted or explained by genetic abnormalities alone. The integration of multiple omics, consisting of genomic, transcriptomic, proteomic, and phosphoproteomic measurements, offers a holistic view to resolve the underlying pathophysiology of AML that influences response to therapy. In this work, we pair multi-omic characterization together with ex vivo drug sensitivity assays accrued with 145 small molecule inhibitors for 210 AML patient samples (Bottomly et al., Cancer Cell 2022). We showcase how the integration of these data can guide drug sensitivity exploration and prediction. We first expanded the dataset by generating matching comprehensive proteomics and phosphoproteomics data for the Beat AML samples and integrated these data using non-negative matrix factorization. This analysis identified four distinct proteogenomic subtypes of AML, each representing distinct clinical and biological features including differences in survival and biological pathway activation. We then sought distinct patterns of drug sensitivity across the subtypes of the patient cohort and found one pair of drugs, venetoclax and panobinostat, to be sensitive in complementary sets of patients, suggesting that they could be more effective in combination. Lastly, we further enhanced the proteogenomic subtypes by a building machine learning based model of distinct drug response that we then evaluated in vitro. Our results show that the four proteogenomic subtypes are independent yet complementary to existing mutational profiles, and can be used to improve drug treatment stratification. We tested the combination of panobinostat and venetoclax in patient samples and show that they are more effective in combination than as single agents. We then tested drug-specific machine learning models to predict drug response on AML cell lines that were in varying stages of resistance to the FLT3 inhibitor quizartinib. The models predicted a change across the proteogenomic landscape as quizartinib resistance evolves, resulting in a shift in drug sensitivities that we experimentally validated. This work represents a seminal effort in the integration of proteogenomic and ex vivo drug sensitivity datasets. In summary, we show how multi-omic characterization of AML maps a proteogenomic landscape that enables improved exploration of patient drug response and ultimately patient treatment. Citation Format: James C. Pino, Camilo Posso, Sunil K. Joshi, Michael Nestor, Jamie Moon, Joshua R. Hansen, Marina A. Gritsenko, Chelsea Hutchinson-Bunch, Karl K. Weitz, Kevin Watanabe-Smith, Jason E. McDermott, Brian J. Druker, Tao Liu, Jeffrey W. Tyner, Anupriya Agarwal, Elie Traer, Paul D. Piehowski, Cristina E. Tognon, Karin D. Rodland, Sara J. Gosline. Mapping the molecular landscape of acute myeloid leukemia enables prediction of drug response from proteogenomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3172.
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Avram, Oren, Aya Kigel, Anna Vaisman-Mentesh, Sharon Kligsberg, Shai Rosenstein, Yael Dror, Tal Pupko, and Yariv Wine. "PASA: Proteomic analysis of serum antibodies web server." PLOS Computational Biology 17, no. 1 (January 25, 2021): e1008607. http://dx.doi.org/10.1371/journal.pcbi.1008607.
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Motivation A comprehensive characterization of the humoral response towards a specific antigen requires quantification of the B-cell receptor repertoire by next-generation sequencing (BCR-Seq), as well as the analysis of serum antibodies against this antigen, using proteomics. The proteomic analysis is challenging since it necessitates the mapping of antigen-specific peptides to individual B-cell clones. Results The PASA web server provides a robust computational platform for the analysis and integration of data obtained from proteomics of serum antibodies. PASA maps peptides derived from antibodies raised against a specific antigen to corresponding antibody sequences. It then analyzes and integrates proteomics and BCR-Seq data, thus providing a comprehensive characterization of the humoral response. The PASA web server is freely available at https://pasa.tau.ac.il and open to all users without a login requirement.
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Mirza, Shama P., and Michael Olivier. "Methods and approaches for the comprehensive characterization and quantification of cellular proteomes using mass spectrometry." Physiological Genomics 33, no. 1 (March 2008): 3–11. http://dx.doi.org/10.1152/physiolgenomics.00292.2007.
