Power of Microbiome Beta-Diversity Analyses Based on Standard Reference Samples

Insight into the composition and differentiation of endophytic microbial communities in kernels via 368 maize transcriptomes

INTRODUCTION

Kernels are important reproductive organs in maize, yet there is a lack of systematic investigation on the differences in the composition of endophytic microorganisms in plants from a population perspective.

OBJECTIVES

We aimed to elucidate the composition of endophytic microorganisms in developing maize kernels, emphasizing differences among various inbred lines.

METHODS

The transcriptomic data of 368 maize inbred lines were used to explore the composition and diversity of endophytic microorganisms.

RESULTS

The findings revealed a higher abundance of fungi than bacteria in developing maize kernels, followed by protozoa, while viruses were less abundant. There were significant differences in the composition and relative abundance of endophytic microorganisms among different maize lines. Diversity analysis revealed overall similarity in the community composition structure between tropical/subtropical (TST) and temperate (NSS) maize germplasm with apparent variations in community richness and abundance. The endophytic microorganisms network in the kernels from TST genotypes exhibited higher connectivity and stability compared to NSS kernels. Bacteria dominated the highly connected species in the networks, and different core species showed microbial phylum specificity. Some low-abundance species acted as core species, contributing to network stability. Beneficial bacteria were predominant in the core species of networks in TST kernels, while pathogenic bacteria were more abundant in the core species of networks in NSS kernels.

CONCLUSION

Tropical maize germplasm may have advantages in resisting the invasion of pathogenic microorganisms, providing excellent genetic resources for disease-resistant breeding.

Multi-Omics Approaches for Freshness Estimation and Detection of Illicit Conservation Treatments in Sea Bass (Dicentrarchus Labrax): Data Fusion Applications

Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.

Analysis of the bacterial communities associated with pupae and winged or wingless adults of Lipoptena fortisetosa collected from cervids in Italy

The hippoboscid Lipoptena fortisetosa Maa, 1965 is a hematophagous ectoparasite of cervids that can bite humans. This fly is expanding its geographical range and is of concern for animal and human health since it can potentially harbour harmful microorganisms. This study was aimed at characterizing the bacterial communities of L. fortisetosa in its different life-cycle stages. Pupae and wingless adults were collected from cervids hunted in Tuscan-Emilian Apennines (central Italy) and pooled into groups of 10 by life stage (30 individual pupae; 1420 individual wingless adults). Winged flies were caught by sweep netting and separated into five pools of 10 insects. After DNA extraction, the bacterial content of each pool was analysed using 16 S metabarcoding. Results revealed that the composition and relative abundance of different taxa greatly differed in the three analysed groups. Wingless adults showed a high abundance of Bartonella (33.07%), which is almost absent in winged flies and pupae. Among the detected pathogens, four genera of concern for human health were found: Bartonella, Moraxella, Mycobacterium and Rickettsia. Interestingly reads similar to Bartonella bovis, Moraxella osloensis and Arsenophonus lipopteni Operational Taxonomic Unit (OTUs) were detected. These findings suggest the possible role of L. fortisetosa as a reservoir of pathogenic microorganisms, confirming the need for further investigation to ascertain its vectorial capacity.

Viral and Bacterial Profiles in Endemic Influenza A Virus Infected Swine Herds Using Nanopore Metagenomic Sequencing on Tracheobronchial Swabs

Swine influenza A virus (swIAV) plays an important role in porcine respiratory infections. In addition to its ability to cause severe disease by itself, it is important in the multietiological porcine respiratory disease complex. Still, to date, no comprehensive diagnostics with which to study polymicrobial infections in detail have been offered. Hence, veterinary practitioners rely on monospecific and costly diagnostics, such as Reverse Transcription quantitative PCR (RT-qPCR), antigen detection, and serology. This prevents the proper understanding of the entire disease context, thereby hampering effective preventive and therapeutic actions. A new, nanopore-based, metagenomic diagnostic platform was applied to study viral and bacterial profiles across 4 age groups on 25 endemic swIAV-infected German farms with respiratory distress in the nursery. Farms were screened for swIAV using RT-qPCR on nasal and tracheobronchial swabs (TBS). TBS samples were pooled per age, prior to metagenomic characterization. The resulting data showed a correlation between the swIAV loads and the normalized reads, supporting a (semi-)quantitative interpretation of the metagenomic data. Interestingly, an in-depth characterization using beta diversity and PERMANOVA analyses allowed for the observation of an age-dependent interplay of known microbial agents. Also, lesser-known microbes, such as porcine polyoma, parainfluenza, and hemagglutinating encephalomyelitis viruses, were observed. Analyses of swIAV incidence and clinical signs showed differing microbial communities, highlighting age-specific observations of various microbes in porcine respiratory disease. In conclusion, nanopore metagenomics were shown to enable a panoramic view on viral and bacterial profiles as well as putative pathogen dynamics in endemic swIAV-infected herds. The results also highlighted the need for better insights into lesser studied agents that are potentially associated with porcine respiratory disease. IMPORTANCE To date, no comprehensive diagnostics for the study of polymicrobial infections that are associated with porcine respiratory disease have been offered. This precludes the proper understanding of the entire disease landscape, thereby hampering effective preventive and therapeutic actions. Compared to the often-costly diagnostic procedures that are applied for the diagnostics of porcine respiratory disease nowadays, a third-generation nanopore sequencing diagnostics workflow presents a cost-efficient and informative tool. This approach offers a panoramic view of microbial agents and contributes to the in-depth observation and characterization of viral and bacterial profiles within the respiratory disease context. While these data allow for the study of age-associated, swIAV-associated, and clinical symptom-associated observations, it also suggests that more effort should be put toward the investigation of coinfections and lesser-known pathogens (e.g., PHEV and PPIV), along with their potential roles in porcine respiratory disease. Overall, this approach will allow veterinary practitioners to tailor treatment and/or management changes on farms in a quicker, more complete, and cost-efficient way.

Beta-diversity distance matrices for microbiome sample size and power calculations - How to obtain good estimates

In microbiome studies, researchers often wish to compare the taxa count distributions between groups of samples. Commonly-used corresponding methods of analysis are built on examining distance matrices, where distances describe the beta-diversity between samples. Analyses then compare the distribution of distances within groups to the distributions between groups. However, when performing a priori sample size or power calculations for such study designs, appropriate within and between group distance distributions can be challenging to obtain. When available, pilot study data, or data from prior studies of similar design should provide realistic distance estimates. However, when these are not available, distances can be extracted from available studies where one can assume similar beta-diversity. Alternatively, distances can be generated by simulation methods. Here, we describe and illustrate these three strategies for obtaining realistic distance matrices. For simulation methods, we illustrate the procedures required starting from existing benchmark data, as well as how to simulate directly from population assumptions. Using data from the American Gut project, we provide tables of observed distances for use by researchers planning their own studies, as well as R codes for generating similar matrices in other datasets. Furthermore, for simulated data, we compare methods, provide R codes, and demonstrate how challenging it is to obtain realistic distance distributions without any benchmark data. This code and illustrative distance tables are provided by the IMPACTT Consortium as a resource to the microbiome research community.