Background: Contemporary coral reef research has firmly established that a genomic approach is urgently needed to better understand the effects of anthropogenic environmental stress and global climate change on coral holobiont interactions. Here we present KEGG orthology-based annotation of the complete genome sequence of the scleractinian coralAcropora digitiferaand provide the first comprehensive view of the genome of a reef-building coral by applying advanced bioinformatics.Description: Sequences from the KEGG database of protein function were used to construct hidden Markov models. These models were used to search the predicted proteome ofA. digitiferato establish complete genomic annotation. The annotated dataset is published in ZoophyteBase, an open access format with different options for searching the data. A particularly useful feature is the ability to use a Google-like search engine that links query words to protein attributes. We present features of the annotation that underpin the molecular structure of key processes of coral physiology that include (1) regulatory proteins of symbiosis, (2) planula and early developmental proteins, (3) neural messengers, receptors and sensory proteins, (4) calcification and Ca²⁺-signalling proteins, (5) plant-derived proteins, (6) proteins of nitrogen metabolism, (7) DNA repair proteins, (8) stress response proteins, (9) antioxidant and redox-protective proteins, (10) proteins of cellular apoptosis, (11) microbial symbioses and pathogenicity proteins, (12) proteins of viral pathogenicity, (13) toxins and venom, (14) proteins of the chemical defensome and (15) coral epigenetics.Conclusions: We advocate that providing annotation in an open-access searchable database available to the public domain will give an unprecedented foundation to interrogate the fundamental molecular structure and interactions of coral symbiosis and allow critical questions to be addressed at the genomic level based on combined aspects of evolutionary, developmental, metabolic, and environmental perspectives.

Obesity is a disorder identified by an inappropriate increase in weight in relation to height and is considered by many international health institutions to be a major pandemic of the 21st century. The gut microbial ecosystem impacts obesity in multiple ways that yield downstream metabolic consequences, such as affecting systemic inflammation, immune response, and energy harvest, but also the gut–host interface. Metabolomics, a systematized study of low-molecular-weight molecules that take part in metabolic pathways, represents a serviceable method for elucidation of the crosstalk between hosts’ metabolism and gut microbiota. In the present review, we confer about clinical and preclinical studies exploring the association of obesity and related metabolic disorders with various gut microbiome profiles, and the effects of several dietary interventions on gut microbiome composition and the metabolome. It is well established that various nutritional interventions may serve as an efficient therapeutic approach to support weight loss in obese individuals, yet no agreement exists in regard to the most effective dietary protocol, both in the short and long term. However, metabolite profiling and the gut microbiota composition might represent an opportunity to methodically establish predictors for obesity control that are relatively simple to measure in comparison to traditional approaches, and it may also present a tool to determine the optimal nutritional intervention to ameliorate obesity in an individual. Nevertheless, a lack of adequately powered randomized trials impedes the application of observations to clinical practice.

Human milk not only provides a perfect balance of nutrients to meet all the needs of the infant in the first months of life but also contains a variety of bacteria that play a key role in tailoring the neonatal faecal microbiome. Microbiome analysis of human milk and infant faeces from mother-breastfed infant pairs was performed by sequencing the V1–V3 region of the 16S rRNA gene using the Illumina MiSeq platform. According to the results, there is a connection in the composition of the microbiome in each mother-breastfed infant pair, supporting the hypothesis that the infant’s gut is colonised with bacteria from human milk. MiSeq sequencing also revealed high biodiversity of the human milk microbiome and the infant faecal microbiome, whose composition changes during lactation and infant development, respectively. A total of 28 genetically distinct strains were selected by hierarchical cluster analysis of RAPD-PCR (Random Amplified Polymorphic DNA-Polymerase Chain Reaction) electrophoresis profiles of 100 strains isolated from human milk and identified by 16S RNA sequencing. Since certain cellular molecules may support their use as probiotics, the next focus was to detect (S)-layer proteins, bacteriocins and exopolysaccharides (EPSs) that have potential as therapeutic biomolecules. SDS-PAGE (Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis) coupled with LC-MS (liquid chromatography-mass spectrometry) analysis revealed that four Levilactobacillus brevis strains expressed S-layer proteins, which were identified for the first time in strains isolated from human milk. The potential biosynthesis of plantaricin was detected in six Lactiplantibacillus plantarum strains by PCR analysis and in vitro antibacterial studies. ¹H NMR (Proton Nuclear Magnetic Resonance) analysis confirmed EPS production in only one strain, Limosilactobacillus fermentum MC1. The overall microbiome analysis suggests that human milk contributes to the establishment of the intestinal microbiota of infants. In addition, it is a promising source of novel Lactobacillus strains expressing specific functional biomolecules.

