Bariatric surgery is often the preferred method to resolve obesity and diabetes, with ∼800,000 cases worldwide yearly and high outcome variability. The ability to predict the long-term body mass index (BMI) change following surgery has important implications for individuals and the health care system in general. Given the tight connection between eating habits, sugar consumption, BMI, and the gut microbiome, we tested whether the microbiome before any treatment is associated with different treatment outcomes, as well as other intakes (high-density lipoproteins [HDL], triglycerides, etc.). A projection of the gut microbiome composition of obese (sampled before and after bariatric surgery) and lean patients into principal components was performed, and the relation between this projection and surgery outcome was studied. The projection revealed three different microbiome profiles belonging to lean, obese, and obese individuals who underwent bariatric surgery, with the postsurgery microbiome more different from the lean microbiome than the obese microbiome. The same projection allowed for a prediction of BMI loss following bariatric surgery, using only the presurgery microbiome. The microbial changes following surgery were an increase in the relative abundance of Proteobacteria and Fusobacteria and a decrease in Firmicutes. The gut microbiome can be decomposed into main components depicting the patient's development and predicting in advance the outcome. Those may be translated into the better clinical management of obese individuals planning to undergo metabolic surgery. IMPORTANCE BMI and diabetes can affect the gut microbiome composition. Bariatric surgery has large variabilities in the outcome. The microbiome was previously shown to be a good predictor for multiple diseases. We analyzed here the gut microbiome before and after bariatric surgery and showed the following. (i) The microbiome before surgery can be used to predict surgery outcomes. (ii) The postsurgery microbiome drifts further away from the lean microbiome than the microbiome of the presurgery obese patients. These results can lead to a microbiome-based presurgery decision whether to perform surgery.

Anti-CRISPR (Acr) proteins are encoded by (pro)viruses to inhibit their host's CRISPR-Cas systems. Genes encoding Acr and Aca (Acr associated) proteins often colocalize to form acr-aca operons. Here, we present AcaFinder as the first Aca genome mining tool. AcaFinder can (i) predict Acas and their associated acr-aca operons using guilt-by-association (GBA); (ii) identify homologs of known Acas using an HMM (Hidden Markov model) database; (iii) take input genomes for potential prophages, CRISPR-Cas systems, and self-targeting spacers (STSs); and (iv) provide a standalone program (https://github.com/boweny920/AcaFinder) and a web server (http://aca.unl.edu/Aca). AcaFinder was applied to mining over 16,000 prokaryotic and 142,000 gut phage genomes. After a multistep filtering, 36 high-confident new Aca families were identified, which is three times that of the 12 known Aca families. Seven new Aca families were from major human gut bacteria (Bacteroidota, Actinobacteria, and Fusobacteria) and their phages, while most known Aca families were from Proteobacteria and Firmicutes. A complex association network between Acrs and Acas was revealed by analyzing their operonic colocalizations. It appears very common in evolution that the same aca genes can recombine with different acr genes and vice versa to form diverse acr-aca operon combinations. IMPORTANCE At least four bioinformatics programs have been published for genome mining of Acrs since 2020. In contrast, no bioinformatics tools are available for automated Aca discovery. As the self-transcriptional repressor of acr-aca operons, Aca can be viewed as anti-anti-CRISPRs, with great potential in the improvement of CRISPR-Cas technology. Although all the 12 known Aca proteins contain a conserved helix-turn-helix (HTH) domain, not all HTH-containing proteins are Acas. However, HTH-containing proteins with adjacent Acr homologs encoded in the same genetic operon are likely Aca proteins. AcaFinder implements this guilt-by-association idea and the idea of using HMMs of known Acas for homologs into one software package. Applying AcaFinder in screening prokaryotic and gut phage genomes reveals a complex acr-aca operonic colocalization network between different families of Acrs and Acas.

Zinc is an essential nutrient in biological systems due to its structural or catalytic requirement in proteins involved in diverse cellular processes. To meet this cellular demand, microbes must acquire sufficient zinc from their environment. However, many environments have low zinc availability. One of the mechanisms used by bacteria to acquire zinc is through the production of small molecules known as zincophores. Similar to bacterial siderophores used for iron uptake, zincophores are synthesized by the bacterium and exported and then reimported as zincophore-zinc complexes. Thus far, only four zincophores have been described, including two from the human pathogens Staphylococcus aureus and Pseudomonas aeruginosa, in which they play a critical role in zinc acquisition during infection, and one in a soil bacterium. To determine what other microbes may produce zincophores, we used bioinformatic analyses to identify new zincophore biosynthetic gene clusters (BGCs) and predict the diversity of molecules synthesized. Genome neighborhood network analysis identified approximately 250 unique zincophore-producing species from actinobacteria, firmicutes, proteobacteria, and fusobacteria. This indicates that zincophores are produced by diverse bacteria that inhabit a broad range of ecological niches. Many of the BGCs likely produce characterized zincophores, based on similarity to the characterized systems. However, this analysis also identified numerous BGCs that, based on the colocalization of additional modifying enzymes and sequence divergence of the biosynthetic enzymes, are likely to produce unique zincophores. Collectively, these findings provide a comprehensive understanding of the zincophore biosynthetic landscape that will be invaluable for future research on these important small molecules.IMPORTANCE Bacteria must acquire essential nutrients, including zinc, from their environment. For bacterial pathogens, this necessitates overcoming the host metal-withholding response known as nutritional immunity. A novel type of zinc uptake mechanism that involves the bacterial production of a small zinc-scavenging molecule was recently described in the human pathogens Staphylococcus aureus, Pseudomonas aeruginosa, and Yersinia pestis, as well as the soil-associated bacterium Paenibacillus mucilaginosus This suggests that zincophores may be important for zinc acquisition in diverse environments. In this study, we sought to identify other zincophore-producing bacteria using bioinformatics. We identified almost 250 unique zincophore-producing species, including human and animal pathogens, as well as isolates from soil, rhizosphere, plant, and marine habitats. Crucially, we observed diversity at the amino acid and gene organization levels, suggesting that many of these species are producing unique zincophores. Together, our findings highlight the importance of zincophores for a broad array of bacteria living in diverse environments.