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Proteomics has been proposed as one of the key technologies in the postgenomic era. So far, however, the comprehensive analysis of cellular proteomes has been a challenge because of the dynamic nature and complexity of the multitude of proteins in cells and tissues. Various approaches have been established for the analyses of proteins in a cell at a given state, and mass spectrometry (MS) has proven to be an efficient and versatile tool. MS-based proteomics approaches have significantly improved beyond the initial identification of proteins to comprehensive characterization and quantification of proteomes and their posttranslational modifications (PTMs). Despite these advances, there is still ongoing development of new technologies to profile and analyze cellular proteomes more completely and efficiently. In this review, we focus on MS-based techniques, describe basic approaches for MS-based profiling of cellular proteomes and analysis methods to identify proteins in complex mixtures, and discuss the different approaches for quantitative proteome analysis. Finally, we briefly discuss novel developments for the analysis of PTMs. Altered levels of PTM, sometimes in the absence of protein expression changes, are often linked to cellular responses and disease states, and the comprehensive analysis of cellular proteome would not be complete without the identification and quantification of the extent of PTMs of proteins.
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Zhang, Shang-Zhi, Lin-Bao Zhu, Ling-Ling You, Jie Wang, Hui-Hua Cao, Ying-Xue Liu, Shahzad Toufeeq, Yu-Ling Wang, Xue Kong, and Jia-Ping Xu. "A Novel Digestive Proteinase Lipase Member H-A in Bombyx mori Contributes to Digestive Juice Antiviral Activity against B. mori Nucleopolyhedrovirus." Insects 11, no. 3 (March 1, 2020): 154. http://dx.doi.org/10.3390/insects11030154.
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Previous studies have revealed that some proteins in Bombyx mori larvae digestive juice show antiviral activity. Here, based on the label-free proteomics data, BmLipase member H-A (BmLHA) was identified as being involved in the response to BmNPV infection in B. mori larvae digestive juice. In the present study, a gene encoding the BmLHA protein in B. mori was characterized. The protein has an open reading fragment of 999 bp, encoding a predicted 332 amino acid residue-protein with a molecular weight of approximately 35.9 kDa. The phylogenetic analysis revealed that BmLHA shares a close genetic distance with Papilio xuthus Lipase member H-A. BmLHA was highly expressed in the middle part of the B. mori gut, and the expression level increased with instar rising in larvae. There was higher expression of BmLHA in A35 than in P50 strains, and it was upregulated in both A35 and P50 strains, following BmNPV infection. The expression level of VP39 decreased significantly in appropriate recombinant-BmLHA-treated groups compared with the PBS-treated group in B. mori larvae and BmN cells. Meanwhile, overexpression of BmLHA significantly reduced the infectivity of BmNPV in BmN cells. These results indicated that BmLHA did not have digestive function but had anti-BmNPV activity. Taken together, our work provides valuable data for the clarification of the molecular characterization BmLHA and supplements research on proteins of anti-BmNPV activity in B. mori.
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Wang, Michelle, Tao Li, Yuan Ren, Bijal Shah, Tint Lwin, Jing Gao, Kenneth H. Shain, Wei Zhang, Xiaohong Zhao, and Jianguo Tao. "Pharmaocogenomic Characterization of MCL-1 Inhibitor Response and Resistance in Aggressive B-Cell Lymphomas." Blood 136, Supplement 1 (November 5, 2020): 20–21. http://dx.doi.org/10.1182/blood-2020-141407.
