Microbiome Studies Research Articles

DeepPhylo: Phylogeny-Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis.

Microbial data analysis poses significant challenges due to its high dimensionality, sparsity, and compositionality. Recent advances have shown that integrating abundance and phylogenetic information is an effective strategy for uncovering robust patterns and enhancing the predictive performance in microbiome studies. However, existing methods primarily focus on the hierarchical structure of phylogenetic trees, overlooking the evolutionary distances embedded within them. This study introduces DeepPhylo, a novel method that employs phylogeny-aware amplicon embeddings to effectively integrate abundance and phylogenetic information. DeepPhylo improves both the unsupervised discriminatory power and supervised predictive accuracy of microbiome data analysis. Compared to the existing methods, DeepPhylo demonstrates superiority in informing biologically relevant insights across five real-world microbiome use cases, including clustering of skin microbiomes, prediction of host chronological age and gender, diagnosis of inflammatory bowel disease (IBD) across 15 studies, and multilabel disease classification.

Expansion of a bacterial operon during cancer treatment ameliorates drug toxicity.

Dose-limiting toxicities remain a major barrier to drug development and therapy, revealing the limited predictive power of human genetics. Herein, we demonstrate the utility of a more comprehensive approach to studying drug toxicity through longitudinal study of the human gut microbiome during colorectal cancer (CRC) treatment ( NCT04054908 ) coupled to cell culture and mouse experiments. 16S rRNA gene sequencing revealed significant shifts in gut microbial community structure during oral fluoropyrimidine treatment across multiple patient cohorts, in mouse small and large intestinal contents, and in patient-derived ex vivo communities. Metagenomic sequencing revealed marked shifts in pyrimidine-related gene abundance during oral fluoropyrimidine treatment, including enrichment of the preTA operon, which is sufficient for the inactivation of active metabolite 5-fluorouracil (5-FU). preTA + bacteria depleted 5-FU in gut microbiota grown ex vivo and the mouse distal gut. Germ-free and antibiotic-treated mice experienced increased fluoropyrimidine toxicity, which was rescued by colonization with the mouse gut microbiota, preTA + E. coli , or preTA -high CRC patient stool. Finally, preTA abundance was negatively associated with fluoropyrimidine toxicity in patients. Together, these data support a causal, clinically relevant interaction between a human gut bacterial operon and the dose-limiting side effects of cancer treatment. Our approach is generalizable to other drugs, including cancer immunotherapies, and provides valuable insights into host-microbiome interactions in the context of disease. One Sentence Summary: Gut microbial enzymes can be used to predict and prevent anticancer drug toxicity.

Small amounts of misassembly can have disproportionate effects on pangenome-based metagenomic analyses.

Individual genes from microbiomes can drive host-level phenotypes. To help identify such candidate genes, several recent tools estimate microbial gene copy numbers directly from metagenomes. These tools rely on alignments to pangenomes, which in turn are derived from the set of all individual genomes from one species. While large-scale metagenomic assembly efforts have made pangenome estimates more complete, mixed communities can also introduce contamination into assemblies, and it is unknown how robust pangenome-based metagenomic analyses are to these errors. To gain insight into this problem, we re-analyzed a case-control study of the gut microbiome in cirrhosis, focusing on commensal Clostridia previously implicated in this disease. We tested for differentially prevalent genes in the Lachnospiraceae , then investigated which were likely to be contaminants using sequence similarity searches. Out of 86 differentially prevalent genes, we found that 33 (38%) were probably contaminants originating in taxa such as Veillonella and Haemophilus , unrelated genera that were independently correlated with disease status. Our results demonstrate that even small amounts of contamination in metagenome assemblies, below typical quality thresholds, can threaten to overwhelm gene-level metagenomic analyses. However, we also show that such contaminants can be accurately identified using a method based on gene-to-species correlation. After removing these contaminants, we observe that several flagellar motility gene clusters in the Lachnospira eligens pangenome are associated with cirrhosis status. We have integrated our analyses into an analysis and visualization pipeline, PanSweep, that can automatically identify cases where pangenome contamination may bias the results of gene-resolved analyses.

