Host and microbiome multi-omics integration: applications and methodologies

Lateral root enriched Massilia associated with plant flowering in maize

BACKGROUND

Beneficial associations between plants and soil microorganisms are critical for crop fitness and resilience. However, it remains obscure how microorganisms are assembled across different root compartments and to what extent such recruited microbiomes determine crop performance. Here, we surveyed the root transcriptome and the root and rhizosphere microbiome via RNA sequencing and full-length (V1-V9) 16S rRNA gene sequencing from genetically distinct monogenic root mutants of maize (Zea mays L.) under different nutrient-limiting conditions.

RESULTS

Overall transcriptome and microbiome display a clear assembly pattern across the compartments, i.e., from the soil through the rhizosphere to the root tissues. Co-variation analysis identified that genotype dominated the effect on the microbial community and gene expression over the nutrient stress conditions. Integrated transcriptomic and microbial analyses demonstrated that mutations affecting lateral root development had the largest effect on host gene expression and microbiome assembly, as compared to mutations affecting other root types. Cooccurrence and trans-kingdom network association analysis demonstrated that the keystone bacterial taxon Massilia (Oxalobacteraceae) is associated with root functional genes involved in flowering time and overall plant biomass. We further observed that the developmental stage drives the differentiation of the rhizosphere microbial assembly, especially the associations of the keystone bacteria Massilia with functional genes in reproduction. Taking advantage of microbial inoculation experiments using a maize early flowering mutant, we confirmed that Massilia-driven maize growth promotion indeed depends on flowering time.

CONCLUSION

We conclude that specific microbiota supporting lateral root formation could enhance crop performance by mediating functional gene expression underlying plant flowering time in maize. Video Abstract.

The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities

Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.

Metagenomic dissection of the intestinal microbiome in the giant river prawn Macrobrachium rosenbergii infected with Decapod iridescent virus 1

Microbiome in the intestines of aquatic invertebrates plays pivotal roles in maintaining intestinal homeostasis, especially when the host is exposed to pathogen invasion. Decapod iridescent virus 1 (DIV1) is a devastating virus seriously affecting the productivity and success of crustacean aquaculture. In this study, a metagenomic analysis was conducted to investigate the genomic sequences, community structure and functional characteristics of the intestinal microbiome in the giant river prawn Macrobrachiumrosenbergii infected with DIV1. The results showed that DIV1 infection could significantly reduce the diversity and richness of intestinal microbiome. Proteobacteria represented the largest taxon at the phylum level, and at the species level, the abundance of Gonapodya prolifera and Solemya velum gill symbiont increased significantly following DIV1 infection. In the infected prawns, four metabolic pathways related to purine metabolism, pyrimidine metabolism, glycerophospholipid metabolism, and pentose phosphate pathway, and five pathways related to nucleotide excision repair, homologous recombination, mismatch repair, base excision repair, and DNA replication were significantly enriched. Moreover, several immune response related pathways, such as shigellosis, bacterial invasion of epithelial cells, Salmonella infection, and Vibrio cholerae infection were repressed, indicating that secondary infection in M. rosenbergii may be inhibited via the suppression of these immune related pathways. DIV1 infection led to the induction of microbial carbohydrate enzymes such as the glycoside hydrolases (GHs), and reduced the abundance and number of antibiotic-resistant ontologies (AROs). A variety of AROs were identified from the microbiota, and mdtF and lrfA appeared as the dominant genes in the detected AROs. In addition, antibiotic efflux, antibiotic inactivation, and antibiotic target alteration were the main antibiotic resistance mechanisms. Collectively, the data would enable a deeper understanding of the molecular response of intestinal microbiota to DIV1, and offer more insights into its roles in prawn resistance to DIVI infection.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

Selective breeding of cold-tolerant black soldier fly (Hermetia illucens) larvae: Gut microbial shifts and transcriptional patterns

The larvae of black soldier fly (BSFL) convert organic waste into insect proteins used as feedstuff for livestock and aquaculture. BSFL production performance is considerably reduced during winter season. Herein, the intraspecific diversity of ten commercial BSF colonies collected in China was evaluated. The Bioforte colony was subjected to selective breeding at 12 °C and 16 °C to develop cold-tolerant BSF with improved production performance. After breeding for nine generations, the weight of larvae, survival rate, and the dry matter conversion rate significantly increased. Subsequently, intestinal microbiota in the cold-tolerant strain showed that bacteria belonging to Morganella, Dysgonomonas, Salmonella, Pseudochrobactrum, and Klebsiella genera were highly represented in the 12 °C bred, while those of Acinetobacter, Pseudochrobactrum, Enterococcus, Comamonas, and Leucobacter genera were significantly represented in the 16 °C bred group. Metagenomic revealed that several animal probiotics of the Enterococcus and Vagococcus genera were greatly enriched in the gut of larvae bred at 16 °C. Moreover, bacterial metabolic pathways including carbohydrate, lipid, amino acids, and cofactors and vitamins, were significantly increased, while organismal systems and human diseases was decreased in the 16 °C bred group. Transcriptomic analysis revealed that the upregulated differentially expressed genes in the 16 °C bred groups mainly participated in Autophagy-animal, AMPK signaling pathway, mTOR signaling pathway, Wnt signaling pathway, FoxO signaling pathway, Hippo signaling pathway at day 34 under 16 °C conditions, suggesting their significant role in the survival of BSFL. Taken together, these results shed lights on the role of intestinal microflora and gene pathways in the adaptation of BSF larvae to cold stress.

