16S rRNA gene amplicon data of midgut microbial diversity in German cockroach, Blattella germanica

Abstract

Objectives

Blattella germanica, commonly known as German cockroach, is a globally prevalent household pest. The gut microbiota of B. germanica is influenced by multiple factors, yet their interactive effects remain poorly understood compared with other insect taxa. Midgut-specific microbial communities also remain understudied relative to whole gut or hindgut microbiota. In this study, 16S rRNA gene sequencing datasets were generated from midgut samples collected across varying life stages (adult and nymph), sexes (male and female), environmental origins (laboratory-reared and field-collected), and physiological states (fed and starved).

Data description

Genomic DNA was extracted using the HiYield Genomic DNA isolation kit and amplified targeting the V3-V4 hypervariable regions of the 16S rRNA gene using primer pair 338 F/806R. Raw sequences reads were subsequently analyzed using QIIME2 pipeline following quality control and host decontamination. Taxonomic classification consistently identified the bacterial families Lactobacillaceae, Desulfovibrionaceae, Lachnospiraceae, Dysgonomonadaceae, Christensenellaceae, and Rikenellaceae across all treatment groups. Diversity indices (Shannon, Simpson, and Chao1) showed similar trends across samples, indicating stable patterns of microbial diversity and richness. These datasets support the exploration of specific midgut-associated taxa as potential targets for biological control strategies against B. germanica.

Objective

Blattella germanica, commonly known as German cockroach, is a globally prevalent household pest. Its rapid infestation potential is attributed to the high reproductive potential [1]. The only known obligate endosymbiont in B. germanica is Blattabacterium, which coexists with a diverse community of gut-associated symbionts distributed along the alimentary canal. These gut symbionts play major roles in nutrition supplementation, defense against biotic threats, immunity modulation, and pesticide degradation [2].

Sequencing-based approaches are essential tools for understanding the dynamics of microbial communities and investigating host-microbe interactions. In particular, 16S rRNA amplicon sequencing enables taxonomic profiling of the gut microbiota with high resolution. This has facilitated the identification of conserved microbiota taxa in B. germanica, defined by their consistent presence across individuals or populations, and presumed to play fundamental roles in host physiology and resilience [3].

B. germanica demonstrates a complex interplay of factors, such as developmental stage, sex, physiological condition, and environmental origin that shape their gut microbial communities [4–8]. While these variables have been individually investigated, their interactive effects remain largely unexplored. Furthermore, despite the anatomical compartmentalization of the gut in B. germanica, research has predominantly focused on the whole gut or hindgut microbiota, with comparatively limited attention given to the midgut-specific microbial assemblages.

Here, we present 16S rRNA amplicon gene sequencing datasets from the midgut of B. germanica, sampled across different life stages (adult and nymph), sexes (male and female), environmental origins (laboratory-reared and field-collected), and physiological states (fed and starved). By capturing this natural variability, our study emphasizes the ecological authenticity of the cockroach gut microbiome, which represents a well-established and stable microbial community. These data provide a valuable resource for exploring midgut microbial dynamics and may inform the development of novel microbial-based pest management strategies.

Data description

Sample collection

The experimental design included two strains of Blattella germanica (laboratory and field). To accommodate the physiological variation, two experimental conditions were established for laboratory strain, including food-fed (L) and 5-day starvation (S). The field strain (F) was collected from at a restaurant in Bayan Lepas, Penang. Different developmental stages, including male (M), female (F), and nymph (N), were examined. Sample codes consisted of three components: (1) strain or treatment condition (L/S/F); (2) sex or developmental stage (M/F/N); (3) replicate number (1–3).

For each experimental group (defined as each strain or treatment condition combined with sex/developmental stage), three independent biological replicates were prepared (n = 3). Each biological replicate comprised a pooled sample of midguts dissected from three randomly selected individuals, with three pools generated per group (3 individuals per pool × 3 pools per group), totaling nine individuals per experimental group [9].

Each cockroach individual was surface-sterilized with 70% ethanol before dissection. The alimentary canal was removed, and the midgut was isolated, longitudinally dissected, and rinsed with phosphate-buffered saline (PBS) to isolate the fat bodies [7]. Pooled midgut tissues from each biological replicate were used for DNA extraction to reduce individual microbial variation [9].

DNA extraction and sequencing

Genomic DNA was extracted using the HiYield Genomic DNA isolation kit (Real Biotech Corporation, Taiwan) in accordance with manufacturer’s protocol. The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified using the conserved primer set 338F (5’-ACTCCTACGGGAGGCAGCA-3’) and 806R (5’-GGACTACHVGGGTATCTAAT-3’). Pair-end sequencing (PE250) was performed using an Illumina MiSeq platform following the standard protocols. A reagent-only negative control sample (N1) was included to evaluate and confirm the purity of the experimental samples.