Novel mechanistic understanding that Lactiplantibacillus plantarum is more capable of improving the ensiling performance of wheat straw silage than xylanase by driving certain key metabolites

Microbial and enzyme additives can improve silage performance, but there is limited comparative research on the effects of microbial and enzyme additives on improving silage fermentation quality, and the underlying microbial and metabolic pathways remain unclear. This study investigated the effects without inoculants (CK treatment) or with Lactiplantibacillus plantarum (LP treatment), xylanase (XY treatment) and their combination (LPXY treatment) on the fermentation quality, as well as on the microbial communities and metabolite profiles of the wheat straw silage. The results demonstrated that the LP treatment has a better effect on improving the fermentation quality of wheat straw silage compared to other treatments, as evidenced by markedly (p < 0.05) decreased the pH (4.06), acid and neutral fiber (ANF, NDF, 23.43 and 31.69%DM), and increased the lactic acid (LA, 965.89 mg/L) and acetic acid (AA, 656.10 mg/L) concentrations. After the fermentation process, the LP treatment significantly (p < 0.05) enhanced the abundance of Lactobacillus, reduced bacterial Shannon (p < 0.05) and increased some key metabolites content. The structural equation models (SEMs) and Pearson’s correlation results proved that the LP treatment improved the wheat straw silage fermentation quality via increasing the abundance of Lactobacillus, decreasing the diversity of bacterial community and enriching the content of certain key metabolites. The present study provides mechanistic evidence that Lactiplantibacillus plantarum additive is superior to xylanase additive and their combination on improving fermentation quality of wheat straw silage, that is, by enriching certain key metabolites to increase AA and LA concentrations, providing a reference for the cross study of silage feed fermentation microbiome and metabolome.Graphical

A review of genetic resources and trends of omics applications in donkey research: focus on China.

Omics methodologies, such as genomics, transcriptomics, proteomics, metabolomics, lipidomics and microbiomics, have revolutionized biological research by allowing comprehensive molecular analysis in livestock animals. However, despite being widely used in various animal species, research on donkeys has been notably scarce. China, renowned for its rich history in donkey husbandry, plays a pivotal role in their conservation and utilization. China boasts 24 distinct donkey breeds, necessitating conservation efforts, especially for smaller breeds facing extinction threats. So far, omics approaches have been employed in studies of donkey milk and meat, shedding light on their composition and quality. Similarly, omics methods have been utilized to explore the molecular basis associated with donkey growth, meat production, and quality traits. Omics analysis has also unraveled the critical role of donkey microbiota in health and nutrition, with gut microbiome studies revealing associations with factors such as pregnancy, age, transportation stress, and altitude. Furthermore, omics applications have addressed donkey health issues, including infectious diseases and reproductive problems. In addition, these applications have also provided insights into the improvement of donkey reproductive efficiency research. In conclusion, omics methodologies are essential for advancing knowledge about donkeys, their genetic diversity, and their applications across various domains. However, omics research in donkeys is still in its infancy, and there is a need for continued research to enhance donkey breeding, production, and welfare in China and beyond.

Insights into Porphyromonas somerae in Bladder Cancer Patients: Urinary Detection by ddPCR.

To date, the increased awareness of the impact of microbes on human health has promoted scientific interest in microbiome studies for diagnostic and therapeutic purposes, revealing correlations between specific taxa and cancer. In particular, numerous species of Porphyromonas have been associated with several types of tumors. Previously, we studied the urobiome using Next-Generation Sequencing (NGS), and found an increase in Porphyromonas somerae in first morning urine of subjects affected by bladder cancer (BCa). Here, we aimed to confirm the presence of P. somerae in BCa patients by using droplet digital Polymerase Chain Reaction (ddPCR), testing a cohort of 102 male subjects over 50 years. Our findings showed a significant increase in P. somerae in the urine of the BCa group within both ddPCR and NGS, and a correlation between the two methods was observed at a statistical level. Moreover, P. somerae's identification with ddPCR confirmed a significant association between this bacterium and the presence of BCa, highlighting its potential role as a biomarker. This allows us to propose the ddPCR as a suitable method for first-stage BCa screening and follow-up.