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Mantle cell lymphoma (MCL) and diffuse large B-cell lymphoma (DLBCL) are aggressive hematologic malignancies characterized by the accumulation of lymphoid cells defective in cell apoptosis biology and function. The anti-apoptotic B-cell lymphoma 2 (BCL-2) family proteins are pivotal regulators of the mitochondrial apoptotic pathway and genetic aberrations in these genes are associated with lymphomagenesis and chemotherapeutic resistance. Notably, the anti-apoptotic myeloid cell leukemia 1 (MCL-1) protein is recurrently highly expressed in various kinds of non-Hodgkin's B-cell lymphomas and promotes the survival of lymphoma cells by counteracting pro-apoptotic protein activity. Collectively, these data support the hypothesis that MCL-1 plays a central role in B-cell lymphoma progression and drug resistance. Pharmacologically targeting MCL-1, therefore, represents an attractive strategy to combat these lymphomas. However, previous clinical and pre-clinical data suggest that treatment with single agent anti-BCL-2 family member therapy is associated with rapid acquisition of resistance. To this end, there is a great need to develop and apply selective small molecule MCL-1 inhibitors as part of a first-line therapy or upon emergence of tumor resistance characterized by upregulation of MCL-1 for lymphoma therapy. Here, we exploited the MCL-1 dependency in MCL and DLBCL by implementing pharmacogenomic and chemical proteomic approaches to investigate the molecular drug response and resistance mechanism to MCL-1 inhibitors. In anticipation of the evolution of MCL-1 inhibitor resistance, we modeled MCL-1 inhibitor resistance mechanisms by developing S63845 resistant lines with high doses of S63845 treatment for an extended period in MCL, DLBCL and MCL-derived lines. RNA sequencing and chemical proteomics on paired parental and resistant cells demonstrated that transcriptome and kinome reprograming linked to the MEK and ERK pathways contribute to MCL-1 inhibitor resistance via regulation of the BCL-2 family profile (BCL-2 and BIM), and as such, represent a novel targetable vulnerability in MCL-1 inhibitor resistant lymphoma. Additional analyses revealed synergistic activity of MCL-1 inhibitors (S63845, AZD5991) in combinations with inhibitors of MEK (Trametinib), ERK (SCH772984) and BCR (Ibrutinib) in MCL-1 inhibitor resistant MCL/DLBCL lines and primary samples. These results provide a strong rationale for further evaluation of MCL-1 inhibitor in combination with established therapy in the clinical setting and highlight a potential strategy for overcoming MCL-1 inhibitor resistance. Disclosures Shah: NCCN: Vice-Chair, Acute Lymphoblastic Leukemia Working Group: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead, Precision Biosciences, Novartis, AstraZeneca: Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Kite/Gilead, Jazz, Incyte: Research Funding; Moffitt Cancer Center: Current Employment; Kite/Gilead, Celgene/Juno/BMS, Novartis, Pfizer, Amgen, Spectrum/Acrotech, Precision Biosciences, Beigene, AstraZeneca, Pharmacyclics/Jansen, Adaptive: Honoraria. Shain:AbbVie: Research Funding; GlaxoSmithKline: Speakers Bureau; Takeda: Honoraria, Speakers Bureau; Amgen: Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive: Consultancy, Honoraria; Janssen: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau.
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Brassat, Ute, Stefan Balabanov, Ulrike Hartmann, Daniel Rössler, Kerstin Borgmann, Judith Dierlamm, and Tim H. Brummendorf. "Characterization of Bcr-Abl Positive Leukemic Cells under Long-Term In Vitro Treatment with Telomerase Inhibitor BIBR1532." Blood 108, no. 11 (November 1, 2006): 2185. http://dx.doi.org/10.1182/blood.v108.11.2185.2185.
