A novel approach, based on BLSOMs (Batch Learning Self-Organizing Maps), to the microbiome analysis of ticks

Abstract

Ticks transmit a variety of viral, bacterial and protozoal pathogens, which are often zoonotic. The aim of this study was to identify diverse tick microbiomes, which may contain as-yet unidentified pathogens, using a metagenomic approach. DNA prepared from bacteria/archaea-enriched fractions obtained from seven tick species, namely Amblyomma testudinarium, Amblyomma variegatum, Haemaphysalis formosensis, Haemaphysalis longicornis, Ixodes ovatus, Ixodes persulcatus and Ixodes ricinus, was subjected to pyrosequencing after whole-genome amplification. The resulting sequence reads were phylotyped using a Batch Learning Self-Organizing Map (BLSOM) program, which allowed phylogenetic estimation based on similarity of oligonucleotide frequencies, and functional annotation by BLASTX similarity searches. In addition to bacteria previously associated with human/animal diseases, such as Anaplasma, Bartonella, Borrelia, Ehrlichia, Francisella and Rickettsia, BLSOM analysis detected microorganisms belonging to the phylum Chlamydiae in some tick species. This was confirmed by pan-Chlamydia PCR and sequencing analysis. Gene sequences associated with bacterial pathogenesis were also identified, some of which were suspected to originate from horizontal gene transfer. These efforts to construct a database of tick microbes may lead to the ability to predict emerging tick-borne diseases. Furthermore, a comprehensive understanding of tick microbiomes will be useful for understanding tick biology, including vector competency and interactions with pathogens and symbionts

Variation in the Microbiota of Ixodes Ticks with Regard to Geography, Species, and Sex

“…While there have been many studies of tick microbiomes (11)(12)(13)(14)(15)(16)(17), relatively little is known about the microbiota of Ixodes ticks. Efforts to identify microbial commensals and symbionts have been made using culture-based methods (18), cloning, and sequencing of gel-purified PCR products (4).…”

An artificial intelligence approach fit for tRNA gene studies in the era of big sequence data

“…When analyzing a dataset composed mainly of sequences shorter than 100 bp, this strategy is useful since conventional phylogenetic tree methods cannot be properly applied to most short sequences; it is impossible to construct reliable phylogenetic trees for most of these short sequences. If the dataset is composed mainly of sequences longer than 500 bp, BLSOMs with tri-and tetranucleotide compositions in all genomic fragments should be more suitable than tDNA-BLSOM, because all genomic sequences are informative Nakao et al, 2013).…”

“…Various unsupervised data mining methods, such as K-means clustering and Fuzzy Art (Forgy, 1965;Carpenter et al, 1991;Hastie et al, 2009), have been developed; and we have previously developed an unsupervised clustering method, BLSOM (batch-learning self-organizing map) (Kanaya et al, 2001;Abe et al, 2003Abe et al, , 2005Kikuchi et al, 2015), which can analyze more than ten million genomic sequences simultaneously and allows acquisition of a wide range of knowledge from big sequence data. For example, BLSOM with oligonucleotide (e.g., tetranucleotide) composition can cluster genomic sequence fragments (e.g., 1-kb sequences) according to phylotype, and has thus succeeded in phylogenetic classification of a large number of metagenomic sequences (Uchiyama et al, 2005;Uehara et al, 2011;Nakao et al, 2013).…”

Emerging Horizons for Tick-Borne Pathogens: From the ‘One Pathogen–one Disease’ Vision to the Pathobiome Paradigm

“…Within the last few years, the rapid development of NGS methods has revolutionized the research field of epidemiology and diagnosis of infectious diseases, thus facilitating complete screening of pathogens within their hosts, discovery of new pathogens or the detection of unexpected ones. NGS has recently been successfully used to identify the bacterial communities associated with I. ricinus [35][36][37][38] based on the amplification and sequencing of hypervariable regions of the 16S rRNA encoding genes (metagenomic profile), revealing a highly diverse microbial community (108 genera representing all bacterial phyla). As expected, those approaches have allowed detection without a priori established tick-borne pathogens, such as the Borrelia, Anaplasma, Coxiella, Francisella or Rickettsia genus.…”