Coping with extremes: the rumen transcriptome and microbiome co-regulate plateau adaptability of Xizang goat

The interactions between the rumen microbiota and the host are crucial for the digestive and absorptive processes of ruminants, and they are heavily influenced by the climatic conditions of their habitat. Owing to the harsh conditions of the high-altitude habitat, little is known about how ruminants regulate the host transcriptome and the composition of their rumen microbiota. Using the model species of goats, we examined the variations in the rumen microbiota, transcriptome regulation, and climate of the environment between high altitude (Lhasa, Xizang; 3650 m) and low altitude (Chengdu, Sichuan, China; 500 m) goats. The results of 16 S rRNA sequencing revealed variations in the abundance, diversity, and composition of rumen microbiota. Papillibacter, Quinella, and Saccharofermentans were chosen as potential microbes for the adaptation of Xizang goats to the harsh climate of the plateau by the Spearman correlation study of climate and microbiota. Based on rumen transcriptome sequencing analysis, 244 genes were found to be differentially expressed between Xizang goats and low-altitude goats, with 127 genes showing up-regulation and 117 genes showing down-regulation. SLC26A9, GPX3, ARRDC4, and COX1 were identified as potential candidates for plateau adaptation in Xizang goats. Moreover, the metabolism of fatty acids, arachidonic acids, pathway involving cytokines and their receptors could be essential for adaptation to plateau hypoxia and cold endurance. The expression of GPX3, a gene linked to plateau acclimatization in Xizang goats, was linked to the abundance of Anaerovibrio, and the expression of SLC26A9 was linked to the quantity of Selenomonas, according to ruminal microbiota and host Spearman correlation analysis. Our findings imply that in order to adapt harsh plateau conditions, Xizang goats have evolved to maximize digestion and absorption as well as to have a rumen microbiota suitable for the composition of their diet.

Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

Synergistic Responses of Tibetan Sheep Rumen Microbiota, Metabolites, and the Host to the Plateau Environment

Plateau adaptation in animals involves genetic mechanisms as well as coevolutionary mechanisms of the microbiota and metabolome of the animal. Therefore, the characteristics of the rumen microbiome and metabolome, transcriptome, and serum metabolome of Tibetan sheep at different altitudes (4500 m, 3500 m, and 2500 m) were analyzed. The results showed that the rumen differential metabolites at 3500 m and 4500 m were mainly enriched in amino acid metabolism, lipid metabolism, and carbohydrate metabolism, and there was a significant correlation with microbiota. The differentially expressed genes and metabolites at middle and high altitudes were coenriched in asthma, arachidonic acid metabolism, and butanoate and propanoate metabolism. In addition, the serum differential metabolites at 3500 m and 4500 m were mainly enriched in amino acid metabolism, lipid metabolism, and metabolism of xenobiotics by cytochrome P450, and they were also related to microbiota. Further analysis revealed that rumen metabolites accounted for 7.65% of serum metabolites. These common metabolites were mainly enriched in metabolic pathways and were significantly correlated with host genes (p < 0.05). This study found that microbiota, metabolites, and epithelial genes were coenriched in pathways related to lipid metabolism, energy metabolism, and immune metabolism, which may be involved in the regulation of Tibetan sheep adaptation to plateau environmental changes.

Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage

BACKGROUND

The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome.

AIM OF REVIEW

From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research- including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies.

Connect the dots: sketching out microbiome interactions through networking approaches

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.

The Fungal and Bacterial Interface in the Respiratory Mycobiome with a Focus on Aspergillus spp

The heterogeneity of the lung microbiome and its alteration are prevalently seen among chronic lung diseases patients. However, studies to date have primarily focused on the bacterial microbiome in the lung rather than fungal composition, which might play an essential role in the mechanisms of several chronic lung diseases. It is now well established that Aspergillus spp. colonies may induce various unfavorable inflammatory responses. Furthermore, bacterial microbiomes such as Pseudomonas aeruginosa provide several mechanisms that inhibit or stimulate Aspergillus spp. life cycles. In this review, we highlighted fungal and bacterial microbiome interactions in the respiratory tract, with a focus on Aspergillus spp.

Beneficial insights into postbiotics against colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and life-threatening cancer types with limited therapeutic options worldwide. Gut microbiota has been recognized as the pivotal determinant in maintaining gastrointestinal (GI) tract homeostasis, while dysbiosis of gut microbiota contributes to CRC development. Recently, the beneficial role of postbiotics, a new concept in describing microorganism derived substances, in CRC has been uncovered by various studies. However, a comprehensive characterization of the molecular identity, mechanism of action, or routes of administration of postbiotics, particularly their role in CRC, is still lacking. In this review, we outline the current state of research toward the beneficial effects of gut microbiota derived postbiotics against CRC, which will represent the key elements of future precision-medicine approaches in the development of novel therapeutic strategies targeting gut microbiota to improve treatment outcomes in CRC.