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Abstract In normal somatic cells telomeres shorten with each cell division because of the end-replication problem. The ribonucleoprotein enzyme telomerase is able to prevent replicative telomere shortening and to maintain or elongate telomere length. In 90 % of tumour cells the enzyme telomerase is found to be upregulated. Chronic myeloid leukemia is a disorder characterized by a reciprocal translocation between Chromosome 9 and 22, leading to the so called Philadelphia chromosome harbouring the BCR-ABL translocation. BCR-ABL positive leukemic stem cells are characterized by increased turnover leading to accelerated telomere shortening as opposed to their normal counterparts. It is unclear to date whether accelerated telomere shortening in Bcr-Abl-positive cells is linked to genetic instability eventually leading to the acquisition of secondary clonal events that might propagate acceleration of the disease to blast crisis. Therefore we aimed to characterize Bcr-Abl positive chronic myeloid leukemia cell line K562 with or without inhibition of telomerase activity under long-term culture conditions. K652 cells were expanded for 400 populations doublings (PD) with or without treatment with the small molecule telomerase inhibitor BIBR1532 in vitro. While telomeres in untreated control cells remained relatively constant, telomeres in BIBR1532 treated cells underwent replicative shortening from 10 kb to 3 kb (as measured by flow FISH), reflecting a rate of 22 base pairs (bp) lost per PD. No difference in growth kinetics were observed until that stage. We next characterized treated K562 with short telomeres (K562-S) in contrast to control cells with long telomeres (K562-L) for the expression of telomere and telomerase-binding proteins. No difference in mRNA expression for any of the candidate proteins were observed by RT-PCR. Comparative analysis of global protein expression was performed by 2D gel electrophoresis. Taken together, 23 protein spots were found to be differentially expressed between treated and untreated cells, fifteen of which were already identified by mass spectometry. Additionally, we analysed the cells for the acquisition of additional cytogenetic abnormalities by M-FISH. Interestingly, in this ongoing study, we consistently found acquisition of genetic material on chromosome 7 in treated as compared to untreated cells. To study radiation sensitivity under BIBR1532 treatment, K562 cells were exposed to increasing doses of irradiation. Interestingly, despite of a dose-dependent increase in the fraction of apoptotic cells in the pre-treated as opposed to control cells, no accumulation in the number of double strand breaks or lethal aberrations were detected. Interestingly, telomere shortening after telomerase inhibition translated to increased sensitivity to Imatinib (IC50 0.6 μM vs. IC50 1.2 μM). Taken together, telomerase inhibition represent a attractive new therapeutic strategy in Bcr-Abl positive leukemias. However, careful evaluation of side effects need to be studied on the proteomics and cytogenetic level.
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Singh, Sarvendra Vikram. "Bioinformatics – Supporting modern life science research, applications, and challenges." Brazilian Journal of Development 10, no. 2 (February 7, 2024): e67060. http://dx.doi.org/10.34117/bjdv10n2-011.
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Bioinformatics is an interdisciplinary field that develops methods, software tools for understanding biological data and aims to investigate questions about biological composition, structure, function, and evolution of molecules, cells, tissues, and organisms using mathematics, informatics, statistics, and computer science. As we are moving towards the era of cutting-edge technologies there will be a lot of data to store, process and analyze. It offers analysis software for data studies and comparisons and provides tools for modeling, visualizing, exploring and interpreting data. It includes analysis, structural and functional characterization of biomolecules leading to the development of Genomics, Proteomics, Transcriptomics, and Metabolomics, etc. Drug discovery and development tools, supported by recent advancements in machine learning and cloud computing should shorten the time to find and produce an efficient drug compound with fewer side effects and more results emerge as a branch called Chemo-informatics. Personalized medicine where bioinformatics can help a lot to make drug molecules based on the genetic makeup of individuals for better outcomes is a prime area of research and need of the society at present. The major futures challenge of the scientific community is to create an in-vitro model of whole-cell or organism and further simulating a whole cell or an organism by applying in-silico approaches. To achieve that, reliable tools that utilize those technologies need to be developed and tested. Bioinformatics reduces the search space/size of the problem by thousand times. The main goal is to convert a multitude of complex data into useful information and knowledge. As a consequence of understanding such data, one can basically engineer longer life for society.
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Anitha Devi.U, Srinivas.T, Parvathi.D, Venkateshwarlu.M, and Ugandhar.T. "The Impact of Innovative Research Methods for Enhancing Agricultural Plants for Sustainable Development in The Future." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 03 (March 14, 2024): 65–73. http://dx.doi.org/10.47392/irjaem.2024.0011.
