From Genomes to Phenotypes: Traitar, the Microbial Trait Analyzer

The vertical partitioning between denitrification and dissimilatory nitrate reduction to ammonium of coastal mangrove sediment microbiomes

Mangrove aquatic ecosystems receive substantial nitrogen (N) inputs from both land and sea, playing critical roles in modulating coastal N fluxes. The microbially-mediated competition between denitrification and dissimilatory nitrate reduction to ammonium (DNRA) in mangrove sediments significantly impacts the N fate and transformation processes. Despite their recognized role in N loss or retention in surface sediments, how these two processes vary with sediment depths and their influential factors remain elusive. Here, we employed a comprehensive approach combining 15N isotope tracer, quantitative PCR (qPCR) and metagenomics to verify the vertical dynamics of denitrification and DNRA across five 100-cm mangrove sediment cores. Our results revealed a clear vertical partitioning, with denitrification dominated in 0-30 cm sediments, while DNRA played a greater role with increasing depths. Quantification of denitrification and DNRA functional genes further explained this phenomenon. Taxonomic analysis identified Pseudomonadota as the primary denitrification group, while Planctomycetota and Pseudomonadota exhibited high proportion in DNRA group. Furthermore, genome-resolved metagenomics revealed multiple salt-tolerance strategies and aromatic compound utilization potential in denitrification assemblages. This allowed denitrification to dominate in oxygen-fluctuating and higher-salinity surface sediments. However, the elevated C/N in anaerobic deep sediments favored DNRA, tending to generate biologically available NH4+. Together, our results uncover the depth-related variations in the microbially-mediated competition between denitrification and DNRA, regulating N dynamics in mangrove ecosystems.

Gut microbiome diversity within Clostridia is negatively associated with human obesity

Clostridia are abundant in the human gut and comprise families associated with host health such as Oscillospiraceae, which has been correlated with leanness. However, culturing bacteria within this family is challenging, leading to their detection primarily through 16S rRNA amplicon sequencing, which has a limited ability to unravel diversity at low taxonomic levels, or by shotgun metagenomics, which is hindered by its high costs and complexity. In this cross-sectional study involving 114 Colombian adults, we used an amplicon-based sequencing strategy with alternative markers-gyrase subunit B (gyrB) and DNA K chaperone heat protein 70 (dnaK)-that evolve faster than the 16S rRNA gene. Comparing the diversity and abundance observed with the three markers in our cohort, we found a reduction in the diversity of Clostridia, particularly within Lachnospiraceae and Oscillospiraceae among obese individuals [as measured by the body mass index (BMI)]. Within Lachnospiraceae, the diversity of Ruminococcus_A negatively correlated with BMI. Within Oscillospiraceae, the genera CAG-170 and Vescimonas also exhibited this negative correlation. In addition, the abundance of Vescimonas was negatively correlated with BMI. Leveraging shotgun metagenomic data, we conducted a phylogenetic and genomic characterization of 120 metagenome-assembled genomes from Vescimonas obtained from a larger sample of the same cohort. We identified 17 of the 72 reported species. The functional annotation of these genomes showed the presence of multiple carbohydrate-active enzymes, particularly glycosyl transferases and glycoside hydrolases, suggesting potential beneficial roles in fiber degradation, carbohydrate metabolism, and butyrate production.

IMPORTANCE

The gut microbiota is diverse across various taxonomic levels. At the intra-species level, it comprises multiple strains, some of which may be host-specific. However, our understanding of fine-grained diversity has been hindered by the use of the conserved 16S rRNA gene. While shotgun metagenomics offers higher resolution, it remains costly, may fail to identify specific microbes in complex samples, and requires extensive computational resources and expertise. To address this, we employed a simple and cost-effective analysis of alternative genetic markers to explore diversity within Clostridia, a crucial group within the human gut microbiota whose diversity may be underestimated. We found high intra-species diversity for certain groups and associations with obesity. Notably, we identified Vescimonas, an understudied group. Making use of metagenomic data, we inferred functionality, uncovering potential beneficial roles in dietary fiber and carbohydrate degradation, as well as in short-chain fatty acid production.

MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques

BACKGROUND

In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes.

RESULTS

The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest.

CONCLUSIONS

The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context.

