Can you increase the microbiota in an insect?

Can you increase the microbiota in an insect?

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I know you can suppress the microbiota of an insect with antibiotics, but I'm am looking for a way to increase it. For example, can I feed an insect with something that helps boost the bacteria like agar or something.

The answer is absolutely yes. There are 3 main ways:

  1. Faecal transplant: faecal sample from other insect could be transplanted in order to colonize the gastro-intestinal tract
  2. Injection of bacterial culture into the desired tissue
  3. Use of functional foods to increase the abundance of specific bacterial genus.

See this review for more detail

9 Jan 2017: Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR (2017) Correction: Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History. PLOS Biology 15(1): e1002587. View correction

Phylosymbiosis was recently proposed to describe the eco-evolutionary pattern, whereby the ecological relatedness of host-associated microbial communities parallels the phylogeny of related host species. Here, we test the prevalence of phylosymbiosis and its functional significance under highly controlled conditions by characterizing the microbiota of 24 animal species from four different groups (Peromyscus deer mice, Drosophila flies, mosquitoes, and Nasonia wasps), and we reevaluate the phylosymbiotic relationships of seven species of wild hominids. We demonstrate three key findings. First, intraspecific microbiota variation is consistently less than interspecific microbiota variation, and microbiota-based models predict host species origin with high accuracy across the dataset. Interestingly, the age of host clade divergence positively associates with the degree of microbial community distinguishability between species within the host clades, spanning recent host speciation events (

1 million y ago) to more distantly related host genera (

108 million y ago). Second, topological congruence analyses of each group's complete phylogeny and microbiota dendrogram reveal significant degrees of phylosymbiosis, irrespective of host clade age or taxonomy. Third, consistent with selection on host–microbiota interactions driving phylosymbiosis, there are survival and performance reductions when interspecific microbiota transplants are conducted between closely related and divergent host species pairs. Overall, these findings indicate that the composition and functional effects of an animal's microbial community can be closely allied with host evolution, even across wide-ranging timescales and diverse animal systems reared under controlled conditions.


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In this study, we aimed to gain understanding of the advantages of symbiosis for the olive fruit fly B. oleae by integrating various sources of genetic information on its microbial gut community. This led to the identification, by 16S rRNA gene sequencing and culture-dependent methods, of a novel facultative member of the gut microbiota, Tatumella sp. TA1, which associates with Mediterranean populations of B. oleae throughout the lifecycle. Tatumella sp. TA1 is phylogenetically distinct from the US-restricted facultative symbiont Enterobacter sp. OLF, highlighting the population-dependent nature of gut microbiota composition in the olive fruit fly. Comparative genomics indicated that the obligate symbiont Ca. E. dacicola, as well as Tatumella sp. TA1 and Enterobacter sp. OLF, which are all stable components of the B. oleae microbiota throughout the olive fruit fly lifecycle, encode genes that allow the use of urea as a nitrogen source. The hydrolysis of urea to ammonia is encoded by genes with distinct phylogenetic origins in each organism: HGT of a urease operon in Ca. E. dacicola, an endogenous urease operon in Enterobacter sp. OLF, and an endogenous alternative enzymatic machinery (urea carboxylase and allophanate hydrolase) in Tatumella sp. TA1. These findings provide a potential mechanistic basis for previous experimental evidence of gut microbiota-mediated dietary nitrogen provisioning during adulthood, but emphasize the need for experimental validation of metabolic cross-feeding between the host and obligate symbiont. A hypothesis in this direction, warranting focus in future studies, is that a VirB-like T4SS encoded by Ca. E. dacicola, but missing from the genomes of Enterobacter sp. OLF and Tatumella sp. TA1, may facilitate specific interactions with the host at the symbiotic interface. Besides T4SS, our study further highlighted extracellular surface structures, encoded in the genomes of the obligate and the facultative symbionts, as mediators of DNA transfer: Detection of a plasmid shared by geographically distinct Ca. E. dacicola and Tatumella sp. TA1, along with the horizontal acquisition of genes important to symbiosis function (urease in this study, genes related to amino-acid degradation in Estes, Hearn, Agrawal, et al. 2018) and the abundance of MGEs in the obligate symbiont genome, indicate that previous and on-going genetic exchanges between gut community members are important determinants of symbiotic interactions with B. oleae.