Glycans and Carbohydrate-Binding/Transforming Proteins in Axon Physiology

The mature nervous system relies on the polarized morphology of neurons for a directed flow of information. These highly polarized cells use their somatodendritic domain to receive and integrate input signals while the axon is responsible for the propagation and transmission of the output signal. However, the axon must perform different functions throughout development before being fully functional for the transmission of information in the form of electrical signals. During the development of the nervous system, axons perform environmental sensing functions, which allow them to navigate through other regions until a final target is reached. Some axons must also establish a regulated contact with other cells before reaching maturity, such as with myelinating glial cells in the case of myelinated axons. Mature axons must then acquire the structural and functional characteristics that allow them to perform their role as part of the information processing and transmitting unit that is the neuron. Finally, in the event of an injury to the nervous system, damaged axons must try to reacquire some of their immature characteristics in a regeneration attempt, which is mostly successful in the PNS but fails in the CNS. Throughout all these steps, glycans perform functions of the outermost importance. Glycans expressed by the axon, as well as by their surrounding environment and contacting cells, encode key information, which is fine-tuned by glycan modifying enzymes and decoded by glycan binding proteins so that the development, guidance, myelination, and electrical transmission functions can be reliably performed. In this chapter, we will provide illustrative examples of how glycans and their binding/transforming proteins code and decode instructive information necessary for fundamental processes in axon physiology.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

Host transcriptome and microbiome interactions in Holstein cattle under heat stress condition

Climate change affects animal physiology. In particular, rising ambient temperatures reduce animal vitality due to heat stress and this can be observed at various levels which included genome, transcriptome, and microbiome. In a previous study, microbiota highly associated with changes in cattle physiology, which included rectal temperature, drooling score and respiratory score, were identified under heat stress conditions. In the present study, genes differentially expressed between individuals were selected representing different additive genetic effects toward the heat stress response in cattle in their production condition. Moreover, a correlation network analysis was performed to identify interactions between the transcriptome and microbiome for 71 Chinese Holstein cows sequenced for mRNA from blood samples and for 16S rRNA genes from fecal samples. Bioinformatics analysis was performed comprising: i) clustering and classification of 16S rRNA sequence reads, ii) mapping cows' transcripts to the reference genome and their expression quantification, and iii) statistical analysis of both data types-including differential gene expression analysis and gene set enrichment analysis. A weighted co-expression network analysis was carried out to assess changes in the association between gene expression and microbiota abundance as well as to find hub genes/microbiota responsible for the regulation of gene expression under heat stress. Results showed 1,851 differentially expressed genes were found that were shared by three heat stress phenotypes. These genes were predominantly associated with the cytokine-cytokine receptor interaction pathway. The interaction analysis revealed three modules of genes and microbiota associated with rectal temperature with which two hubs of those modules were bacterial species, demonstrating the importance of the microbiome in the regulation of gene expression during heat stress. Genes and microbiota from the significant modules can be used as biomarkers of heat stress in cattle.

MULTI-OMICS as Invaluable Tools for the Elucidation of Host-Microbe-Microbiota Interactions

"Omics" is becoming an increasingly recognizable term, even to the general public, as it is used more and more often in everyday scientific research [...].

Comparison of microbial signatures between paired faecal and rectal biopsy samples from healthy volunteers using next-generation sequencing and culturomics

BACKGROUND

Faecal samples are frequently used to characterise the gut microbiota in health and disease, yet there is considerable debate about how representative faecal bacterial profiles are of the overall gut community. A particular concern is whether bacterial populations associated with the gut mucosa are properly represented in faecal samples, since these communities are considered critical in the aetiology of gastrointestinal diseases. In this study we compared the profiles of the faecal and mucosal microbiota from ten healthy volunteers using bacterial culturing (culturomics) and next-generation sequencing targeting the 16S ribosomal nucleic acid (rRNA) gene. Paired fresh rectal biopsies and faecal samples were processed under stringent anaerobic conditions to maintain the viability of the bacteria. Four different sample types were analysed: faecal (F), faecal homogenised (FHg), biopsy tissue (B) and biopsy wash (BW) samples.  RESULTS: There were no significant statistical differences in either bacterial richness or diversity between biopsy washes (BW) and faecal (F) or faecal homogenised (FHg) samples. Principal coordinates analysis of a Bray-Curtis distance matrix generated from sequence variant tables did not show distinct clustering between these samples (PERMANOVA; p = 0.972) but showed strong clustering of samples from individual donors. However, the rectal biopsy tissue (B) samples had a significantly altered bacterial signature with greater abundance of Proteobacteria and Acidobacteria compared to faecal (F) and faecal homogenised (FHg) samples. A total of 528 bacteria encompassing 92 distinct bacterial species were isolated and cultured from a subset of six volunteer samples (biopsy washes and faeces). This included isolation of 22 novel bacterial species. There was significant similarity between the bacterial species grown in anaerobic culture and those identified by 16S rRNA gene sequencing (Spearman correlation; rho = 0.548, p = 0.001).

CONCLUSION

This study showed that the bacterial profiles of paired faecal and rectal biopsy wash samples were very similar, validating the use of faecal samples as a convenient surrogate for rectal biopsy-associated microbiota. Anaerobic bacterial culture results showed similar taxonomic patterns to the amplicon sequence analysis disproving the dogma that culture-based methods do not reflect findings of molecular assessments of gut bacterial composition. Video abstract.