A decade ago, when the Human Microbiome Project was starting, urinary tract (UT) was not included because the bladder and urine were considered to be sterile. Today, we are presented with evidence that healthy UT possesses native microbiota and any major event disrupting its “equilibrium” can impact the host also. This dysbiosis often leads to cystitis symptoms, which is the most frequent lower UT complaint, especially among women. Cystitis is one of the most common causes of antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of majority of cystitis cases is a single pathogen overgrowth, or a systemic disorder affecting entire UT microbiota. There are relatively few studies monitoring changes and dynamics of UT microbiota in cystitis patients, making this field of research still an unknown. In this study variations to the UT microbiota of cystitis patients were identified and microbial dynamics has been modeled. The microbial genetic profile of urine samples from 28 patients was analyzed by 16S rDNA Illumina sequencing and bioinformatics analysis. One patient with bacterial cystitis symptoms was prescribed therapy based on national guideline recommendations on antibacterial treatment of urinary tract infections (UTI) and UT microbiota change was monitored by 16S rDNA sequencing on 24 h basis during the entire therapy duration. The results of sequencing implied that a particular class of bacteria is associated with majority of cystitis cases in this study. The contributing role of this class of bacteria – Gammaproteobacteria, was further predicted by generalized Lotka-Volterra modeling (gLVM). Longitudinal microbiota insight obtained from a single patient under prescribed antimicrobial therapy revealed rapid and extensive changes in microbial composition and emphasized the need for current guidelines revision in regards to therapy duration. Models based on gLVM indicated protective role of two taxonomic classes of bacteria, Actinobacteria and Bacteroidia class, which appear to actively suppress pathogen overgrowth.

The high G+C content and large genome size make the sequencing and assembly of Streptomyces genomes more difficult than for other bacteria. Many pharmaceutically important natural products are synthesized by modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The analysis of such gene clusters is difficult if the genome sequence is not of the highest quality, because clusters can be distributed over several contigs, and sequencing errors can introduce apparent frameshifts into the large PKS and NRPS proteins. An additional problem is that the modular nature of the clusters results in the presence of imperfect repeats, which may cause assembly errors. The genome sequence of Streptomyces tsukubaensis NRRL18488 was scanned for potential PKS and NRPS modular clusters. A phylogenetic approach was used to identify multiple contigs belonging to the same cluster. Four PKS clusters and six NRPS clusters were identified. Contigs containing cluster sequences were analyzed in detail by using the ClustScan program, which suggested the order and orientation of the contigs. The sequencing of the appropriate PCR products confirmed the ordering and allowed the correction of apparent frameshifts resulting from sequencing errors. The product chemistry of such correctly assembled clusters could also be predicted. The analysis of one PKS cluster showed that it should produce a bafilomycin-like compound, and reverse transcription (RT)-PCR was used to show that the cluster was transcribed.

An increasing amount of evidence implies that native microbiota is a constituent part of a healthy urinary tract (UT), making it an ecosystem on its own. What is still not clear is whether the origin of the urinary microbial community is the indirect consequence of the more abundant gut microbiota or a more distinct separation exists between these two systems. Another area of uncertainty is the existence of a link between the shifts in UT microbial composition and both the onset and persistence of cystitis symptoms. Cystitis is one of the most common reasons for antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of the majority of cystitis cases is a single pathogen overgrowth or a systemic disorder affecting the entire urinary microbiota. There is an increasing trend in studies monitoring changes and dynamics of UT microbiota, but this field of research is still in its infancy. Using NGS and bioinformatics, it is possible to obtain microbiota taxonomic profiles directly from urine samples, which can provide a window into microbial diversity (or the lack of) underlying each patient’s cystitis symptoms. However, while microbiota refers to the living collection of microorganisms, an interchangeably used term microbiome referring to the genetic material of the microbiota is more often used in conjunction with sequencing data. It is this vast amount of sequences, which are truly “Big Data”, that allow us to create models that describe interactions between different species contributing to an UT ecosystem, when coupled with machine-learning techniques. Although in a simplified predator—prey form these multi-species interaction models have the potential to further validate or disprove current beliefs; whether it is the presence or the absence of particular key players in a UT microbial ecosystem, the exact cause or consequence of the otherwise unknown etiology in the majority of cystitis cases. These insights might prove to be vital in our ongoing struggle against pathogen resistance and offer us new and promising clinical markers.