Advancements in prokaryotic systematics and the role of Bergey's International Society for Microbial Systematicsin addressing challenges in the meta-data era

Prokaryotes are ubiquitous in the biosphere, important for human health and drive diverse biological and environmental processes. Systematics of prokaryotes, whose origins can be traced to the discovery of microorganisms in the 17th century, has transitioned from a phenotype-based classification to a more comprehensive polyphasic taxonomy and eventually to the current genome-based taxonomic approach. This transition aligns with a foundational shift from studies focused on phenotypic traits that have limited comparative value to those using genome sequences. In this context, Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB) and Bergey's International Society for Microbial Systematics (BISMiS) play a pivotal role in guiding prokaryotic systematics. This review focuses on the historical development of prokaryotic systematics with a focus on the roles of BMSAB and BISMiS. We also explore significant contributions and achievements by microbiologists, highlight the latest progress in the field and anticipate challenges and opportunities within prokaryotic systematics. Additionally, we outline five focal points of BISMiS that are aimed at addressing these challenges. In conclusion, our collaborative effort seeks to enhance ongoing advancements in prokaryotic systematics, ensuring its continued relevance and innovative characters in the contemporary landscape of genomics and bioinformatics.

Bosea rubneri sp. nov. Isolated from Organically Grown Allium cepa

Strain ZW T0_25T was isolated from an onion sample (Allium cepa var. Hytech F1) within a storage trial and proofed to be a novel, aerobic, Gram-stain negative, rod-shaped bacterial strain. Analyses of the 16S rRNA gene sequence and of the whole draft genome sequences, i.e., digital DNA-DNA hybridization (dDDH), Average Nucleotide Identity (ANI) and Average Amino Acid Identity (AAI) showed that this strain represents a new species of the genus Bosea. The genome size of strain ZW T0_25T is 6.19 Mbp, and the GC content is 66.9%. As whole cell sugars, rhamnose, ribose and glucose were identified. Ubiquinone Q-10 is the major respiratory quinone with 97.8%. Polar lipids in strain ZW T0_25T are composed of one phosphatidylethanolamine, one phosphatidylglycerol, one aminophospholipid, two aminolipids, one glycolipid and two phospholipids whereas the fatty acid profile predominantly consists of C18:1 w7c (63.3%), C16:1 w7c (19.5%) and C16:0 (7.1%). Phenotypic traits were tested in the wet lab as well as predicted in silico from genome data. Therefore, according to this polyphasic approach, the new name Bosea rubneri sp. nov. with the type strain ZW T0_25T (= DSM 116094 T = LMG 33093 T) is proposed.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Social and environmental transmission spread different sets of gut microbes in wild mice

Gut microbes shape many aspects of organismal biology, yet how these key bacteria transmit among hosts in natural populations remains poorly understood. Recent work in mammals has emphasized either transmission through social contacts or indirect transmission through environmental contact, but the relative importance of different routes has not been directly assessed. Here we used a novel radio-frequency identification-based tracking system to collect long-term high-resolution data on social relationships, space use and microhabitat in a wild population of mice (Apodemus sylvaticus), while regularly characterizing their gut microbiota with 16S ribosomal RNA profiling. Through probabilistic modelling of the resulting data, we identify positive and statistically distinct signals of social and environmental transmission, captured by social networks and overlap in home ranges, respectively. Strikingly, microorganisms with distinct biological attributes drove these different transmission signals. While the social network effect on microbiota was driven by anaerobic bacteria, the effect of shared space was most influenced by aerotolerant spore-forming bacteria. These findings support the prediction that social contact is important for the transfer of microorganisms with low oxygen tolerance, while those that can tolerate oxygen or form spores may be able to transmit indirectly through the environment. Overall, these results suggest social and environmental transmission routes can spread biologically distinct members of the mammalian gut microbiota.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Microbial composition associated with biliary stents in patients undergoing pancreatic resection for cancer

Malignant bile duct obstruction is typically treated by biliary stenting, which however increases the risk of bacterial infections. Here, we analyzed the microbial content of the biliary stents from 56 patients finding widespread microbial colonization. Seventeen of 36 prevalent stent species are common oral microbiome members, associate with disease conditions when present in the gut, and include dozens of biofilm- and antimicrobial resistance-related genes. This work provides an overview of the microbial communities populating the stents.

Establishment of a non-Westernized gut microbiota in men who have sex with men is associated with sexual practices

The human gut microbiota is influenced by various factors, including health status and environmental conditions, yet considerable inter-individual differences remain unexplained. Previous studies identified that the gut microbiota of men who have sex with men (MSM) is distinct from that of non-MSM. Here, we reveal through species-level microbiota analysis using shotgun metagenomics that the gut microbiota of many MSM with Western origin resembles gut microbial communities of non-Westernized populations. Specifically, MSM gut microbiomes are frequently dominated by members of the Prevotellaceae family, including co-colonization of species from the Segatella copri complex and unknown Prevotellaceae members. Questionnaire-based analysis exploring inter-individual differences in MSM links specific sexual practices to microbiota composition. Moreover, machine learning identifies microbial features associated with sexual activities in MSM. Together, this study shows associations of sexual activities with gut microbiome alterations in MSM, which may have a large impact on population-based microbiota studies.