Caatinga Microbiome Initiative: disentangling the soil microbiome across areas under desertification and restoration in the Brazilian drylands

The Caatinga biome in Brazil is renowned for its rich aboveground biodiversity. However, approximately 19% of this biome has experienced desertification, severely affecting the soil microbiome. Monitoring these microbial communities is essential to understanding the impacts of desertification and the potential outcomes of restoration efforts. The Caatinga Microbiome Initiative aims to assess the soil microbiome in areas undergoing desertification and those under restoration in the Brazilian drylands. This initiative seeks to explore the core microbial communities and their functions, including overlooked groups such as protists, nematodes, and viruses across native, desertified, and restored areas. We believe that two approaches should be used simultaneously in the study of microbiomes in areas undergoing desertification. First, the use of the soil microbiome as a bioindicator of soil quality in both desertified and restored lands. Second, the manipulation of the microbiome to facilitate soil restoration. By understanding soil microbiome responses to desertification and restoration, we can develop targeted restoration strategies to enhance biodiversity and promote functional recovery in desertified areas.

Assessment of Skimmed Milk Flocculation for Bacterial Enrichment from Water Samples, and Benchmarking of DNA Extraction and 16S rRNA Databases for Metagenomics.

Water samples for bacterial microbiome studies undergo biomass concentration, DNA extraction, and taxonomic identification steps. Through benchmarking, we studied the applicability of skimmed milk flocculation (SMF) for bacterial enrichment, an adapted in-house DNA extraction protocol, and six 16S rRNA databases (16S-DBs). Surface water samples from two rivers were treated with SMF and vacuum filtration (VF) and subjected to amplicon or shotgun metagenomics. A microbial community standard underwent five DNA extraction protocols, taxonomical identification with six different 16S-DBs, and evaluation by the Measurement Integrity Quotient (MIQ) score. In SMF samples, the skimmed milk was metabolized by members of lactic acid bacteria or genera such as Polaromonas, Macrococcus, and Agitococcus, resulting in increased relative abundance (p < 0.5) up to 5.0 log fold change compared to VF, rendering SMF inapplicable for bacterial microbiome studies. The best-performing DNA extraction protocols were FastSpin Soil, the in-house method, and EurX. All 16S-DBs yielded comparable MIQ scores within each DNA extraction kit, ranging from 61-66 (ZymoBIOMICs) up to 80-82 (FastSpin). DNA extraction kits exert more bias toward the composition than 16S-DBs. This benchmarking study provided valuable information to inform future water metagenomic study designs.

Identification of age-associated microbial changes via long-read 16S sequencing

BackgroundAge-related gut microbial changes have been widely investigated over the past decade. Most of the previous age-related microbiome studies were conducted on the Western population, and the short-read sequencing (e.g., 16S V4 or V3-V4 region) was the most common microbiota profiling method. We evaluated the gut compositional differences using the long-read sequencing approach (i.e., PacBio sequencing targeting the full-length V1-V9 regions) to enable a deeper taxonomic resolution and better characterize the gut microbiome of Singaporeans from different age groups.ResultsA total of 83 research participants were included in this study. Although no significant differences were detected in alpha and beta diversity, our study demonstrated several bacterial taxa with abundances that were significantly different across age groups. With young individuals as the reference group, Eggerthella lenta and Bacteroides uniformis were found to be significantly altered in the middle-aged group, while Catenibacterium mitsuokai and Bacteroides plebeius were significantly altered in the elderly group. These age-related differences in the gut microbiome were associated with aberrations in several predicted functional pathways, including dysregulations of pathways related to lipopolysaccharide and tricarboxylic acid cycle in older adults.ConclusionsThe utilization of long-read sequencing facilitated the identification of species- and strain-level differences across age groups, which was challenging with the partial 16S rRNA sequencing approach. Nevertheless, replication studies are warranted to confirm our findings, and if confirmed, further in vitro and in vivo studies are crucial to better understand the impact of the altered levels of age-related bacterial taxa. Additionally, the modest performance of strain-level taxonomic classification using 16S-ITS-23S gene sequences, likely due to the limited depth of currently available alignment databases, highlights the need for optimization and refinement in curating these databases for the long-read sequencing approach.