New tools and methods to dissect holobiont components

The first issue regarding holobiont research is the characterization of microbiomes in hosts. While sequencing technologies are progressing rapidly both in the quantity and the length of sequences, allowing to generate large full-genome metagenomics datasets (the whole genome sequencing of the host and associated microbial communities), bioinformatics is currently challenged with the difficulty to sort reads from the different entities forming the holobiont and to explore microbial diversity at a fine resolution scale (i.e., strain-resolved metagenomics [14]). In this special issue, Guyomar and co-workers [15] present a methodological framework to characterize microbial diversity associated with aphid populations at species and intraspecies scales. Using this approach, on metagenomics read sets, these authors were able to reveal unsuspectedly high genomic diversity in different symbiont taxa both between and within their individual hosts. Meng and colleagues [16] propose a new method to separate the transcriptome of each member of the holobiont using metatranscriptomic datasets, allowing to access for the first time the whole functional diversity associated with the host and its microbiota. Young and co-workers [17] also used metatranscriptomics to characterize the microbiota associated with the wild grass Holcus lanatus and its changes in relation with soil types. In particular, they detected shifts in arbuscular mycorrhizal fungal communities according to phosphorus availability in the soil. Cregger and colleagues [18] characterize the respective effects of plant tissues and genotypes on microbial community structure in poplars. They found that microbial diversity varied primarily according to plant organs although fungal communities were also impacted by plant genotype.


Insect collection and rearing

In April and May 2012, adult Heliconius erato butterflies were collected from a wild population as they visited flowers in Parque Nacional Soberanía, Panama (9°7′20″N, 79°42′54″W), for which permission was provided by the Panamanian Environmental Authority (ANAM) under permit #SE/A-92-11. Voucher specimens have been deposited at the Fairchild Invertebrate Museum of the University of Panama. Thirteen individuals (nine males and four females) were stored at −20°C directly after field collection. All samples described below were preserved in the same manner.

We relocated nine additional wild-caught females to a nearby insectary, where they were housed under semi-natural conditions in separate mesh cages. They were supplied with flowers frequently visited by wild H. erato in this area (Psychotria elata, Lantana camara), and with an autoclaved sucrose and honeybee pollen solution. Potted Passiflora biflora, the main host plant of the specialist H. erato [34], were placed in the cages to elicit oviposition. Eggs were removed and placed individually in plastic cups. The parental females were sampled after a sufficient number of eggs were obtained, corresponding to appx. 2–4 weeks in captivity. Given that females of H. erato only very rarely mate more than once in the wild [35], it is likely that the individuals in each brood are full siblings.

We reared larvae on plant material collected from potted P. biflora grown in an open-air greenhouse near the forest. One larva per brood was sampled two days into the fifth stadium, while it was actively feeding, as was the frass it had produced that day. Pupae were sampled midway through the pupal stage. Newly emerged adults were sampled immediately after they had excreted meconium. The rest of the adults were kept under identical conditions as described above for wild-caught parental females. One male and one female per brood were sampled four days after eclosion, by which point both sexes of this species have reached sexual maturity.

Sample processing

We used whole, surface-sterilized insects to describe the dominant bacterial taxa associated with the internal portion of the body. Insects were rinsed in sterile molecular-grade water (Sigma-Aldrich), soaked in 70% ethanol for 30 s followed by 10% bleach for 30 s, and rinsed again in sterile water. For adults, wings were clipped where they met the thorax prior to sterilizing the body. After surface sterilization the samples were ground under liquid N2 with single-use, sterile mortar and pestles (Fisher Scientific). Frass samples were not surface sterilized.

DNA sequencing and data processing

Bacterial communities were characterized using barcoded Illumina sequencing of 16S rRNA genes. Total DNA was extracted from homogenized material using the MoBio PowerSoil kit as described previously [36]. We used the primer pair 515F/806R to amplify the V4 region of the 16S rRNA gene, and PCR conditions followed those described previously [37]. Amplicons were sequenced on the Illumina MiSeq platform, resulting in an average of 1779 150-bp reads per sample after filtering with default parameters for sequence length and minimum quality score in QIIME v. 1.6.0 [38]. Sequences were clustered into operational taxonomic units (hereafter, “phylotypes”) at the 97% similarity level by reference-based picking with the QIIME implementation of UCLUST [39] against the October 2012 release of the Greengenes database [40] with remaining sequences clustered de novo. The Ribosomal Database Project (RDP) classifier [41] set at a minimum confidence level of 0.5 was used to assign taxonomy to the phylotypes. The centroid (seed sequence) used by UCLUST was chosen as the representative sequence for each phylotype. With representative sequences from the 10 most abundant phylotypes across all H. erato samples, we used SeqMatch to find the best high-quality matches ≥1200 bp in the curated RDP 16S database [42].

