A Beginner’s Guide on Integrating *Omics Approaches to Study Marine Microbial Communities: Details and Discussions From Sample Collection to Bioinformatics Analysis

The variety of Earth’s organisms is manifold. However, it is the small-scale marine community that makes the world goes round. Microbial organisms of pro- and eukaryotic origin drive the carbon supply and nutrient cycling, thus are mediating the primary productivity within the world largest ecosystem called ocean. But due to the ocean’s great size and large number of biogeographically habitats, the total of microbial species can hardly be grabbed and therefore their functional roles not fully described. However, recent advances in high-throughput sequencing technologies are revolutionizing our understanding of the marine microbial diversity, ecology and evolution. Nowadays, research questions on species differentiation can be solved with genomic approaches such as metabarcoding, while transcriptomics offers the possibility to assign gene functions even to a single cell, e.g., single-cell transcriptomics. On the other hand, due to the diversified amount of sequencing data, the certainty of a data crisis is currently evolving. Scientists are forced to broaden their view on bioinformatics resources for analysis and data storage in from of, e.g., cloud services, to ensure the data’s exchangeability. Which is why time resources are now shifting toward solving data problems rather than answering the eco-evolutionary questions stated in the first place. This review is intended to provide exchange on *omics approaches and key points for discussions on data handling used to decipher the relevant diversity and functions of microbial organisms in the marine ecosystem.

A comparative study of pan-genome methods for microbial organisms: Acinetobacter baumannii pan-genome reveals structural variation in antimicrobial resistance-carrying plasmids

Microbial organisms have diverse populations, where using a single linear reference sequence in comparative studies introduces reference-bias in downstream analyses, and leads to a failure to account for variability in the population. Recently, pan-genome graphs have emerged as an alternative to the traditional linear reference with many successful applications and a rapid increase in the number of methods available in the literature. Despite this enthusiasm, there has been no attempt at exploring these graph construction methods in depth, demonstrating their practical use. In this study, we aim to develop a general guide to help researchers who may want to incorporate pan-genomes in their analyses of microbial organisms. We evaluated the state-of-the art pan-genome construction tools to model a collection of 70 Acinetobacter baumannii strains. Our results suggest that all tools produced pan-genome graphs conforming to our expectations based on previous literature, and that their approach to homologue detection is likely to be the most influential in determining the final size and complexity of the pan-genome. The graphs overlapped most in the core pan-genome content while the cloud genes varied significantly among tools. We propose an alternative approach for pan-genome construction by combining two of the tools, Panaroo and Ptolemy, to further exploit them in downstream analyses, and demonstrate the effectiveness of our pipeline for structural variant calling in beta-lactam resistance genes in the same set of A. baumannii isolates, identifying various transposon structures for carbapenem resistance in chromosome, as well as plasmids. We identify a novel plasmid structure in two multidrug-resistant clinical isolates that had previously been studied, and which could be important for their resistance phenotypes.

Protistan-Bacterial Microbiota Exhibit Stronger Species Sorting and Greater Network Connectivity Offshore than Nearshore across a Coast-to-Basin Continuum

Microbial organisms play a crucial role in global nutrient cycling. Few studies have attempted to simultaneously investigate the community assembly of microeukaryotes and prokaryotes and their association patterns in oceanic waters.

Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species

Host-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted proteincoding genes, but on opposite strands or on different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species. Results are available for public access at https://omics.informatics.indiana.edu/GutBac.Author summaryMany reference genomes for studying human gut microbiome are available, but knowledge about how microbial organisms work is limited. Identification of proteins at individual species or community level provides direct insight into the functionality of microbial organisms. By analyzing more than a thousand metaproteomics datasets, we examined protein landscapes of more than two thousands of microbial species that may be important to human health and diseases. This work demonstrated new applications of metaproteomic datasets for studying individual genomes. We made the analysis results available through the GutBac website, which we believe will become a resource for studying microbial species important for human health and diseases.