Role of TM7 in the Oral Microbiome and its Implications for Periodontal Disease: A Systematic Review

ABSTRACT Periodontal disease, a prevalent oral health condition, involves a complex microbial community. Advanced sequencing technologies have enabled in-depth studies of the oral microbiome, focusing on specific bacterial taxa like TM7. This systematic review analyzes the role of TM7 in the oral microbiome and its implications for periodontal disease. This review underscores TM7’s importance in the oral microbiome and its potential as a target for future research on periodontal disease pathogenesis.

Bacteria from the Amycolatopsis genus associated with a toxic bird secrete protective secondary metabolites

Uropygial gland secretions of birds consist of host and bacteria derived compounds and play a major sanitary and feather-protective role. Here we report on our microbiome studies of the New Guinean toxic bird Pachycephala schlegelii and the isolation of a member of the Amycolatopsis genus from the uropygial gland secretions. Bioactivity studies in combination with co-cultures, MALDI imaging and HR-MS/MS-based network analyses unveil the basis of its activity against keratinolytic bacteria and fungal skin pathogens. We trace the protective antimicrobial activity of Amycolatopsis sp. PS_44_ISF1 to the production of rifamycin congeners, ciromicin A and of two yet unreported compound families. We perform NMR and HR-MS/MS studies to determine the relative structures of six members belonging to a yet unreported lipopeptide family of pachycephalamides and of one representative of the demiguisins, a new hexapeptide family. We then use a combination of phylogenomic, transcriptomic and knock-out studies to identify the underlying biosynthetic gene clusters responsible for the production of pachycephalamides and demiguisins. Our metabolomics data allow us to map molecular ion features of the identified metabolites in extracts of P. schlegelii feathers, verifying their presence in the ecological setting where they exert their presumed active role for hosts. Our study shows that members of the Actinomycetota may play a role in avian feather protection.

Impacts of ectoparasite flea infestation on gut microbiota of the Meriones unguiculatus in a rodent-flea system.

Fleas, along with one of their host species, Meriones unguiculatus, possess the capability to act as vectors in the transmission of plague. Parasitism by fleas may markedly influence the physiology and immune system of their hosts. Gut microbiota plays an important role in the growth and development of rodents. However, few studies have explored the impacts of ectoparasitic flea on the gut microbiome of rodents. This study investigated the immunological responses and changes in the gut microbial diversity and composition in both wild and laboratory rodents infested with fleas and laboratory rodents infested with Ctenocephalides felis. We measured immune reactions post-infestation by enzyme-linked immunosorbent assay (ELISA) and explored the effects of ectoparasitic infestation on the diversity and composition of the rodent gut microbiota in microbiome studies by 16S rRNA gene sequencing techniques. After flea infestation, results from 16S rRNA gene sequencing techniques indicated a significant increase in alpha diversity, affecting the composition of the gut microbiota. At the genus level, the abundance of the harmful bacterium Desulfovibrio increased. Beta diversity analyses showed significant differences between the gut microbiota of the experimental and control groups. ELISA results revealed significantly elevated levels of IgG in parasitized mice, while the differences in IgA levels were not significant. Flea infections affect the composition and diversity of the gut microbiota in the M. unguiculatu, and it has been found that the biomarker Desulfovibrio increases following flea infection in these rodents. Our results indicate that the gut microbiota can regulate the stability of the rodent-flea system. These findings may provide a foundation for exploring preventive measures against plague and aid in developing more effective treatments for the infection.