Because this primer set can amplify non-bacterial rRNA gene sequences, phylotypes identified by the RDP classifier as chloroplast or mitochondrial 16S rRNA (which represented 24% of the sequences on average) were removed prior to downstream analyses. In order to standardize sequencing effort, all samples were rarefied by randomly selecting 500 sequences per sample. As the samples from which we obtained fewer than 500 bacterial sequences were excluded from further analysis, there are fewer replicates for pupae than were initially collected. This sequencing depth has been shown to be sufficient for detecting biological patterns in insect-associated bacterial communities [43] and other community types [44]. Amplicon sequences and associated metadata from this study are publicly available in the EMBL-EBI database ( under accession number ERP003400.

Statistical analyses

We used nonparametric Kruskal-Wallis tests in R v. 3.0.0 [45] to determine whether there were significant differences in community richness or the relative abundances of individual bacterial taxa (families or phylotypes) with a Bonferroni correction applied to account for multiple comparisons. The family-level tests were conducted only on dominant families, defined as those contributing at least a median 2% of the sequences within any of the factor levels. To compare community composition between sample types, we used vegan [46] to compute a Bray-Curtis dissimilarity matrix after Hellinger transformation of the phylotype count data. Subsequent multivariate analyses were conducted in PRIMER [47]. Variation among samples in their bacterial taxonomic composition was visualized using constrained principal coordinates analyses [48]. We used Mantel tests to determine whether patterns of compositional dissimilarities among larvae were correlated with dissimilarities among their frass. Permutational multivariate ANOVA tests [49] were used to assess differences in bacterial community composition associated with several sample categories, with tests of life stage or frass versus larvae run using sample type as a fixed effect. Variation in the dissimilarity matrix linked to the level of relatedness among captive adults was tested using family as a random effect. Lastly, for all adult butterflies, a two-factor design was used to test the effects of captivity/rearing status and sex (both fixed).

Graduate students

Danielle Rutkowski, PhD Candidate in the Entomology Graduate Group

Email: dmrutkowski ‘at’ ucdavis ‘dot’ edu

Co-advised with Rick Karban

Danielle is an entomology PhD student interested in the relationships between bees and microbes. She completed her bachelor’s degree at Cornell University, where she studied how the relationship between mycorrhizal fungi and their host plants impacts insect herbivores. At UC Davis, she studies how bumble bees interact with the microbes, particularly fungi, in their environment, and how these relationships impact bee health.

Shawn Christensen, PhD Candidate in the Microbiology Graduate Group

Email: smchristensen ‘at’ ucdavis ‘dot’ edu

Shawn is an evolutionary biologist turned microbiologist, broadly interested in microbial interactions/symbioses with plant-pollinator systems, weird evolutionary traits, and crosswords. They obtained a BS from University of Wisconsin-Madison in Evolutionary Biology, where they did research on reducing ecological impacts of phosphorus runoff, ethnobotany and domestication traits in Brassica rapa, botanical field excursions of all kinds, and the evolution of chemical sets in the early origins of life. In the Vannette lab, Shawn is currently studying nectar-dwelling Acinetobacter and other nectar microbes and their potential influences on pollen for nutrient procurement, as well as the metabolomics of solitary bee pollen provisions.

Dino Sbardellati, PhD Student in the Microbiology Graduate Group

email: dlsbardellati ‘at’ ucdavis ‘dot’ edu

Dino is a microbiologist interested in understanding how microbial ecology shapes macroscale ecology. He received a BA in biology from Sonoma State University and an MS in Bacteriology from the University of Wisconsin Madison. Dino has worked on projects exploring how reintroduced Tule elk modulate terrestrial arthropod populations, how antibiotic treatment impacts gut microbial communities in Passalid beetles, and how diet effects bovine rumen microbial communities. In the Vannette lab, Dino’s work deals with studying the bacteriophage (viruses which target bacteria) communities associated with the bumble bee gut and how phages shape gut microbial communities. In his spare time Dino enjoys catching, pinning, and drawing insects, cooking pizza, and making art.


The microbiome is the collection of all microbes, such as bacteria, fungi, viruses, and their genes, that naturally live on our bodies and inside us. Although microbes require a microscope to see them, they contribute to human health and wellness in many ways. They protect us against pathogens, help our immune system develop, and enable us to digest food to produce energy. Some microbes alter environmental chemicals in ways that make them more toxic, while others act as a buffer and make environmental chemicals less toxic.

