Maria Pimenova

Nasopharyngeal microbiota in infants and changes during viral upper respiratory tract infection and acute otitis media

Background Interferences between pathogenic bacteria and specific commensals are known. We determined the interactions between nasopharyngeal microbial pathogens and commensals during viral upper respiratory tract infection (URI) and acute otitis media (AOM) in infants. Methods We analyzed 971 specimens collected monthly and during URI and AOM episodes from 139 infants. The 16S rRNA V4 gene regions were sequenced on the Illumina MiSeq platform. Results Among the high abundant genus-level nasopharyngeal microbiota were Moraxella, Haemophilus, and Streptococcus (3 otopathogen genera), Corynebacterium, Dolosigranulum, Staphylococcus, Acinetobacter, Pseudomonas, and Bifidobacterium. Bacterial diversity was lower in culture-positive samples for Streptococcus pneumoniae, and Haemophilus influenzae, compared to cultured-negative samples. URI frequencies were positively associated with increasing trend in otopathogen colonization. AOM frequencies were associated with decreasing trend in Micrococcus colonization. During URI and AOM, there were increases in abundance of otopathogen genera and decreases in Pseudomonas, Myroides, Yersinia, and Sphingomonas. Otopathogen abundance was increased during symptomatic viral infection, but not during asymptomatic infection. The risk for AOM complicating URI was reduced by increased abundance of Staphylococcus and Sphingobium. Conclusion Otopathogen genera played the key roles in URI and AOM occurrences. Staphylococcus counteracts otopathogens thus Staphylococcal colonization may be beneficial, rather than harmful. While Sphingobium may play a role in preventing AOM complicating URI, the commonly used probiotic Bifidobacterium did not play a significant role during URI or AOM. The role of less common commensals in counteracting the deleterious effects of otopathogens requires further studies.

Microbiome interaction networks and community structure from laboratory-reared and field-collected Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquito vectors

Microbial interactions are an underappreciated force in shaping insect microbiome communities. Although pairwise patterns of symbiont interactions have been identified, we have a poor understanding regarding the scale and the nature of co-occurrence and co-exclusion interactions within the microbiome. To characterize these patterns in mosquitoes, we sequenced the bacterial microbiome of Aedes aegypti, Ae. albopictus, and Culex quinquefasciatus caught in the field or reared in the laboratory and used these data to generate interaction networks. For collections, we used traps that attracted host-seeking or ovipositing female mosquitoes to determine how physiological state affects the microbiome under field conditions. Interestingly, we saw few differences in species richness or microbiome community structure in mosquitoes caught in either trap. Co-occurrence and co-exclusion analysis identified 116 pairwise interactions substantially increasing the list of bacterial interactions observed in mosquitoes. Networks generated from the microbiome of Ae. aegypti often included highly interconnected hub bacteria. There were several instances where co-occurring bacteria co-excluded a third taxa, suggesting the existence of tripartite relationships. Several associations were observed in multiple species or in field and laboratory-reared mosquitoes indicating these associations are robust and not influenced by environmental or host factors. To demonstrate that microbial interactions can influence colonization of the host, we administered symbionts to Ae. aegypti larvae that either possessed or lacked their resident microbiota. We found that the presence of resident microbiota can inhibit colonization of particular bacterial taxa. Our results highlight that microbial interactions in mosquitoes are complex and influence microbiome composition.

The ability of human nuclear DNA to cause false positive low-abundance heteroplasmy calls varies across the mitochondrial genome