"Metagenomics reveal the potential for geosmin and 2-methylisoborneol production across multiple bacterial phyla in recirculating aquaculture systems".

Geosmin and 2-methylisoborneol (MIB) are known to cause taste-and-odour problems in recirculating aquaculture systems (RAS). Both geosmin and MIB are microbial metabolites belonging to terpenoids. Precursors for terpenoids are biosynthesized via the methylerythritol phosphate (MEP) and the mevalonate (MVA) pathways. We carried out a metagenomic analysis of 50 samples from five RAS to investigate terpenoid biosynthesis and metabolic potential for geosmin and MIB production in RAS microbiomes. A total of 1008 metagenome-assembled genomes (MAGs) representing 26 bacterial and three archaeal phyla were recovered. Although most archaea are thought to use the MVA pathway for terpenoid precursor biosynthesis, an Iainarchaeota archaeal MAG is shown to harbour a complete set of genes encoding the MEP pathway but lacking genes associated with the MVA pathway. In this study, a total of 16 MAGs affiliated with five bacterial phyla (Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexota, and Myxococcota) were identified as possessing potential geosmin or MIB synthases. These putative taste and odour producers were diverse, many were taxonomically unidentified at the genus or species level, and their relative abundance differed between the investigated RAS farms. The metagenomic study of the RAS microbiomes revealed a previously unknown phylogenetic diversity of the potential to produce geosmin and MIB.

Exploring the gut microbiome composition and diversity of leopard cats (Prionailurus bengalensis) in captive population: a case study in Zoo Melaka, Malaysia

Abstract Gut microbiomes are crucial in determining wildlife’s health, especially in the captive population. The alternation of environment can lead to microbiome changes that could affect the host’s health. Therefore, the information regarding gut microbiomes is important in conducting effective husbandry management for captive animals especially in zoos. Thus, we conducted the gut microbiome study on captive leopard cat (Prionailurus bengalensis) using Illumina Miniseq platform. The 16S rRNA gene sequencing was performed on two samples from both male and female leopard cats. The sequencing analysis successfully unveiled the diversity of gut microbiomes within this species, identifying members from 12 phyla, 74 families, and 127 genera of bacteria. Proteobacteria and Firmicutes emerged as the dominant phyla, constituting 49% in females and 34.7% in males, respectively. Intriguingly, no significant differences in alpha diverisity indices were observed between the sexes of P. bengalensis. These findings contribute substantially to our comprehension of the gut microbiome diversity in leopard cats in Malaysia. Generally, the findings of this study provide the fundamental data for the captive management and health assessment of P. bengalensis, acting as a valuable resource for future conservation efforts. By establishing a baseline understanding of the gut microbiomes in captivity, this research supports the broader goal of preserving the biological integrity of leopard cat populations especially in Malaysia.

A population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases

The gut microbiome has been implicated in various human diseases, though findings across studies have shown considerable variability. In this study, we reanalyzed 6314 publicly available fecal metagenomes from 36 case-control studies on different diseases to investigate microbial diversity and disease-shared signatures. Using a unified analysis pipeline, we observed reduced microbial diversity in many diseases, while some exhibited increased diversity. Significant alterations in microbial communities were detected across most diseases. A meta-analysis identified 277 disease-associated gut species, including numerous opportunistic pathogens enriched in patients and a depletion of beneficial microbes. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.776) and high-risk patients from controls (AUC = 0.825), and it also performed well in external cohorts. These results offer insights into the gut microbiome’s role in common diseases in the Chinese population and will guide personalized disease management strategies.

Optimization of a DNA extraction protocol for improving bacterial and fungal classification based on Nanopore sequencing.