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In the pursuit of sustainable agricultural practices, the role of innovative research methods in enhancing agricultural plants is paramount. This abstract delineates the profound impact of such methodologies on sustainable development in the foreseeable future. Cutting-edge techniques such as genomic sequencing, molecular markers, and genome editing technologies revolutionize plant breeding by identifying and manipulating genes responsible for desirable traits, including disease resistance and drought tolerance. Omics technologies, encompassing transcriptomics, proteomics, and metabolomics, provide comprehensive insights into gene expression patterns and metabolic pathways, thereby facilitating targeted breeding strategies aimed at enhancing plant resilience and productivity. High-throughput phenotyping platforms enable rapid and accurate characterization of plant traits, empowering breeders to select superior varieties with enhanced performance under diverse environmental conditions. Concurrently, bioinformatics tools and data analytics play a pivotal role in deciphering complex genomic datasets, aiding in the identification of key genes and regulatory networks governing important agronomic traits. Biotechnology and synthetic biology approaches offer novel avenues for genetic manipulation, allowing for the design and engineering of plants with optimized traits, such as improved nutritional content and stress tolerance. Additionally, the integration of climate modeling with systems biology provides valuable insights into the interaction between genotype and environment, facilitating the development of climate-resilient crop varieties tailored to specific agroecosystems. Through the synergistic application of these innovative research methods, agricultural scientists and breeders can accelerate the pace of crop improvement while simultaneously promoting sustainability by fostering resilience, resource efficiency, and environmental stewardship. This transformative paradigm in agricultural research promises to address the complex challenges of food security, climate change, and environmental degradation, thereby laying the foundation for a more sustainable and resilient agricultural future.
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So, Shan Shan, Valen Z. Yu, Simon Y. Law, and Maria L. Lung. "Abstract 5835: Functional and mechanistic characterization of ΔNp63α in esophageal squamous cell carcinoma." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5835. http://dx.doi.org/10.1158/1538-7445.am2022-5835.
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Abstract Esophageal squamous cell carcinoma (ESCC) is highly prevalent in Asia with a poor prognosis and a high mortality rate [1, 2]. The p63 protein, encoded by TP63, is a master regulator involved in many cell events including cell cycle, DNA damage, cell proliferation, stem cell maintenance, and cell death in different cancer types. ΔNp63α is the dominantly expressed isoform of p63 in squamous cell carcinomas, playing a critical role in tumorigenesis [3, 4]. However, the functional roles of ΔNp63α in ESCC have not been fully elucidated. Our results have revealed that ΔNp63α is highly expressed and up-regulated in ESCC. We have shown that ΔNp63α protein expression plays an oncogenic role in ESCC cells, depletion of which inhibits in vitro cell proliferation and colony formation and greatly suppresses in vivo tumor growth, in a panel of ESCC cell lines and our latest patient-derived organoid cultures, potentially in a differentiation-dependent manner. Glycolysis pathway and two novel downstream signaling pathways of ΔNp63α, MYC/4EBP1 and AKT/NDRG1, are significantly suppressed upon ΔNp63α depletion in ESCC cell lines. Notably, xenografts of ΔNp63α-depleted human ESCC cells show elevated infiltrating cancer-associated fibroblasts (CAFs), which may contribute to tumor suppression by ΔNp63α depletion. These data not only shed light on the ΔNp63α functions and how they contribute to the ESCC tumorigenesis, but also unravel potential therapeutic benefits for ESCC patients in the future. Acknowledgements: We acknowledge DSMZ (German Collection of Microorganisms and Cell Culture) for the KYSE cell lines. We acknowledge the Research Grants Council Theme-Based Research Scheme grant T12-701/17-R to MLL. References [1] Chen, W., et al., Cancer statistics in China, 2015. CA Cancer J Clin, 2016. 66(2): p. 115-32. [2] Peng, L., et al., CCGD-ESCC: A Comprehensive Database for Genetic Variants Associated with Esophageal Squamous Cell Carcinoma in Chinese Population. Genomics Proteomics Bioinformatics, 2018. 16(4): p. 262-268. [3] Fukunishi, N., et al., Induction of DeltaNp63 by the newly identified keratinocyte-specific transforming growth factor beta Signaling Pathway with Smad2 and IkappaB Kinase alpha in squamous cell carcinoma. Neoplasia, 2010. 12(12): p. 969-79. [4] Moses, M.A., et al., Molecular Mechanisms of p63-Mediated Squamous Cancer Pathogenesis. Int J Mol Sci, 2019. 20(14). Citation Format: Shan Shan So, Valen Z. Yu, Simon Y. Law, Maria L. Lung. Functional and mechanistic characterization of ΔNp63α in esophageal squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5835.