Metagenomics Approaches to Investigate the Neonatal Gut Microbiome

Early infancy is critical for the development of an infant's gut flora. Many factors can influence microbiota development during the pre- and postnatal periods, including maternal factors, antibiotic exposure, mode of delivery, dietary patterns, and feeding type. Therefore, investigating the connection between these variables and host and microbiome interactions in neonatal development would be of great interest. As the "unculturable" era of microbiome research gives way to an intrinsically multidisciplinary field, microbiome research has reaped the advantages of technological advancements in next-generation sequencing, particularly 16S rRNA gene amplicon and shotgun sequencing, which have considerably expanded our knowledge about gut microbiota development during early life. Using omics approaches to explore the neonatal microbiome may help to better understand the link between the microbiome and newborn diseases. Herein, we summarized the metagenomics methods and tools used to advance knowledge on the neonatal microbiome origin and evolution and how the microbiome shapes early and late individuals' lives for health and disease. The way to overcome limitations in neonatal microbiome studies will be discussed.

Multi-Omics Reveals That the Rumen Transcriptome, Microbiome, and Its Metabolome Co-regulate Cold Season Adaptability of Tibetan Sheep

Tibetan sheep can maintain a normal life and reproduce in harsh environments under extreme cold and lack of nutrition. However, the molecular and metabolic mechanisms underlying the adaptability of Tibetan sheep during the cold season are still unclear. Hence, we conducted a comprehensive analysis of rumen epithelial morphology, epithelial transcriptomics, microbiology and metabolomics in a Tibetan sheep model. The results showed that morphological structure of rumen epithelium of Tibetan sheep in cold season had adaptive changes. Transcriptomics analysis showed that the differential genes were primarily enriched in the PPAR signaling pathway (ko03320), legionellosis (ko05134), phagosome (ko04145), arginine and proline metabolism (ko00330), and metabolism of xenobiotics by cytochrome P450 (ko00980). Unique differential metabolites were identified in cold season, such as cynaroside A, sanguisorbin B and tryptophyl-valine, which were mainly enriched in arachidonic acid metabolism, arachidonic acid metabolism and linolenic acid metabolism pathways, and had certain correlation with microorganisms. Integrated transcriptome-metabolome-microbiome analysis showed that epithelial gene-GSTM3 expression was upregulated in the metabolism of xenobiotics by the cytochrome P450 pathway during the cold season, leading to the downregulation of some harmful metabolites; TLR5 gene expression was upregulated and CD14 gene expression was downregulated in the legionellosis pathway during the cold season. This study comprehensively described the interaction mechanism between the rumen host and microbes and their metabolites in grazing Tibetan sheep during the cold season. Rumen epithelial genes, microbiota and metabolites act together in some key pathways related to cold season adaptation.

Disentangling host-microbiota complexity through hologenomics

Research on animal-microbiota interactions has become a central topic in biological sciences because of its relevance to basic eco-evolutionary processes and applied questions in agriculture and health. However, animal hosts and their associated microbial communities are still seldom studied in a systemic fashion. Hologenomics, the integrated study of the genetic features of a eukaryotic host alongside that of its associated microbes, is becoming a feasible - yet still underexploited - approach that overcomes this limitation. Acknowledging the biological and genetic properties of both hosts and microbes, along with the advantages and disadvantages of implemented techniques, is essential for designing optimal studies that enable some of the major questions in biology to be addressed.

Integrated 16S rRNA Gene Sequencing and Metabolomics Analysis to Investigate the Important Role of Osthole on Gut Microbiota and Serum Metabolites in Neuropathic Pain Mice

Accumulating evidence suggests that neuropathic pain (NP) is closely connected to the metabolic disorder of gut microbiota, and natural products could relieve NP by regulating gut microbiota. The purpose of this study is to investigate the important regulatory effects of osthole on gut microbiota and serum metabolites in mice with chronic constriction injury (CCI). Mice's intestinal contents and serum metabolites were collected from the sham group, CCI group, and osthole treatment CCI group. The 16S rRNA gene sequencing was analyzed, based on Illumina NovaSeq platform, and ANOVA analysis were used to analyze the composition variety and screen differential expression of intestinal bacteria in the three groups. Ultra-high-performance liquid chromatography-quadrupole time of flight-tandem mass spectrometry (UHPLC-Q-TOF-MS) was used for analyzing the data obtained from serum specimens, and KEGG enrichment analysis was used to identify pathways of differential metabolites in the treatment of neuralgia mice. Furthermore, the Pearson method and Cytoscape soft were used to analyze the correlation network of differential metabolites, gut microbiota, and disease genes. The analysis results of 16S rRNA gene sequencing displayed that Bacteroidetes, Firmicutes, and Verrucomicrobia were highly correlated with NP after osthole treatment at the phylum level. Akkermansia, Lachnospiraceae_unclassified, Lachnospiraceae_NK4A136_group, Bacteroides, Lactobacillus, and Clostridiales_unclassified exhibited higher relative abundance and were considered important microbial members at genus level in neuralgia mice. Serum metabolomics results showed that 131 metabolites were considered to be significantly different in the CCI group compared to the sham group, and 44 metabolites were significantly expressed between the osthole treatment group and the CCI group. At the same time, we found that 29 differential metabolites in the two comparison groups were overlapping. Integrated analysis results showed that many intestinal microorganisms and metabolites have a strong positive correlation. The correlation network diagram displays that 10 genes were involved in the process of osthole alleviating NP through a metabolic pathway and gut microbiota, including IGF2, GDAP1, MYLK, IL18, CD55, MIR331, FHIT, F3, ERBB4, and ITGB3. Our findings have preliminarily confirmed that NP is closely related to metabolism and intestinal microbial imbalance, and osthole can improve the metabolic disorder of NP by acting on gut microbiota.

Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System

Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”

Studies of xenobiotic-induced gut microbiota dysbiosis: from correlation to mechanisms

Environmental chemicals can alter gut microbial community composition, known as dysbiosis. However, the gut microbiota is a highly dynamic system and its functions are still largely underexplored. Likewise, it is unclear whether xenobiotic exposure affects host health through impairing host-microbiota interactions. Answers to this question not only can lead to a more precise understanding of the toxic effects of xenobiotics but also can provide new targets for the development of new therapeutic strategies. Here, we aim to identify the major challenges in the field of microbiota-exposure research and highlight the need to exam the health effects of xenobiotic-induced gut microbiota dysbiosis in host bodies. Although the changes of gut microbiota frequently co-occur with the xenobiotic exposure, the causal relationship of xenobiotic-induced microbiota dysbiosis and diseases is rarely established. The high dynamics of the gut microbiota and the complex interactions among exposure, microbiota, and host, are the major challenges to decipher the specific health effects of microbiota dysbiosis. The next stage of study needs to combine various technologies to precisely assess the xenobiotic-induced gut microbiota perturbation and the subsequent health effects in host bodies. The exposure, gut microbiota dysbiosis, and disease outcomes have to be causally linked. Many microbiota-host interactions are established by previous studies, including signaling metabolites and response pathways in the host, which may use as start points for future research to examine the mechanistic interactions of exposure, gut microbiota, and host health. In conclusion, to precisely understand the toxicity of xenobiotics and develop microbiota-based therapies, the causal and mechanistic links of exposure and microbiota dysbiosis have to be established in the next stage study.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma

Asthma is a multifactorial inflammatory disorder of the respiratory system characterized by high diversity in clinical manifestations, underlying pathological mechanisms and response to treatment. It is generally established that human microbiota plays an essential role in shaping a healthy immune response, while its perturbation can cause chronic inflammation related to a wide range of diseases, including asthma. Systems biology approaches encompassing microbiome analysis can offer valuable platforms towards a global understanding of asthma complexity and improving patients' classification, status monitoring and therapeutic choices. In the present review, we summarize recent studies exploring the contribution of microbiota dysbiosis to asthma pathogenesis and heterogeneity in the context of asthma phenotypes-endotypes and administered medication. We subsequently focus on emerging efforts to gain deeper insights into microbiota-host interactions driving asthma complexity by integrating microbiome and host multi-omics data. One of the most prominent achievements of these research efforts is the association of refractory neutrophilic asthma with certain microbial signatures, including predominant pathogenic bacterial taxa (such as Proteobacteria phyla, Gammaproteobacteria class, especially species from Haemophilus and Moraxella genera). Overall, despite existing challenges, large-scale multi-omics endeavors may provide promising biomarkers and therapeutic targets for future development of novel microbe-based personalized strategies for diagnosis, prevention and/or treatment of uncontrollable asthma.

NFnetFu: A novel workflow for microbiome data fusion

Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.

Review of multi-omics data resources and integrative analysis for human brain disorders

In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer's disease, Parkinson's disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.

Culturing Human Gut Microbiomes in the Laboratory

The human gut microbiota is a complex community of prokaryotic and eukaryotic microbes and viral particles that is increasingly associated with many aspects of host physiology and health. However, the classical microbiology approach of axenic culture cannot provide a complete picture of the complex interactions between microbes and their hosts in vivo. As such, recently there has been much interest in the culture of gut microbial ecosystems in the laboratory as a strategy to better understand their compositions and functions. In this review, we discuss the model platforms and methods available in the contemporary microbiology laboratory to study human gut microbiomes, as well as current knowledge surrounding the isolation of human gut microbes for the potential construction of defined communities for use in model systems. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

C. elegans: A biosensor for host-microbe interactions

Microbes are an integral part of life on this planet. Microbes and their hosts influence each other in an endless dance that shapes how the meta-organism interacts with its environment. Although great advances have been made in microbiome research over the past 20 years, the mechanisms by which both hosts and their microbes interact with each other and the environment are still not well understood. The nematode Caenorhabditis elegans has been widely used as a model organism to study a remarkable number of human-like processes. Recent evidence shows that the worm is a powerful tool to investigate in fine detail the complexity that exists in microbe-host interactions. By combining the large array of genetic tools available for both organisms together with deep phenotyping approaches, it has been possible to uncover key effectors in the complex relationship between microbes and their hosts. In this perspective, we survey the literature for insightful discoveries in the microbiome field using the worm as a model. We discuss the latest conceptual and technological advances in the field and highlight the strengths that make C. elegans a valuable biosensor tool for the study of microbe-host interactions.

