WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 36Citation - Scopus: 35Trail Promotes the Polarization of Human Macrophages Toward a Proinflammatory M1 Phenotype and Is Associated With Increased Survival in Cancer Patients With High Tumor Macrophage Content(Frontiers Media S.A., 2023-09-21) Gunalp, Sinem; Helvaci, Derya Goksu; Oner, Aysenur; Bursali, Ahmet; Conforte, Alessandra; Guener, Hueseyin; Sag, Duygu; Güner, HüseyinBackgroundTNF-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily that can either induce cell death or activate survival pathways after binding to death receptors (DRs) DR4 or DR5. TRAIL is investigated as a therapeutic agent in clinical trials due to its selective toxicity to transformed cells. Macrophages can be polarized into pro-inflammatory/tumor-fighting M1 macrophages or anti-inflammatory/tumor-supportive M2 macrophages and an imbalance between M1 and M2 macrophages can promote diseases. Therefore, identifying modulators that regulate macrophage polarization is important to design effective macrophage-targeted immunotherapies. The impact of TRAIL on macrophage polarization is not known.MethodsPrimary human monocyte-derived macrophages were pre-treated with either TRAIL or with DR4 or DR5-specific ligands and then polarized into M1, M2a, or M2c phenotypes in vitro. The expression of M1 and M2 markers in macrophage subtypes was analyzed by RNA sequencing, qPCR, ELISA, and flow cytometry. Furthermore, the cytotoxicity of the macrophages against U937 AML tumor targets was assessed by flow cytometry. TCGA datasets were also analyzed to correlate TRAIL with M1/M2 markers, and the overall survival of cancer patients.ResultsTRAIL increased the expression of M1 markers at both mRNA and protein levels while decreasing the expression of M2 markers at the mRNA level in human macrophages. TRAIL also shifted M2 macrophages towards an M1 phenotype. Our data showed that both DR4 and DR5 death receptors play a role in macrophage polarization. Furthermore, TRAIL enhanced the cytotoxicity of macrophages against the AML cancer cells in vitro. Finally, TRAIL expression was positively correlated with increased expression of M1 markers in the tumors from ovarian and sarcoma cancer patients and longer overall survival in cases with high, but not low, tumor macrophage content.ConclusionsTRAIL promotes the polarization of human macrophages toward a proinflammatory M1 phenotype via both DR4 and DR5. Our study defines TRAIL as a new regulator of macrophage polarization and suggests that targeting DRs can enhance the anti-tumorigenic response of macrophages in the tumor microenvironment by increasing M1 polarization.Article Citation - WoS: 23Citation - Scopus: 23The Age Structure, Stringency Policy, Income, and Spread of Coronavirus Disease 2019: Evidence From 209 Countries(Frontiers Media S.A., 2021-02-12) Bilgili, Faik; Dundar, Munis; Kuskaya, Sevda; Lorente, Daniel Balsalobre; Unlu, Fatma; Gencoglu, Pelin; Mugaloglu, ErhanThis article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.Article Citation - WoS: 80Citation - Scopus: 93Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions(Frontiers Media S.A., 2021-02-22) Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava; Elbere, Ilze; Roshchupkin, Gennady; Adilovic, Muhamed; Claesson, Marcus J.The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.Article Citation - WoS: 9Citation - Scopus: 15MicroBiomeGSM: The Identification of Taxonomic Biomarkers From Metagenomic Data Using Grouping, Scoring and Modeling (G-S-M) Approach(Frontiers Media S.A., 2023-11-22) Bakir-Gungor, Burcu; Temiz, Mustafa; Jabeer, Amhar; Wu, Di; Yousef, MalikNumerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM.Article Citation - WoS: 16Citation - Scopus: 17Inhibition of Pathologic Immunoglobulin E in Food Allergy by EBF-2 and Active Compound Berberine Associated With Immunometabolism Regulation(Frontiers Media S.A., 2023-02-07) Yang, Nan; Maskey, Anish R.; Srivastava, Kamal; Kim, Monica; Wang, Zixi; Musa, Ibrahim; Li, Xiu-MinIntroductionFood allergy is a significant public health problem with limited treatment options. As Food Allergy Herbal Formula 2 (FAHF-2) showed potential as a food allergy treatment, we further developed a purified version named EBF-2 and identified active compounds. We investigated the mechanisms of EBF-2 on IgE-mediated peanut (PN) allergy and its active