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Krishnan, Rahul, Lisa Schweizer, Agnes Bilecz, Aasa Shimizu, Rachelle Mendoza, Diane Yamada, Ricardo Lastra, Matthias Mann, and Ernst Lengyel. "Abstract 6782: Spatial transcriptomics of serous tubal intraepithelial carcinoma and its putative precursor lesions." Cancer Research 83, no. 7_Supplement (April 4, 2023): 6782. http://dx.doi.org/10.1158/1538-7445.am2023-6782.
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Abstract Background: Increasing evidence over the past two decades has implicated serous tubal intraepithelial carcinoma (STIC) of the fallopian tube (FT) epithelium as the putative precursor lesion for high-grade serous ovarian cancer (HGSC). Additional atypical lesions of the FT known as p53 signature lesions and serous tubal intraepithelial lesions (STIL) have been proposed as early precursors in the carcinogenic sequence based on morphology, histochemical and genetic studies. Little however is known about the molecular events that drive FT epithelial cells to transform into HGSC. Methods: To elucidate the molecular changes that underlie the progression from p53 signature to STIL to STIC, we performed spatial transcriptomics on formalin-fixed paraffin embedded (FFPE) tissue sections using the Nanostring GeoMx Whole Transcriptome Atlas panel (18,000 protein-encoding gene panel). Our study cohort consisted of 16 representative cases (3 p53 signature cases, 3 STIL cases, 3 STIC lesions, and 7 STIC cases with matched HGSC). Results: We characterized gene expression to a depth of over 9400 genes in these rare precursor cell populations. Our analysis demonstrated that by gene expression profiles, p53 signatures and STIL lesions bear close resemblance to normal FT secretory cells compared to FT ciliated cells or even STIC and invasive HGSC. We identify for the first time, several pathways altered in expression between normal fallopian tube epithelium and early p53 signature lesions including those related to signaling pathways (Trop-2), Wnt pathway (LGR5), identifying new pathways observed in the progression from normal FT cells to precursor intraepithelial lesion. Our data show that STIC lesions bear significant similarities to invasive HGSC by gene expression, especially compared to the earlier precursor lesions (p53 signature and STIL). We observed several alterations in cell-adhesion(EGR1) and signaling pathways, transcription factors (Trop-2) and metabolism that mark the transition from STIC to HGSC, representing possible mechanisms by which these intraepithelial lesions transform into disseminated invasive disease. Conclusion: Our data profile HGSC precursor lesions in high-depth with characterization of over 9400 genes providing new insight into the molecular alterations that characterize these intraepithelial lesions. Our future direction is to pair transcriptomics data with our established spatial proteomics platform (Deep Visual Proteomics) to measure matched spatial protein expression in our cohort. Citation Format: Rahul Krishnan, Lisa Schweizer, Agnes Bilecz, Aasa Shimizu, Rachelle Mendoza, Diane Yamada, Ricardo Lastra, Matthias Mann, Ernst Lengyel. Spatial transcriptomics of serous tubal intraepithelial carcinoma and its putative precursor lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6782.
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Chow, Jocelyn, Alexander Chen, Se-yeong Oh, Elizabeth Young, Nathaniel Boyd, and Renee Read. "BIOM-11. THE ROLE OF INSULIN-LIKE GROWTH FACTOR 2 BINDING PROTEIN 3 (IGF2BP3) AS A DRIVER OF TUMORIGENESIS IN GLIOBLASTOMA." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v6. http://dx.doi.org/10.1093/neuonc/noad179.0022.