The critical role of the microbiome is not surprising when considering that there are as many microbes as there are human cells in the body. 1 The human microbiome is diverse and each body site &ndash for example, the gut, skin, and oral and nasal cavities &ndash is home to a unique community of microbes. 2 A person&rsquos core microbiome is formed in the first few years of life, but can change over time in response to different factors including diet, medications, and a variety of environmental exposures.

Differences in the microbiome may lead to different health effects from environmental exposures and may also help determine individual susceptibility to certain illnesses. Environmental exposures can also disrupt a person&rsquos microbiome in ways that could increase the likelihood of developing conditions such as diabetes, obesity, cardiovascular diseases, allergies, and inflammatory bowel disease. 3

What is NIEHS Doing?

NIEHS studies the microbiome to gain a better understanding of its complex relationships with the environment, and how these interactions may contribute to human health and disease. This knowledge could help us revolutionize the way new chemicals are tested for toxicity, and design prevention and treatment strategies for diseases that have environmental causes.

NIEHS-supported research related to the microbiome includes the environmental factors described below.

Chronic stress &ndash NIEHS researchers found chronic stress disturbs the gut microbiome in mice, triggering an immune response and promoting the development of colitis, a chronic digestive disease characterized by inflammation of the inner lining of the colon. 4

Artificial sweeteners &ndash A NIEHS&ndashfunded study found sucralose, a widely&ndashused artificial sweetener, changes the gut microbiome in mice and may increase the risk of developing chronic inflammation. 5 In a separate study, they found that acesulfame&ndashpotassium, another artificial sweetener, induced weight gain in male, but not female, mice. 6

Diet &ndash NIEHS researchers showed a high&ndashfat diet shaped the gut microbiome of mice in a way that predisposed them to gain weight and develop obesity. 7

Caesarean delivery &ndash NIEHS&ndashfunded research indicates the way a newborn enters the world, by C-section or natural birth, and what is eaten, formula or breast milk, during the first six weeks of life may affect the type of microbes in the gut microbiome. 8

Antimicrobials &ndash A NIEHS&ndashfunded study examined the effects of triclosan, a common ingredient in antimicrobial products, on the gut microbiome in mice. Mice that consumed triclosan through drinking water displayed an uptick in bacterial genes related to the stress response, antibiotic resistance, and heavy metal resistance. 9

Arsenic &ndash NIEHS&ndashfunded researchers showed arsenic exposure in mice changed the gut microbiome and altered molecular pathways in bacteria that are important to biological functions like DNA repair. 10

Pathogens &ndash A NIEHS&ndashfunded study showed pathogens, microbes that cause infection or disease, in oral mouthwash samples were associated with pancreatic cancer in humans. 11

Pesticides &ndash A NIEHS&ndashfunded study reported exposure to the widely used agricultural insecticide diazinon changed the gut microbiome of mice. 12 These changes were more pronounced in male than female mice, providing insight into previously reported sex&ndashspecific effects of this toxicant on the nervous system.

Ultrafine particles &ndash NIEHS&ndashfunded research found breathing ultrafine particles, a component of air pollution, altered the gut microbiome and changed lipid metabolism in mice with atherosclerosis. 13


The past several years have witnessed the increasingly rapid adoption of NGS technologies to address questions relevant to the biology and evolution of disease vectors. WGS efforts have resulted in full genome sequences for most of the major arthropod vector species. For more neglected species, de novo transcriptome assembly from RNA-seq data has been sufficient to reveal coding sequences, SNPs, and differential expression. As these data continue to be generated, they should be made available to other researchers through public databases such as NCBI’s Sequence Read Archive (SRA), the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ). In this way, the work of one research group not only informs the study at hand but can also be mined to address innumerable future questions (Fig. 2). Furthermore, the field of vector biology would be well served by the adoption of a set of common data standards that could provide a basic framework to ensure that high-quality, readily accessible datasets will be optimized in their utility to other researchers. This could be accomplished by first examining the standards that groups such as the Immunogenomic Next Generation Sequencing Data Consortium ( have put forward. In this way, the true power of large repositories of NGS data can be fully utilized so that the data are both particularly and cumulatively informative, becoming a gift that keeps on giving.

We have attempted to highlight the growing impact of NGS on vector biology. Nonetheless, it is clear that too few studies have utilized sequencing-based approaches despite their rapidly expanding accessibility. It will probably be some time before the field of medical entomology embraces the comprehensiveness and agnosticism offered by NGS assays. Until that happens, the potential benefits of data integration among studies will remain unrealized, and the myriad potential of this 21 st century research strategy will remain mired within the experimental paradigms of the 20 th century.