compound, berberine, on IgE production. MethodsIgE plasma cell line U266 cells were cultured with EBF-2 and FAHF-2, and their effects on IgE production were compared. EBF-2 was evaluated in a murine PN allergy model for its effect on PN-specific IgE production, number of IgE(+) plasma cells, and PN anaphylaxis. Effects of berberine on IgE production, the expression of transcription factors, and mitochondrial glucose metabolism in U266 cells were evaluated. ResultsEBF-2 dose-dependently suppressed IgE production and was over 16 times more potent than FAHF-2 in IgE suppression in U266 cells. EBF-2 significantly suppressed PN-specific IgE production (70%, p<0.001) and the number of IgE-producing plasma cells in PN allergic mice, accompanied by 100% inhibition of PN-induced anaphylaxis and plasma histamine release (p<0.001) without affecting IgG1 or IgG2a production. Berberine markedly suppressed IgE production, which was associated with suppression of XBP1, BLIMP1, and STAT6 transcription factors and a reduced rate of mitochondrial oxidation in an IgE-producing plasma cell line. ConclusionsEBF-2 and its active compound berberine are potent IgE suppressors, associated with cellular regulation of immunometabolism on IgE plasma cells, and may be a potential therapy for IgE-mediated food allergy and other allergic disorders.Editorial Editorial: Microbial Production of Medicinally Important Agents(Frontiers Media S.A., 2023-09-20) Zeng, Jia; Zhan, Jixun; Qiao, Xue; Fidan, OzkanArticle Citation - WoS: 25Citation - Scopus: 31Discovering Potential Taxonomic Biomarkers of Type 2 Diabetes From Human Gut Microbiota via Different Feature Selection Methods(Frontiers Media S.A., 2021-08-25) Bakir-Gungor, Burcu; Bulut, Osman; Jabeer, Amhar; Nalbantoglu, O. Ufuk; Yousef, MalikHuman gut microbiota is a complex community of organisms including trillions of bacteria. While these microorganisms are considered as essential regulators of our immune system, some of them can cause several diseases. In recent years, next-generation sequencing technologies accelerated the discovery of human gut microbiota. In this respect, the use of machine learning techniques became popular to analyze disease-associated metagenomics datasets. Type 2 diabetes (T2D) is a chronic disease and affects millions of people around the world. Since the early diagnosis in T2D is important for effective treatment, there is an utmost need to develop a classification technique that can accelerate T2D diagnosis. In this study, using T2D-associated metagenomics data, we aim to develop a classification model to facilitate T2D diagnosis and to discover T2D-associated biomarkers. The sequencing data of T2D patients and healthy individuals were taken from a metagenome-wide association study and categorized into disease states. The sequencing reads were assigned to taxa, and the identified species are used to train and test our model. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization, Maximum Relevance and Minimum Redundancy, Correlation Based Feature Selection, and select K best approach. To test the performance of the classification based on the features that are selected by different methods, we used random forest classifier with 100-fold Monte Carlo cross-validation. In our experiments, we observed that 15 commonly selected features have a considerable effect in terms of minimizing the microbiota used for the diagnosis of T2D and thus reducing the time and cost. When we perform biological validation of these identified species, we found that some of them are known as related to T2D development mechanisms and we identified additional species as potential biomarkers. Additionally, we attempted to find the subgroups of T2D patients using k-means clustering. In summary, this study utilizes several supervised and unsupervised machine learning algorithms to increase the diagnostic accuracy of T2D, investigates potential biomarkers of T2D, and finds out which subset of microbiota is more informative than other taxa by applying state-of-the art feature selection methods.