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Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment
(NATURE RESEARCH, 2020-01-01) Chng, Kern Rei; Li, Chenhao; Bertrand, Denis; Ng, Amanda Hui Qi; Kwah, Junmei Samantha; Low, Hwee Meng; Tong, Chengxuan; Natrajan, Maanasa; Zhang, Michael Hongjie; Xu, Licheng; Ko, Karrie Kwan Ki; Ho, Eliza Xin Pei; Av-Shalom V, Tamar; Teo, Jeanette Woon Pei; Khor, Chiea Chuen; Chen, Swaine L.; Mason, Christopher E.; Ng, Oon Tek; Marimuthu, Kalisvar; Ang, Brenda; Nagarajan, Niranjan; Danko, David; Bezdan, Daniela; Afshinnekoo, Ebrahim; Ahsanuddin, Sofia; Bhattacharya, Chandrima; Butler, Daniel J.; De Filippis, Francesca; Hecht, Jochen; Kahles, Andre; Karasikov, Mikhail; Kyrpides, Nikos C.; Leung, Marcus H. Y.; Meleshko, Dmitry; Mustafa, Harun; Mutai, Beth; Neches, Russell Y.; Ng, Amanda; Nieto-Caballero, Marina; Nikolayeva, Olga; Nikolayeva, Tatyana; Png, Eileen; Sanchez, Jorge L.; Shaaban, Heba; Sierra, Maria A.; Tong, Xinzhao; Young, Ben; Alicea, Josue; Bhattacharyya, Malay; Blekhman, Ran; Castro-Nallar, Eduardo; Canas, Ana M.; Chatziefthimiou, Aspassia D.; Crawford, Robert W.; Deng, Youping; Desnues, Christelle; Dias-Neto, Emmanuel; Donnellan, Daisy; Dybwad, Marius; Elhaik, Eran; Ercolini, Danilo; Frolova, Alina; Graf, Alexandra B.; Green, David C.; Hajirasouliha, Iman; Hernandez, Mark; Iraola, Gregorio; Jang, Soojin; Jones, Angela; Kelly, Frank J.; Knights, Kaymisha; Labaj, Pawel P.; Lee, Patrick K. H.; Shawn, Levy; Ljungdahl, Per; Lyons, Abigail; Mason-Buck, Gabriella; McGrath, Ken; Mongodin, Emmanuel F.; Moraes, Milton Ozorio; Noushmehr, Houtan; Oliveira, Manuela; Ossowski, Stephan; Osuolale, Olayinka O.; Ozcan, Orhan; Paez-Espino, David; Rascovan, Nicolas; Richard, Hugues; Raetsch, Gunnar; Schriml, Lynn M.; Semmler, Torsten; Sezerman, Osman U.; Shi, Leming; Song, Le Huu; Suzuki, Haruo; Court, Denise Syndercombe; Thomas, Dominique; Tighe, Scott W.; Udekwu I, Klas; Ugalde, Juan A.; Valentine, Brandon; Vassilev I, Dimitar; Vayndorf, Elena; Velavan, Thirumalaisamy P.; Zambrano, Maria M.; Zhu, Jifeng; Zhu, Sibo; Consortium, MetaSU. B.
Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60\% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections. Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting.
Invertebrate Iridoviruses: A Glance over the Last Decade
(MDPI, 2018-01-01) Ince, Ikbal Agah; Ozcan, Orhan; Ilter-Akulke, Ayca Zeynep; Scully, Erin D.; Ozgen, Arzu
Members of the family Iridoviridae (iridovirids) are large dsDNA viruses that infect both invertebrate and vertebrate ectotherms and whose symptoms range in severity from minor reductions in host fitness to systemic disease and large-scale mortality. Several characteristics have been useful for classifying iridoviruses
Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models
(MDPI, 2022-01-01) Cekikj, Miodrag; Jakimovska Ozdemir, Milena; Kalajdzhiski, Slobodan; Ozcan, Orhan; Sezerman, Osman Ugur
Simple Summary Cancer is one of the leading causes of death worldwide. Colorectal cancer belongs to the group of the most malignant tumors for which their burden can be only reduced through early detection and appropriate treatment. Increasing evidence indicates that the intestine microbiota is related and can impact colorectal carcinogenesis. This study proposes a multidisciplinary approach of two-phase methodology for modeling and interpreting the key biomarkers that can play a significant role in understanding the drug-resistant mechanism for patients diagnosed with colorectal cancer. The proposed methodology was evaluated using a publicly accessible dataset, which may serve clinicians as a complementary analysis tool in colorectal cancer diagnostics and therapeutics. This study contributes to the field of predictive modeling in healthcare. Recent studies have highlighted that gut microbiota can alter colorectal cancer susceptibility and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology based on evaluating high-performance machine learning models where a Python-based random forest classifier provides the best performance metrics, with an overall accuracy of 91.7\%. Our approach identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many studies point out the importance of present genera in the microbiome and intend to treat it separately. The symbiotic bacterial analysis generated different sets of joint feature combinations, providing a combined overview of the model's predictiveness and uncovering additional data correlations where different genera joint impacts support the therapy-resistant effect. This study points out the different perspectives of treatment since our aggregate analysis gives precise results for the genera that are often found together in a resistant group of patients, meaning that resistance is not due to the presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that live in symbiosis.

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