110 research outputs found

    Detecting Bacterial Species from Ancient Human Skeletal Samples

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    This paleopathological study aims to identify Mycobacterium tuberculosis complex (MTBC), Mycobacterium avium complex (MAC) and other Mycobacterium species in silico from skeletal samples that belonged to 28 Polish individuals in the Neolithic period under PRJNA422903 from the Sequence Read Archive (SRA). After next-generation sequencing (NGS), bioinformatics methods are heavily relied upon for identification of pathogens from complex samples. We implemented a bioinformatics pipeline, with custom-built databases, utilizing the following software tools: Trim Galore! and Kraken2. After adapter trimming, Kraken2 was used for taxonomic classifications. We have found that Mycobacterium is present in all 28 individuals. The average percentage of MAC present in the genus Mycobacterium, in all 28 individuals, is 6%. Reads from MTBC makes up an average of 7% of the Mycobacterium genus. We have identified previously unreported strains of MTBC and MAC such as Mycobacterium tuberculosis XDR1219, which is an extensively drug-resistance strain. Our analysis also revealed 14.8% of reads from MTBC belong to Mycobacterium avium hominissuis, which was commonly found in humans and pigs. Additionally, strains of Mycobacterium simiae complex were also discovered. Mycobacterium simiae has been commonly found among immunocompromised individuals. In conclusion, our bioinformatics pipeline has been more effective than other published approaches. This approach broadens the potential scope of paleoepidemiology both to older, sub-optimally preserved samples and to pathogens with difficult intrageneric taxonomy. It is therefore suitable for other studies in paleopathology using NGS technologies

    Detecting bacterial species from ancient human skeletal samples

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    Diagnosis of tuberculosis (TB) via morphological analysis is difficult and often inconsistent. With next-generation sequencing (NGS), ancient host microbiomes can be subjected to metagenomic analyses for the detection of TB in silico. Suitable bioinformatic workflows are needed for reliable ancient DNA (aDNA) analysis of causative agents. This study aims to enhance available bioinformatic screening methods to create more suitable bioinformatic processes and generate insights in relation to TB. This research utilizes publicly available NGS data accessed through the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI). Initial quality control steps included adapter trimming with Trim Galore!. Kraken2 was then used for taxonomic classification with a custom-built database comprised of Mycobacterial genomes from the NCBI. Quantitation and visualization were carried out with Bracken and Krona, respectively. Our workflow was first applied to 28 Neolithic skeletons (SRA number PRJNA422903) representing the Middle Neolithic Brześć Kujawski Group of the Lengyel culture (∼4400–4000 BC, 26 individuals), and the Late Neolithic Globular Amphora culture (∼3100–2900 BC, 2 individuals). Three additional datasets have since been utilized in this research: mummified remains of 265 individuals from Hungary (1731–1838 CE; PRJNA795622), one calcified lung nodule from Lund, Sweden (17th century; PRJNA517266); and dental calculus of four individuals from the Iberian Peninsula (4500-5000 BP; PRJEB46022). Preliminary results for the 28 Neolithic skeletons revealed an average of 7% of the Mycobacterium genus sequencing reads mapping to Mycobacterium tuberculosis complex (MTBC) among all individuals. This work also revealed additional species of MTBC and Mycobacterium avium complex (MAC) that were previously unreported by the originator of datasets, including the extensively drug-resistant (XDR) Mycobacterium tuberculosis XDR1219 and Mycobacterium avium hominissuis. Our bioinformatic workflow has therefore been more effective than previously published approaches and is suitable for future paleopathological studies

    Protein secretion systems in bacterial-host associations, and their description in the Gene Ontology

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    Protein secretion plays a central role in modulating the interactions of bacteria with their environments. This is particularly the case when symbiotic bacteria (whether pathogenic, commensal or mutualistic) are interacting with larger host organisms. In the case of Gram-negative bacteria, secretion requires translocation across the outer as well as the inner membrane, and a diversity of molecular machines have been elaborated for this purpose. A number of secreted proteins are destined to enter the host cell (effectors and toxins), and thus several secretion systems include apparatus to translocate proteins across the plasma membrane of the host also. The Plant-Associated Microbe Gene Ontology (PAMGO) Consortium has been developing standardized terms for describing biological processes and cellular components that play important roles in the interactions of microbes with plant and animal hosts, including the processes of bacterial secretion. Here we survey bacterial secretion systems known to modulate interactions with host organisms and describe Gene Ontology terms useful for describing the components and functions of these systems, and for capturing the similarities among the diverse systems

    Genomic sequence of temperate phage Smp131 of Stenotrophomonas maltophilia that has similar prophages in xanthomonads

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    Stenotrophomonas maltophilia is a ubiquitous Gram-negative bacterium previously named as Xanthomonas maltophilia. This organism is an important nosocomial pathogen associated with infections in immunocompromised patients. Clinical isolates of S. maltophilia are mostly resistant to multiple antibiotics and treatment of its infections is becoming problematic. Several virulent bacteriophages, but not temperate phage, of S. maltophilia have been characterized

    Mean structure and fluctuations of the Kuroshio east of Taiwan from in situ and remote observations

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    Author Posting. © The Oceanography Society, 2015. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 28, no. 4 (2015): 74–83, doi:10.5670/oceanog.2015.83.The Kuroshio is important to climate, weather prediction, and fishery management along the northeast coast of Asia because it transports tremendous heat, salt, and energy from east of the Philippines to waters southeast of Japan. In the middle of its journey northward, the Kuroshio’s velocity mean and its variability east of Taiwan crucially affect its downstream variability. To improve understanding of the Kuroshio there, multiple platforms were used to collect intensive observations off Taiwan during the three-year Observations of the Kuroshio Transports and their Variability (OKTV) program (2012–2015). Mean Kuroshio velocity transects show two velocity maxima southeast of Taiwan, with the primary velocity core on the onshore side of the Kuroshio exhibiting a mean maximum velocity of ~1.2 m s–1. The two cores then merge and move at a single velocity maximum of ~1 m s–1 east of Taiwan. Standard deviations of both the directly measured poleward (v) and zonal (u) velocities are ~0.4 m s–1 in the Kuroshio main stream. Water mass exchange in the Kuroshio east of Taiwan was found to be complicated, as it includes water of Kuroshio origin, South China Sea Water, and West Philippine Sea Water, and it vitally affects heat, salt, and nutrient inputs to the East China Sea. Impinging eddies and typhoons are two of the principal causes of variability in the Kuroshio. This study’s models are more consistent with the observed Kuroshio than with high-frequency radar measurements.This study was sponsored by the Ministry of Science and Technology (MOST) of the ROC (Taiwan) under grants NSC 101-2611-M-002-018-MY3, NSC 101-2611- M-019-002, NSC 102-2611-M-002-017, NSC 102-2611- M-019-012, MOST 103-2611-M-002-014, and MOST 103-2611-M-002-018. MA was sponsored by the US Office of Naval Research under grant N00014- 12-1-0445. YHT was supported by NSF Earth System Model (EaSM) Grant 1419292
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