BackgroundLow-abundance mutations in mitochondrial populations (mutations with minor allele frequency ≤ 1%), are associated with cancer, aging, and neurodegenerative disorders. While recent progress in high-throughput sequencing technology has significantly improved the heteroplasmy identification process, the ability of this technology to detect low-abundance mutations can be affected by the presence of similar sequences originating from nuclear DNA (nDNA). To determine to what extent nDNA can cause false positive low-abundance heteroplasmy calls, we have identified mitochondrial locations of all subsequences that are common or similar (one mismatch allowed) between nDNA and mitochondrial DNA (mtDNA).ResultsPerformed analysis revealed up to a 25-fold variation in the lengths of longest common and longest similar (one mismatch allowed) subsequences across the mitochondrial genome. The size of the longest subsequences shared between nDNA and mtDNA in several regions of the mitochondrial genome were found to be as low as 11 bases, which not only allows using these regions to design new, very specific PCR primers, but also supports the hypothesis of the non-random introduction of mtDNA into the human nuclear DNA.ConclusionAnalysis of the mitochondrial locations of the subsequences shared between nDNA and mtDNA suggested that even very short (36 bases) single-end sequencing reads can be used to identify low-abundance variation in 20.4% of the mitochondrial genome. For longer (76 and 150 bases) reads, the proportion of the mitochondrial genome where nDNA presence will not interfere found to be 44.5 and 67.9%, when low-abundance mutations at 100% of locations can be identified using 417 bases long single reads. This observation suggests that the analysis of low-abundance variations in mitochondria population can be extended to a variety of large data collections such as NCBI Sequence Read Archive, European Nucleotide Archive, The Cancer Genome Atlas, and International Cancer Genome Consortium.

CoCo: An application to store High-Throughput Sequencing data in compact text and binary file formats

The storage, manipulation, and especially internet transfer of large amounts of data produced by High-Throughput Sequencing (HTS) instruments present major obstacles utilizing the full potential of this promising technology. The current standard is based on storing all data, which are produced in text (FASTQ and FASTA) and often stored in binary (SRA and BAM) formats. To date, significant effort has been devoted to efficiently compressing these cumbersome sequencing data sets in their existing formats. However, given the substantial improvements in the quality of HTS data, we believe that if one can afford to exclude low quality data and read headers, new much more compressed data formats can be used to reduce size of HTS data files by at least two orders of magnitude. Here we present several examples of file formats specifically designed to store only high quality sequencing reads in space efficient text and binary form. The basic principles used to decrease file size include storage of only one copy of a sequence when reads are present in multiple copies; alphabetical sorting of all reads and storage of only the differences (suffixes) between consecutive reads; and optimization of the number of bits/bytes required to store the information in binary formats. While file size reduction depends on properties of the sequencing data, the size of the resulting files can be as low as 0.1 %-5% of the original FASTQ, SRA, or BAM files. The greatest advantage of the proposed formats however, is based on its time and memory efficiency. The time required to convert reads from FASTQ/FAST A files into the proposed formats is up to 10 times faster than gzip and SRA. The conversion of files in the proposed formats back to FAST A is limited only by the time required to read the file from the hard drive. We present the source code of the C++ object (class) implemented to store, sort, and perform I/O operations with equal length subsequences; and two executable LINUX command line applications (CoCo and CoCo-PIus) able to work with all types of sequencing data including paired-end and flexible size reads. Source code, Linux executables, as well as user manual can be downloaded from http://bgl.utmb.edu/publications/34cocoplus.

Using High Throughput DNA Sequencing to Evaluate the Accuracy of Serial Dilution Based Tests of Microbial Activities in Oil Pipelines

Microbial activities have detrimental effects on industrial infrastructure. If not controlled, microbial presence can result in corrosion, biofilm formation, and product degradation. Serial dilution tests are routinely used for evaluating presence and abundance of microorganisms by diluting samples and culturing microbes in specific media designed to support microorganisms with particular properties, such as sulfate reduction. A high-throughput sequencing approach was used to evaluate changes in microbial composition during four standard serial dilution tests. Analysis of 159 isolates revealed significant differences in the microbial compositions of sequential serial dilution titers and identified several cases where: (a) bacteria known to have a detrimental metabolic function (such as acid production) were lost in the serial dilution medium designed to test for this function; (b) bacteria virtually absent in the original sample became dominant in the serial dilution medium. These observations raise concerns regarding the accuracy and overall usefulness of serial dilution tests.

Draft genome sequence of Zobellella denitrificans ZD1 (JCM 13380), a salt-tolerant denitrifying bacterium capable of producing poly(3-hydroxybutyrate)

ABSTRACT Zobellella denitrificans ZD1, isolated from sediments of an estuarine mangrove ecosystem in Taiwan, exhibits growth-associated production of biopolymer poly(3-hydroxybutyrate) (PHB). This work reports the 4.05-Mbp draft genome sequence of Z. denitrificans ZD1, consisting of 217 contigs with a G+C content of 63.8% and 3,672 protein-coding sequences.