Data analyses

Raw sequence reads were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME2) software (version 2024.5) [10]. Preliminary quality control of the raw sequences was performed using FastQC (version 0.12.1) [11]. A total of 4,222,315 clean sequence reads with more than 89.99% of bases had Phred scores above 30 was obtained. Denoising, quality filtering, and chimera removal were conducted using the DADA2 algorithm [12], retaining an average of 87,672 ± 27,901 reads per sample.

The taxonomy file contained 9,390 ASVs, whereas the feature table contained 9,051 ASVs due to zero-abundance variants. After removing 148 non-bacterial ASVs from the taxonomy file, 9,242 bacterial ASVs remained. In the feature table, 182 contaminant ASVs detected in the negative control were removed using the decontam tool [13], and 17 zero-abundance ASVs were further removed. After synchronizing the feature table, taxonomy assignments, and representative sequences, 8,670 high-quality bacterial ASVs remained for ecological analyses (Data files 1–2, Table 1).

Table 1.

Overview of data files/data sets

Label	Name of data file/data set	File types (file extension)	Data repository and identifier (DOI or accession number)
Data set 1	Amplicon sequencing of Blattella germanica midgut.	Fasta files (.fastq)	Sequence Read Archive from NCBI repository, accession number: SRP559039 (https://identifiers.org/insdc.sra:SRP559039) [15]
Data file 1	Sample metadata: 16S rRNA gene amplicon sequencing of midgut microbial diversity in German cockroach, Blattella germanica.	Microsoft Excel file (.xlsx)	Figshare (10.6084/m9.figshare.29833028) [16]
Data file 2	Clean feature table: 16S rRNA gene amplicon sequencing of midgut microbial diversity in German cockroach.	Microsoft Excel file (.tsv)	Figshare (10.6084/m9.figshare.31023931) [17]
Data file 3	Top 10 most abundant bacterial taxa at the family level recovered from the midgut of German cockroach.	Text file (.pdf)	Figshare (10.6084/m9.figshare.31743514) [18]
Data file 4	Sequencing quality and diversity indices of midgut microbiome in German cockroach.	Text file (.pdf)	Figshare (10.6084/m9.figshare.31743529) [19]

Taxonomic classification was performed using a Naïve Bayes classifier trained on the SILVA 138 reference database (version 1.4.2) within QIIME2 [14]. Preliminary analyses revealed the successful classification of 1,252 unique genera corresponding to 606 families, 344 orders, 148 classes, and 47 phyla. The bacterial families Lactobacillaceae, Desulfovibrionaceae, Lachnospiraceae, Dysgonomonadaceae, Christensenellaceae, and Rikenellaceae were consistently detected across all treatment groups, suggesting they constitute the conserved midgut microbiome of B. germanica (Data file 3, Table 1). The range of sample sizes for genus level ASV after quality control was observed to be 11,549–113,563 reads. The Shannon, Simpson, and Chao1 diversity indices exhibited comparable trends across all samples, indicating consistent patterns in microbial community diversity and richness (Data file 4, Table 1). Raw sequencing data from 28 samples were deposited in the NCBI Sequence Read Archive under the accession number SRP559039 (Data set 1, Table 1).

Limitations

Complete removal of fat bodies during dissection was not feasible because of their close anatomical association with midgut tissues. Therefore, sequences corresponding to obligate and facultative endosymbionts, specifically Blattabacterium and Rickettsiella, were identified and removed during bioinformatic processing to minimize bias in midgut microbiota characterization.

The sequencing data allowed the identification of conserved midgut microbiome members only up to the family level. This could be attributed to the limitations in taxonomic resolution associated with 16S rRNA V3–V4 amplicon sequencing. Consequently, these factors limited the ability to pinpoint specific microbial genera that could serve as precise targets for downstream functional studies or biological control strategies. Further investigations using higher-resolution approaches, such as shotgun metagenomic sequencing, may provide improved taxonomic resolution and facilitate the identification of core microbiota at lower taxonomic levels.

Abbreviations

PBS

Phosphate-buffered saline

QIIME2

Quantitative Insights Into Microbial Ecology

Author contributions

Conceptualization, A.H.A.M.; methodology, A.H.A.M. and K.Y.V.; software, K.Y.V.; validation, A.H.A.M. and K.Y.V.; formal analysis, K.Y.V.; investigation, A.H.A.M. and K.Y.V.; resources, A.H.A.M.; data curation, A.H.A.M. and K.Y.V.; writing—original draft preparation, A.H.A.M. and K.Y.V; writing—review and editing, A.H.A.M. and K.Y.V; visualization, A.H.A.M. and K.Y.V.; supervision, A.H.A.M.; project administration, A.H.A.M.; funding acquisition, A.H.A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Industry Research Grant, grant number R504-LR-GAL007-0000001030-E136.

Data availability

The data described in this Data note can be freely and openly accessed on Figshare under 10.6084/m9.figshare.31023931; 10.6084/m9.figshare.31743514, and 10.6084/m9.figshare.31743529. The raw amplicon sequences can be freely and openly accessed on NCBI under accession number PRJNA1211809. Please see Table 1 and reference [15] for details and links to the data.

Declarations

Ethics approval and consent to participate

None.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.