Impact of preoperative antibiotics and other variables on integrated microbiome-host transcriptomic data generated from colorectal cancer resections

BACKGROUND

Integrative multi-omic approaches have been increasingly applied to discovery and functional studies of complex human diseases. Short-term preoperative antibiotics have been adopted to reduce site infections in colorectal cancer (CRC) resections. We hypothesize that the antibiotics will impact analysis of multi-omic datasets generated from resection samples to investigate biological CRC risk factors.

AIM

To assess the impact of preoperative antibiotics and other variables on integrated microbiome and human transcriptomic data generated from archived CRC resection samples.

METHODS

Genomic DNA (gDNA) and RNA were extracted from prospectively collected 51 pairs of frozen sporadic CRC tumor and adjacent non-tumor mucosal samples from 50 CRC patients archived at a single medical center from 2010-2020. The 16S rRNA gene sequencing (V3V4 region, paired end, 300 bp) and confirmatory quantitative polymerase chain reaction (qPCR) assays were conducted on gDNA. RNA sequencing (IPE, 125 bp) was performed on parallel tumor and non-tumor RNA samples with RNA Integrity Numbers scores ≥ 6.

RESULTS

PERMANOVA detected significant effects of tumor vs nontumor histology (P = 0.002) and antibiotics (P = 0.001) on microbial β-diversity, but CRC tumor location (left vs right), diabetes mellitus vs not diabetic and Black/African Ancestry (AA) vs not Black/AA, did not reach significance. Linear mixed models detected significant tumor vs nontumor histology*antibiotics interaction terms for 14 genus level taxa. QPCR confirmed increased Fusobacterium abundance in tumor vs nontumor groups, and detected significantly reduced bacterial load in the (+)antibiotics group. Principal coordinate analysis of the transcriptomic data showed a clear separation between tumor and nontumor samples. Differentially expressed genes obtained from separate analyses of tumor and nontumor samples, are presented for the antibiotics, CRC location, diabetes and Black/AA race groups.

CONCLUSION

Recent adoption of additional preoperative antibiotics as standard of care, has a measurable impact on -omics analysis of resected specimens. This study still confirmed increased Fusobacterium nucleatum in tumor.

Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation

Beneficial interactions between plant roots and rhizosphere microorganisms are pivotal for plant fitness. Nevertheless, the molecular mechanisms controlling the feedback between root architecture and microbial community structure remain elusive in maize. Here, we demonstrate that transcriptomic gradients along the longitudinal root axis associate with specific shifts in rhizosphere microbial diversity. Moreover, we have established that root-derived flavones predominantly promote the enrichment of bacteria of the taxa Oxalobacteraceae in the rhizosphere, which in turn promote maize growth and nitrogen acquisition. Genetic experiments demonstrate that LRT1-mediated lateral root development coordinates the interactions of the root system with flavone-dependent Oxalobacteraceae under nitrogen deprivation. In summary, these experiments reveal the genetic basis of the reciprocal interactions between root architecture and the composition and diversity of specific microbial taxa in the rhizosphere resulting in improved plant performance. These findings may open new avenues towards the breeding of high-yielding and nutrient-efficient crops by exploiting their interaction with beneficial soil microorganisms.

Holo-omics for deciphering plant-microbiome interactions

Host-microbiome interactions are recognized for their importance to host health. An improved understanding of the molecular underpinnings of host-microbiome relationships will advance our capacity to accurately predict host fitness and manipulate interaction outcomes. Within the plant microbiome research field, unlocking the functional relationships between plants and their microbial partners is the next step to effectively using the microbiome to improve plant fitness. We propose that strategies that pair host and microbial datasets-referred to here as holo-omics-provide a powerful approach for hypothesis development and advancement in this area. We discuss several experimental design considerations and present a case study to highlight the potential for holo-omics to generate a more holistic perspective of molecular networks within the plant microbiome system. In addition, we discuss the biggest challenges for conducting holo-omics studies; specifically, the lack of vetted analytical frameworks, publicly available tools, and required technical expertise to process and integrate heterogeneous data. Finally, we conclude with a perspective on appropriate use-cases for holo-omics studies, the need for downstream validation, and new experimental techniques that hold promise for the plant microbiome research field. We argue that utilizing a holo-omics approach to characterize host-microbiome interactions can provide important opportunities for broadening system-level understandings and significantly inform microbial approaches to improving host health and fitness. Video abstract.

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.

Emerging computational tools and models for studying gut microbiota composition and function

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

Insects' potential: Understanding the functional role of their gut microbiome

The study of insect-associated microbial communities is a field of great importance in agriculture, principally because of the role insects play as pests. In addition, there is a recent focus on the potential of the insect gut microbiome in areas such as biotechnology, given some microorganisms produce molecules with biotechnological and industrial applications, and also in biomedicine, since some bacteria and fungi are a reservoir of antibiotic resistance genes (ARGs). To date, most studies aiming to characterize the role of the gut microbiome of insects have been based on high-throughput sequencing of the 16S rRNA gene and/or metagenomics. However, recently functional approaches such as metatranscriptomics, metaproteomics and metabolomics have also been employed. Besides providing knowledge about the taxonomic distribution of microbial populations, these techniques also reveal their functional and metabolic capabilities. This information is essential to gain a better understanding of the role played by microbes comprising the microbial communities in their hosts, as well as to indicate their possible exploitation. This review provides an overview of how far we have come in characterizing insect gut functionality through omics, as well as the challenges and future perspectives in this field.