Whole-genome bacterial sequences are required to better understand microbial functions, niche-specific bacterial metabolism, and disease states. Although genomic sequences are available for many of the human-associated bacteria from commonly tested body habitats (e.g., feces), as few as 13% of bacterium-derived reads from other sites such as the skin map to known bacterial genomes. To facilitate a better characterization of metagenomic shotgun reads from underrepresented body sites, we collected over 10,000 bacterial isolates originating from 14 human body habitats, identified novel taxonomic groups based on full-length 16S rRNA gene sequences, clustered the sequences to ensure that no individual taxonomic group was overselected for sequencing, prioritized bacteria from underrepresented body sites (such as skin and respiratory and urinary tracts), and sequenced and assembled genomes for 665 new bacterial strains. Here, we show that addition of these genomes improved read mapping rates of Human Microbiome Project (HMP) metagenomic samples by nearly 30% for the previously underrepresented phylum Fusobacteria, and 27.5% of the novel genomes generated here had high representation in at least one of the tested HMP samples, compared to 12.5% of the sequences in the public databases, indicating an enrichment of useful novel genomic sequences resulting from the prioritization procedure. As our understanding of the human microbiome continues to improve and to enter the realm of therapy developments, targeted approaches such as this to improve genomic databases will increase in importance from both an academic and a clinical perspective.IMPORTANCE The human microbiome plays a critically important role in health and disease, but current understanding of the mechanisms underlying the interactions between the varying microbiome and the different host environments is lacking. Having access to a database of fully sequenced bacterial genomes provides invaluable insights into microbial functions, but currently sequenced genomes for the human microbiome have largely come from a limited number of body sites (primarily feces), while other sites such as the skin, respiratory tract, and urinary tract are underrepresented, resulting in as little as 13% of bacterium-derived reads mapping to known bacterial genomes. Here, we sequenced and assembled 665 new bacterial genomes, prioritized from a larger database to select underrepresented body sites and bacterial taxa in the existing databases. As a result, we substantially improve mapping rates for samples from the Human Microbiome Project and provide an important contribution to human bacterial genomic databases for future studies.

PMA (propidium monoazide) is one of the few methods that are compatible with metagenomic sequencing to characterize the live/intact microbiota. However, its efficiency in complex communities such as saliva and feces is still controversial. An effective method for depleting host and dead bacterial DNA in human microbiome samples is lacking. Here, we systematically evaluate the efficiency of osmotic lysis and PMAxx treatment (lyPMAxx) in characterizing the viable microbiome with four live/dead Gram+/Gram- microbial strains in simple synthetic and spiked-in complex communities. We show that lyPMAxx-quantitative PCR (qPCR)/sequencing eliminated more than 95% of the host and heat-killed microbial DNA and had a much smaller effect on the live microbes in both simple mock and spiked-in complex communities. The overall microbial load and the alpha diversity of the salivary and fecal microbiome were decreased by lyPMAxx, and the relative abundances of the microbes were changed. The relative abundances of Actinobacteria, Fusobacteria, and Firmicutes in saliva were decreased by lyPMAxx, as was that of Firmicutes in feces. We also found that the frequently used sample storage method, freezing with glycerol, killed or injured 65% and 94% of the living microbial cells in saliva and feces, respectively, with the Proteobacteria phylum affected most in saliva and the Bacteroidetes and Firmicutes phyla affected most in feces. By comparing the absolute abundance variation of the shared species among different sample types and individuals, we found that sample habitat and personal differences affected the response of microbial species to lyPMAxx and freezing. IMPORTANCE The functions and phenotypes of microbial communities are largely defined by viable microbes. Through advanced nucleic acid sequencing technologies and downstream bioinformatic analyses, we gained an insight into the high-resolution microbial community composition of human saliva and feces, yet we know very little about whether such community DNA sequences represent viable microbes. PMA-qPCR was used to characterize the viable microbes in previous studies. However, its efficiency in complex communities such as saliva and feces is still controversial. By spiking-in four live/dead Gram+/Gram- bacterial strains, we demonstrate that lyPMAxx can effectively discriminate between live and dead microbes in the simple synthetic community and complex human microbial communities (saliva and feces). In addition, freezing storage was found to kill or injure the microbes in saliva and feces significantly, as measured with lyPMAxx-qPCR/sequencing. This method has a promising prospect in the viable/intact microbiota detection of complex human microbial communities.