Metagenomic Approach to Characterizing Disease Epidemiology in a Disease-Endemic Environment in Northern Thailand

Takhampunya, Ratree; Korkusol, Achareeya; Pongpichit, Chalermpol; Yodin, Komsan; Rungrojn, Artharee; Chanarat, Nitima; Promsathaporn, Sommai; Monkanna, Taweesak; Thaloengsok, Sasikanya; Tippayachai, Bousaraporn; Kumfao, Naruemon; Richards, Allen L.; Davidson, Silas A.

The effect of environmental factors on the prevalence and transmission of scrub typhus among populations studied was also evaluated. Rainfall (mm) and temperature in 2017 from Nan province was acquired from the Thai Meteorological Department 4 . The rainy season started from late April through September corresponding to increased rainfall (mm) recorded during this period of the year ( Figure 8A ). The temperature was relatively constant throughout the year except a slight decrease at the end and the beginning of the year (October-March). The chigger index (no. of chigger/number of hosts collected) and the O. tsutsugamushi infection rates in rodents and chiggers were examined each month ( Figure 8B) . The chigger index slightly increased at the beginning of the year and peaked around June-September. O. tsutsugamushi in chigger could be found almost every month and the highest infection rates were in March. On the other hand, the number of UFI patients with IgM antibody against scrub typhus slowly increased from April to October and peaked at the end through the beginning of the year (November-February) ( Figure 8C) . O. tsutsugamushi was also detected by PCR from patient whole blood samples but the number did not correlate well with the number of patients having IgM seroactivity. Interestingly, the number of patients with rickettsiosis IgM increased sharply from April to September and this high number continued through the rest of the year. Likewise, the same pattern was observed with their corresponding IgGs although the number of patients with scrub typhus IgG and its titer seemed to be higher than that observed for rickettsiosis (TG and SFG) ( Figure 8C and Table 5 ). FIGURE 7 | Venn diagram (Oliveros et al., 2007 (Oliveros et al., -2015 indicates the bacteria species shared between populations or unique to each of them (A). Bacterial species were identified on the basis of DNA sequence and phylogenetic analyses of their target genes (Supplementary Table S1 ). Rodent_ecto = ectoparasites (chiggers, ticks, fleas, and lice) collected from rodents, Domes_ecto = ectoparasites collected from domesticated mammals. Map of O. tsutsugamushi-positive samples in Bo Kluea district, Nan (B). Each dot represents only positive samples found among UFI patients (red), chigger pools (blue), and rodent populations (green). were collected twice a year (dry and wet seasons) from 2014 until the beginning of 2018 at field collection sites in Mae Charim, Phu Phiang, and Bo Kluea districts. In this study, extraction and PCR controls were included in each NGS run to exclude bacteria genera commonly found in molecular reagents, water, and other environments (Tanner et al., 1998; Goodrich et al., 2014) . Several common bacterial genera were found in controls similar to previous studies (Salter et al., 2014; Razzauti et al., 2015) . Some potential zoonotic and pathogenic bacteria were also detected in controls such as Bartonella spp., Leptospira spp., and Rickettsia spp. albeit at relatively low numbers of reads. Therefore, we applied cutoff values (number of reads detected in controls) for those bacteria detected in controls and applied these numbers to all samples in our study. Contamination likely came from crosscontamination during sample processing and carry-over between sequencing runs (Swei et al., 2013) . The NGS technique has many benefits over conventional tests since it does not require prior knowledge of the target pathogens which conventional tests m