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

[Analysis of intestinal flora in patients with chronic rhinosinusitis based on highthroughput sequencing]

OBJECTIVE

To investigate the changes in diversity, relative abundance and distribution of intestinal flora in patients with chronic rhinosinusitis and nasal polyps (CRSwNP) using high-throughput sequencing technology identify the intestinal flora significantly related to pathogenesis and progression of CRSwNP.

METHODS

Ten patients with CRSwNP hospitalized in the Department of Otolaryngology-Head and Neck Surgery of Guangdong Provincial People's Hospital were selected as the case group with 10 healthy volunteers recruited in the same period as the control group. Fecal genomic DNA extraction kit was used to extract the DNA in the fecal samples, and the DNA fragment length was measured and quantified. The V3 and V4 highly variable regions of the 16S rDNA gene of prokaryotes were amplified followed by library construction, Illumina MiSeq sequencing, sequence alignment and species identification analysis. The relative abundance, diversity and distribution characteristics of the intestinal flora were analyzed, and the relevant metabolic pathways were predicted.

RESULTS

Compared with the control group, the patients with CRSwNP had significant changes in the overall structure of the intestinal flora, highlighted by increased abundance of Saccharopolyspora and decreased contents of Ruminococcae, Coprococcus, Collinsella and Dialister. Among the metabolic pathways predicted to be associated with CRSwNP, 9 showed significant changes in patients with CRSwNP as compared with the control group (P < 0.05).

CONCLUSIONS

Patients with CRSwNP have significant changes in the structural characteristics of intestinal flora related with multiple metabolic pathways, and these changes may play an important role in the development of chronic rhinosinusitis.

Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host's phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways.

Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research

From ontogenesis to homeostasis, the phenotypes of complex organisms are shaped by the bidirectional interactions between the host organisms and their associated microbiota. Current technology can reveal many such interactions by combining multi-omic data from both hosts and microbes. However, exploring the full extent of these interactions requires careful consideration of study design for the efficient generation and optimal integration of data derived from (meta)genomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics. In this perspective, we introduce the holo-omic approach that incorporates multi-omic data from both host and microbiota domains to untangle the interplay between the two. We revisit the recent literature on biomolecular host-microbe interactions and discuss the implementation and current limitations of the holo-omic approach. We anticipate that the application of this approach can contribute to opening new research avenues and discoveries in biomedicine, biotechnology, agricultural and aquacultural sciences, nature conservation, as well as basic ecological and evolutionary research.

Multi-omics analysis to examine microbiota, host gene expression and metabolites in the intestine of black tiger shrimp (Penaeus monodon) with different growth performance

Understanding the correlation between shrimp growth and their intestinal bacteria would be necessary to optimize animal's growth performance. Here, we compared the bacterial profiles along with the shrimp's gene expression responses and metabolites in the intestines between the Top and the Bottom weight groups. Black tiger shrimp (Penaeus monodon) were collected from the same population and rearing environments. The two weight groups, the Top-weight group with an average weight of 36.82 ± 0.41 g and the Bottom-weight group with an average weight of 17.80 ± 11.81 g, were selected. Intestines were aseptically collected and subjected to microbiota, transcriptomic and metabolomic profile analyses. The weighted-principal coordinates analysis (PCoA) based on UniFrac distances showed similar bacterial profiles between the two groups, suggesting similar relative composition of the overall bacterial community structures. This observed similarity was likely due to the fact that shrimp were from the same genetic background and reared under the same habitat and diets. On the other hand, the unweighted-distance matrix revealed that the bacterial profiles associated in intestines of the Top-weight group were clustered distinctly from those of the Bottom-weight shrimp, suggesting that some unique non-dominant bacterial genera were found associated with either group. The key bacterial members associated to the Top-weight shrimp were mostly from Firmicutes (Brevibacillus and Fusibacter) and Bacteroidetes (Spongiimonas), both of which were found in significantly higher abundance than those of the Bottom-weight shrimp. Transcriptomic profile of shrimp intestines found significant upregulation of genes mostly involved in nutrient metabolisms and energy storage in the Top-weight shrimp. In addition to significantly expressed metabolic-related genes, the Bottom-weight shrimp also showed significant upregulation of stress and immune-related genes, suggesting that these pathways might contribute to different degrees of shrimp growth performance. A non-targeted metabolome analysis from shrimp intestines revealed different metabolic responsive patterns, in which the Top-weight shrimp contained significantly higher levels of short chain fatty acids, lipids and organic compounds than the Bottom-weight shrimp. The identified metabolites included those that were known to be produced by intestinal bacteria such as butyric acid, 4-indolecarbaldehyde and L-3-phenyllactic acid as well as those produced by shrimp such as acyl-carnitines and lysophosphatidylcholine. The functions of these metabolites were related to nutrient absorption and metabolisms. Our findings provide the first report utilizing multi-omics integration approach to investigate microbiota, metabolic and transcriptomics profiles of the host shrimp and their potential roles and relationship to shrimp growth performance.