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Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with a poor median survival of fifteen months after diagnosis. GBM accounts for half of malignant and fifteen percent of all primary brain tumors diagnosed in adults. To promote targeted therapy, there are ongoing efforts to understand GBM at a molecular level. This involves characterization of these tumors via their transcriptional and mutational profile. Recent studies indicate aberrations in receptor tyrosine kinase (RTKs), including EGFR and PDGFRA, and the Pi-3 kinase (PI3K) signaling pathways are major drivers of tumorigenesis in GBM. Using a proteomics approach to locate downstream targets of these pathways, we identified IGF2 Binding Protein 3 (IGF2BP3), which is an RNA binding protein, as a downstream effector of aberrant RTK-PI3K signaling in gliomagenesis. Our subsequent follow-up studies indicate that IGF2BP3, which is overexpressed in RTK mutant human GBMs, drives survival and proliferation of RTK-PI3K-dependent human GBM cells. To understand how IGF2BP3 functions to promote RTK-PI3K driven tumorigenesis, we used cultured human GBM cells and in GBM xenograft models to test the effects of loss of IGF2BP3 function in tumorous glia as well as normal glia. Our results indicate that IGF2BP3 activity is essential for self-renewal and maintenance of neural stem-cell like features of GBM tumor cells, but dispensable for the growth of normal glia. Currently, we are using genetic approaches to identify and validate RNA targets of IGF2BP3 function in tumor cells and to understand how these targets are specifically regulated by RTK-PI3K-dependent signaling in glioma. Overall, our data suggests that IGF2BP3 may play a role in promoting GBM tumorigenesis and our future directions aim to reveal an important tumor-specific RNA target signaling pathway for consideration in potential therapeutics.
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Neagu, Anca-Narcisa, Danielle Whitham, Pathea Bruno, Hailey Morrissiey, Celeste A. Darie, and Costel C. Darie. "Omics-Based Investigations of Breast Cancer." Molecules 28, no. 12 (June 14, 2023): 4768. http://dx.doi.org/10.3390/molecules28124768.
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Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three ”big omics”, based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein–protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.
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Sakura, Fumiaki, Kosuke Noma, Takaki Asano, Kay Tanita, Etsushi Toyofuku, Kentaro Kato, Miyuki Tsumura, et al. "A complementary approach for genetic diagnosis of inborn errors of immunity using proteogenomic analysis." PNAS Nexus, March 28, 2023. http://dx.doi.org/10.1093/pnasnexus/pgad104.
Full textAbstract:
Advances in next-generation sequencing technology have identified many genes responsible for inborn errors of immunity (IEI). However, there is still room for improvement in the efficiency of genetic diagnosis. Recently, RNA sequencing and proteomics using peripheral blood mononuclear cells (PBMCs) have gained attention, but only some studies have integrated these analyses in IEI. Moreover, previous proteomic studies for PBMCs have achieved limited coverage (approximately 3000 proteins). More comprehensive data are needed to gain valuable insights into the molecular mechanisms underlying IEI. Here, we propose a state-of-the-art method for diagnosing IEI using PBMCs proteomics integrated with targeted RNA sequencing (T-RNA-seq), providing unique insights into the pathogenesis of IEI. This study analyzed 70 IEI patients whose genetic etiology had not been identified by genetic analysis. In-depth proteomics identified 6498 proteins, which covered 63% of 527 genes identified in T-RNAseq, allowing us to examine the molecular cause of IEI and immune cell defects. This integrated analysis identified the disease-causing genes in four cases undiagnosed in previous genetic studies. Three of them could be diagnosed by T-RNA-seq, while the other could only be diagnosed by proteomics. Moreover, this integrated analysis showed high protein-mRNA correlations in B- and T-cell-specific genes, and their expression profiles identified patients with immune cell dysfunction. These results indicate that integrated analysis improves the efficiency of genetic diagnosis and provides a deep understanding of the immune cell dysfunction underlying the etiology of IEI. Our novel approach demonstrates the complementary role of proteogenomic analysis in the genetic diagnosis and characterization of IEI.
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Sun, Benjamin B., Joshua Chiou, Matthew Traylor, Christian Benner, Yi-Hsiang Hsu, Tom G. Richardson, Praveen Surendran, et al. "Plasma proteomic associations with genetics and health in the UK Biobank." Nature, October 4, 2023. http://dx.doi.org/10.1038/s41586-023-06592-6.
Full textAbstract:
AbstractThe Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Zhang, Minzhe, Thomas Sheffield, Xiaowei Zhan, Qiwei Li, Donghan M. Yang, Yunguan Wang, Shidan Wang, Yang Xie, Tao Wang, and Guanghua Xiao. "Spatial molecular profiling: platforms, applications and analysis tools." Briefings in Bioinformatics, August 6, 2020. http://dx.doi.org/10.1093/bib/bbaa145.
Full textAbstract:
Abstract Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.