Study to Assess the Risk of Transmission of Microbial Organisms and their Resistance Pattern on Dresses and Stethoscopes of Health Care Workers

The main objective of the study was to find out the contamination of different types of microbial organisms and their resistance pattern on the dresses and stethoscopes of the health care workers in order to evaluate the risk of transmission of microbial organisms and its consequences on safety of the patient and control of hospital acquired infections (HAIs). Swabs were taken from the dresses of 100 different healthcare workers from 4 different areas namely collar, pocket, sleeves and sides and 100 stethoscopes (diaphragm) were tested for bacteriological analysis using standard techniques. Then each health care worker were given a structured questionnaire and requested to fill the form which includes his or her speciality/unit, cadre, practice of hand hygiene, white-coat or uniform usage (Example: duration of usage, frequency of washing, type of washing etc). The collar and pockets were found to be the most contaminated areas. Gram-positive cocci such as Staphylococcus aureus and Coagulase-negative Staphylococci (CONS) were the important organisms isolated from the dresses and stethoscopes of health care workers followed by Gram-negative bacilli such as Pseudomonas aeruginosa and Klebsiella species. Among the microbial organisms, Staphylococcus aureus and CONS were resistant to the drugs like Co-trimoxazole and Penicillin-G. Organisms isolated from the dresses and stethoscopes of health care workers could be a source of infection to immunocompromised patients. Hence, frequent washing of the dresses and strict disinfectant practices of the stethoscopes will minimize the contamination with microbial organisms and the patient safety is improved in the hospital environment.

Codon usage bias and environmental adaptation in microbial organisms

In each genome, synonymous codons are used with different frequencies; this general phenomenon is known as codon usage bias. It has been previously recognised that codon usage bias could affect the cellular fitness and might be associated with the ecology of microbial organisms. In this exploratory study, we investigated the relationship between codon usage bias, lifestyles (thermophiles vs. mesophiles; pathogenic vs. non-pathogenic; halophilic vs. non-halophilic; aerobic vs. anaerobic and facultative) and habitats (aquatic, terrestrial, host-associated, specialised, multiple) of 615 microbial organisms (544 bacteria and 71 archaea). Principal component analysis revealed that species with given phenotypic traits and living in similar environmental conditions have similar codon preferences, as represented by the relative synonymous codon usage (RSCU) index, and similar spectra of tRNA availability, as gauged by the tRNA gene copy number (tGCN). Moreover, by measuring the average tRNA adaptation index (tAI) for each genome, an index that can be associated with translational efficiency, we observed that organisms able to live in multiple habitats, including facultative organisms, mesophiles and pathogenic bacteria, are characterised by a reduced translational efficiency, consistently with their need to adapt to different environments. Our results show that synonymous codon choices might be under strong translational selection, which modulates the choice of the codons to differently match tRNA availability, depending on the organism’s lifestyle needs. To our knowledge, this is the first large-scale study that examines the role of codon bias and translational efficiency in the adaptation of microbial organisms to the environment in which they live.

Rapid soil organic carbon decomposition in river systems: effects of the aquatic microbial community and hydrodynamical disturbance

Different erosion processes deliver large amounts of terrestrial soil organic carbon (SOC) to rivers. Mounting evidence indicates that a significant fraction of this SOC, which displays a wide range of ages, is rapidly decomposed after entering the river system. The mechanisms explaining this rapid decomposition of previously stable SOC still remain unclear. In this study, we investigated the relative importance of two mechanisms that possibly control SOC decomposition rates in aquatic systems: (i) in the river water SOC is exposed to the aquatic microbial community which is able to metabolize SOC much more quickly than the soil microbial community and (ii) SOC decomposition in rivers is facilitated due to the hydrodynamic disturbance of suspended sediment particles. We performed different series of short-term (168 h) incubations quantifying the rates of SOC decomposition in an aquatic system under controlled conditions. Organic carbon decomposition was measured continuously through monitoring dissolved O2 (DO) concentration using a fiber-optic sensor (FireStingO2, PyroScience). Under both shaking and standing conditions, we found a significant difference in decomposition rate between SOC with aquatic microbial organisms added (SOC + AMO) and without aquatic microbial organisms (SOC − AMO). The presence of an aquatic microbial community enhanced the SOC decomposition process by 70 %–128 % depending on the soil type and shaking–standing conditions. While some recent studies suggested that aquatic respiration rates may have been substantially underestimated by performing measurement under stationary conditions, our results indicate that the effect of hydrodynamic disturbance is relatively minor, under the temperature conditions, for the soil type, and for the suspended matter concentration range used in our experiments. We propose a simple conceptual model explaining these contrasting results.