Fecal Sample Collection Method for Wild Birds-Associated Microbiome Research: Perspectives for Wildlife Studies

Simple Summary

This paper describes an easy-to-build box for the noninvasive collection of feces from wild birds or small wild animals (up to 1 kg), including a plastic storage box, a plastic tray, and a vinyl-coated hardware cloth. This method could minimize potential contamination and allow for cross-study comparisons on gut microbiomes for wildlife medicine, conservation, ecology, and evolutionary biology.

Abstract

Gut microbial communities play important roles in host health, modulating development, nutrient acquisition, immune and metabolic regulation, behavior and diseases. Wildlife microbiome studies and host–microbe interaction and exploration might be an important goal for evolutionary biology, conservation, and ecology. Therefore, collection and sampling methods must be considered before choosing a microbiome-based research plan. Since the fecal microbial community reflects the true gut community better than that of cloacal swab samples and only few nondestructive methods have been described, we propose an easy-to-build box for a noninvasive fecal collection method. The main components of the collection box include a plastic storage box, a plastic tray, a vinyl-coated hardware cloth, and a 10% bleach solution. In the plastic box, the tray is positioned under the raised grate, where the bird is placed, to reduce the risk of contamination of the fecal samples. This procedure could simplify handling and processing phases in wild birds or other animals. It might represent a cheap and useful method for research studies, wildlife rescue center activities, veterinary practices, and conservation practitioners.

Microbiota-derived metabolites as diagnostic markers for respiratory fungal infections

An emerging body of evidence has highlighted the significant role of the pulmonary microbiota during respiratory infections. The individual microbiome is nowadays recognized to supervise the outcome of the host-pathogen interaction by orchestrating mechanisms of immune regulation, inflammation, metabolism, and other physiological processes. A shift in the normal flora of the respiratory tract is associated with several lung inflammatory disorders including asthma, chronic obstructive pulmonary disease, or cystic fibrosis. These diseases are characterized by a lung microenvironment that becomes permissive to infections caused by the opportunistic fungal pathogen Aspergillus fumigatus. Although the role of the lung microbiota in the pathophysiology of respiratory fungal diseases remains elusive, microbiota-derived components have been proposed as important biomarkers to be considered in the diagnosis of these severe infections. Here, we review this emerging area of research and discuss the potential of microbiota-derived products in the diagnosis of respiratory fungal diseases.

Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.

Multi-omic profiling reveals associations between the gut mucosal microbiome, the metabolome, and host DNA methylation associated gene expression in patients with colorectal cancer

Background

The human gut microbiome plays a critical role in the carcinogenesis of colorectal cancer (CRC). However, a comprehensive analysis of the interaction between the host and microbiome is still lacking.

Results

We found correlations between the change in abundance of microbial taxa, butyrate-related colonic metabolites, and methylation-associated host gene expression in colonic tumour mucosa tissues compared with the adjacent normal mucosa tissues. The increase of genus Fusobacterium abundance was correlated with a decrease in the level of 4-hydroxybutyric acid (4-HB) and expression of immune-related peptidase inhibitor 16 (PI16), Fc Receptor Like A (FCRLA) and Lymphocyte Specific Protein 1 (LSP1). The decrease in the abundance of another potentially 4-HB-associated genus, Prevotella 2, was also found to be correlated with the down-regulated expression of metallothionein 1 M (MT1M). Additionally, the increase of glutamic acid-related family Halomonadaceae was correlated with the decreased expression of reelin (RELN). The decreased abundance of genus Paeniclostridium and genus Enterococcus were correlated with increased lactic acid level, and were also linked to the expression change of Phospholipase C Beta 1 (PLCB1) and Immunoglobulin Superfamily Member 9 (IGSF9) respectively. Interestingly, 4-HB, glutamic acid and lactic acid are all butyrate precursors, which may modify gene expression by epigenetic regulation such as DNA methylation.

Conclusions

Our study identified associations between previously reported CRC-related microbial taxa, butyrate-related metabolites and DNA methylation-associated gene expression in tumour and normal colonic mucosa tissues from CRC patients, which uncovered a possible mechanism of the role of microbiome in the carcinogenesis of CRC. In addition, these findings offer insight into potential new biomarkers, therapeutic and/or prevention strategies for CRC.

The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Assessment of gut microbiota fecal metabolites by chromatographic targeted approaches

Gut microbiota, the specific microbial community of the gastrointestinal tract, by means of the production of microbial metabolites provides the host with several functions affecting metabolic and immunological homeostasis. Insights into the intricate relationships between gut microbiota and the host require not only the understanding of its structure and function but also the measurement of effector molecules acting along the gut microbiota axis. This article reviews the literature on targeted chromatographic approaches in analysis of gut microbiota specific metabolites in feces as the most accessible biological matrix which can directly probe the connection between intestinal bacteria and the (patho)physiology of the holobiont. Together with a discussion on sample collection and preparation, the chromatographic methods targeted to determination of some classes of microbiota-derived metabolites (e.g., short-chain fatty acids, bile acids, low molecular masses amines and polyamines, vitamins, neurotransmitters and related compounds) are discussed and their main characteristics, summarized in Tables.