Ribosomal RNA gene amplicon sequencing is commonly used to evaluate microbiome profiles in health and disease and document the impact of interventional treatments. Nanopore sequencing is attractive since it can provide greater classification at the species level. However, optimized protocols to target marker genes for bacterial and fungal profiling are needed. To achieve an increased taxonomic resolution, we developed extraction and full-length amplicon PCR-based approaches using Nanopore sequencing. Three lysis conditions were applied to a mock microbial community, including known bacterial and fungal species: ZymoBIOMICS lysis buffer (ML) alone, incorporating bead-beating (MLB) or bead-beating plus MetaPolyzyme enzymatic treatment (MLBE). In profiling of bacteria in comparison to reference data, MLB had more statistically different bacterial phyla and genera than the other two conditions. In fungal profiling, MLB had a significant increase of Ascomycota and a decline of Basidiomycota, subsequently failing to detect Malassezia and Cryptococcus. Also, a principal coordinates analysis plot by the Bray-Curtis metric showed a significant difference among groups for bacterial (P=0.033) and fungal (P=0.012) profiles, highlighting the importance of understanding the biases present in pretreatment. Overall, microbial profiling and diversity analysis revealed that ML and MLBE are more similar than MLB for both bacteria and fungi; therefore, using this specific pipeline, bead-beating is not recommended for whole gene amplicon sequencing. However, ML alone was suggested as an optimal approach considering DNA yield, taxonomic classification, reagent cost and hands-on time. This could be an initial proof-of-concept study for simultaneous human bacterial and fungal microbiome studies.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

The Art of Asking and Analyzing Sensitive Questions in Breast Milk Microbiome Research

Human milk includes, in addition to various bioactive molecules and microorganisms, that make up the “human milk microbiota (HMM)”. The breast milk microbiome is essential in gastrointestinal coloni¬zation of microbiome in children during the first 100 days of life. Microbial imbalance can be a starting point for necrotizing enterocolitis in premature infants, and can also contribute to the development of chronic diseases such as asthma and obesity later in life. In medical and biomedical research, particularly breast milk microbiome research, the use of surveys is often resorted to assess changes in the status of certain groups over a certain period. These questionnaires can focus on a specific sample and serve as an objective tool for professionals to acquire additional information. Creating a new questionnaire can be quite a challenge. The main difficulty lies in the development of an effective questionnaire for research and clinical use, meeting all legal frameworks, as well as the subsequent handling of sensitive data. The purpose of this study was to present the great need and difficulties in designing an effective survey for our research objectives. Research aimed at establishing the microbiome of breast milk and the associated pre¬diction of the gastrointestinal microbiome of the newborn has been of great importance recently. We have presented sample custom surveys that we have used to prepare and conduct various microbiome studies, and we believe that good preparation of an appropriate questionnaire contributes immensely to the overall completion of any biomedical study.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsOur simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsTight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

The Organelle in the Ointment: improved detection of cryptic mitochondrial reads resolves many unknown sequences in cross-species microbiome analyses

Abstract The genomes of mitochondria and chloroplasts contain ribosomal RNA (rRNA) genes, reflecting their ancestry as free-living bacteria. These organellar rRNAs are often amplified in microbiome studies of animals and plants. If identified, they can be discarded, merely reducing sequencing depth. However, we identify certain high-abundance organeller RNAs not identified by common pipelines, which may compromise statistical analysis of microbiome structure and diversity. We quantified this by reanalyzing 7459 samples from seven 16S rRNA studies, including microbiomes from 927 unique animal genera. We find that under-annotation of cryptic mitochondrial and chloroplast reads affects multiple of these large-scale cross-species microbiome comparisons, and varies between host species, biasing comparisons. We offer a straightforward solution: supplementing existing taxonomies with diverse organelle rRNA sequences. This resolves up to 97% of unique unclassified sequences in some entire studies as mitochondrial (14% averaged across all studies), without increasing false positive annotations in mitochondria-free mock communities. Improved annotation decreases the proportion of unknown sequences by ≥10-fold in 2262 of 7459 samples (30%), spanning 5 of 7 major studies examined. We recommend leveraging organelle sequence diversity to better identify organelle gene sequences in microbiome studies, and provide code, data resources and tutorials that implement this approach.