</p>Article Citation - WoS: 9Citation - Scopus: 9Berberine-Containing Natural-Medicine With Boiled Peanut-Oit Induces Sustained Peanut-Tolerance Associated With Distinct Microbiota Signature(Frontiers Media S.A., 2023-07-26) Srivastava, Kamal; Cao, Mingzhuo; Fidan, Ozkan; Shi, Yanmei; Yang, Nan; Nowak-Wegrzyn, Anna; Li, Xiu-MinBackgroundGut microbiota influence food allergy. We showed that the natural compound berberine reduces IgE and others reported that BBR alters gut microbiota implying a potential role for microbiota changes in BBR function. ObjectiveWe sought to evaluate an oral Berberine-containing natural medicine with a boiled peanut oral immunotherapy (BNP) regimen as a treatment for food allergy using a murine model and to explore the correlation of treatment-induced changes in gut microbiota with therapeutic outcomes. MethodsPeanut-allergic (PA) mice, orally sensitized with roasted peanut and cholera toxin, received oral BNP or control treatments. PA mice received periodic post-therapy roasted peanut exposures. Anaphylaxis was assessed by visualization of symptoms and measurement of body temperature. Histamine and serum peanut-specific IgE levels were measured by ELISA. Splenic IgE(+)B cells were assessed by flow cytometry. Fecal pellets were used for sequencing of bacterial 16S rDNA by Illumina MiSeq. Sequencing data were analyzed using built-in analysis platforms. ResultsBNP treatment regimen induced long-term tolerance to peanut accompanied by profound and sustained reduction of IgE, symptom scores, plasma histamine, body temperature, and number of IgE(+) B cells (p <0.001 vs Sham for all). Significant differences were observed for Firmicutes/Bacteroidetes ratio across treatment groups. Bacterial genera positively correlated with post-challenge histamine and PN-IgE included Lachnospiraceae, Ruminococcaceae, and Hydrogenanaerobacterium (all Firmicutes) while Verrucromicrobiacea. Caproiciproducens, Enterobacteriaceae, and Bacteroidales were negatively correlated. ConclusionsBNP is a promising regimen for food allergy treatment and its benefits in a murine model are associated with a distinct microbiota signature.Article Citation - WoS: 11Citation - Scopus: 12Analyzing the Genetic Diversity and Biotechnological Potential of Leuconostoc Pseudomesenteroides by Comparative Genomics(Frontiers Media S.A., 2023-01-11) Gumustop, Ismail; Ortakci, FatihLeuconostoc pseudomesenteroides is a lactic acid bacteria species widely exist in fermented dairy foods, cane juice, sourdough, kimchi, apple dumpster, caecum, and human adenoid. In the dairy industry, Ln. pseudomesenteroides strains are usually found in mesophilic starter cultures with lactococci. This species plays a crucial role in the production of aroma compounds such as acetoin, acetaldehyde, and diacetyl, thus beneficially affecting dairy technology. We performed genomic characterization of 38 Ln. pseudomesenteroides from diverse ecological niches to evaluate this species' genetic diversity and biotechnological potential. A mere similar to 12% of genes conserved across 38 Ln. pseudomesenteroides genomes indicate that accessory genes are the driving force for genotypic distinction in this species. Seven main clades were formed with variable content surrounding mobile genetic elements, namely plasmids, transposable elements, IS elements, prophages, and CRISPR-Cas. All but three genomes carried CRISPR-Cas system. Furthermore, a type IIA CRISPR-Cas system was found in 80% of the CRISPR-Cas positive strains. AMBR10, CBA3630, and MGBC116435 were predicted to encode bacteriocins. Genes responsible for citrate metabolism were found in all but five strains belonging to cane juice, sourdough, and unknown origin. On the contrary, arabinose metabolism genes were only available in nine strains isolated from plant-related systems. We found that Ln. pseudomesenteroides genomes show evolutionary adaptation to their ecological environment due to niche-specific carbon metabolism and forming closely related phylogenetic clades based on their isolation source. This species was found to be a reservoir of type IIA CRISPR-Cas system. The outcomes of this study provide a framework for uncovering the biotechnological potential of Ln. pseudomesenteroides and its future development as starter or adjunct culture for dairy industry.Article Citation - WoS: 19Citation - Scopus: 28A Toolbox of Machine Learning Software to Support Microbiome Analysis(Frontiers Media S.A., 2023-11-22) Marcos-Zambrano, Laura Judith; Lopez-Molina, Victor Manuel; Bakir-Gungor, Burcu; Frohme, Marcus; Karaduzovic-Hadziabdic, Kanita; Klammsteiner, Thomas; Carrillo de Santa Pau, EnriqueThe human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.