Enhanced methane production with co-feeding spent coffee grounds using spare capacity of existing anaerobic food waste digesters

With increasing coffee consumption worldwide, the efficient and sustainable management of spent coffee grounds (SCG) has become increasingly challenging. This study investigated the anaerobic co-digestion of small amounts of SCG with food waste (FW) at increasing co-feeding ratios of 1:100-1:10 (volatile solids basis) to assess the possibility of SCG treatment using the spare capacity of existing anaerobic digesters. Co-feeding SCG increased methane production compared to FW mono-digestion in the tested range of co-feeding ratios without compromising process stability. Methane yield did not further increase when the SCG/FW ratio increased above 4%, and process failure occurred at a 1:10 co-feeding ratio without trace element supplementation. The enhanced methanogenic performance was attributed to increased protein removal efficiency, which was potentially related to the promotion of peptide hydrolysis. The overall results suggest that co-feeding appropriate small amounts of SCG to FW digesters can be a realistic sustainable option for SCG management.

Functional host-specific adaptation of the intestinal microbiome in hominids

Fine-scale knowledge of the changes in composition and function of the human gut microbiome compared that of our closest relatives is critical for understanding the evolutionary processes underlying its developmental trajectory. To infer taxonomic and functional changes in the gut microbiome across hominids at different timescales, we perform high-resolution metagenomic-based analyzes of the fecal microbiome from over two hundred samples including diverse human populations, as well as wild-living chimpanzees, bonobos, and gorillas. We find human-associated taxa depleted within non-human apes and patterns of host-specific gut microbiota, suggesting the widespread acquisition of novel microbial clades along the evolutionary divergence of hosts. In contrast, we reveal multiple lines of evidence for a pervasive loss of diversity in human populations in correlation with a high Human Development Index, including evolutionarily conserved clades. Similarly, patterns of co-phylogeny between microbes and hosts are found to be disrupted in humans. Together with identifying individual microbial taxa and functional adaptations that correlate to host phylogeny, these findings offer insights into specific candidates playing a role in the diverging trajectories of the gut microbiome of hominids. We find that repeated horizontal gene transfer and gene loss, as well as the adaptation to transient microaerobic conditions appear to have played a role in the evolution of the human gut microbiome.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

Metagenomic Analysis of a Concrete Bridge Reveals a Microbial Community Dominated by Halophilic Bacteria and Archaea

Concrete hosts a small but diverse microbiome that changes over time. Shotgun metagenomic sequencing would enable assessment of both the diversity and function of the microbial community in concrete, but a number of unique challenges make this difficult for concrete samples. The high concentration of divalent cations in concrete interferes with nucleic acid extraction, and the extremely low biomass in concrete means that DNA from laboratory contamination may be a large fraction of the sequence data. Here, we develop an improved method for DNA extraction from concrete, with higher yield and lower laboratory contamination. To show that this method provides DNA of sufficient quality and quantity to do shotgun metagenomic sequencing, DNA was extracted from a sample of concrete obtained from a road bridge and sequenced with an Illumina MiSeq system. This microbial community was dominated by halophilic Bacteria and Archaea, with enriched functional pathways related to osmotic stress responses. Although this was a pilot-scale effort, we demonstrate that metagenomic sequencing can be used to characterize microbial communities in concrete and that older concrete structures may host different microbes than recently poured concrete. IMPORTANCE Prior work on the microbial communities of concrete focused on the surfaces of concrete structures such as sewage pipes or bridge pilings, where thick biofilms were easy to observe and sample. Because the biomass inside concrete is so low, more recent analyses of the microbial communities inside concrete used amplicon sequencing methods to describe those communities. However, to understand the activity and physiology of microbes in concrete, or to develop living infrastructure, we must develop more direct methods of community analysis. The method developed here for DNA extraction and metagenomic sequencing can be used for analysis of microbial communities inside concrete and can likely be adapted for other cementitious materials.

Genome taxonomy of the genus Neptuniibacter and proposal of Neptuniibacter victor sp. nov. isolated from sea cucumber larvae

A Gram-staining-negative, oxidase-positive, strictly aerobic rod-shaped bacterium, designated strain PT1T, was isolated from the laboratory-reared larvae of the sea cucumber Apostichopus japonicus. A phylogenetic analysis based on the 16S rRNA gene nucleotide sequences revealed that PT1T was closely related to Neptuniibacter marinus ATR 1.1T (= CECT 8938T = DSM 100783T) and Neptuniibacter caesariensis MED92T (= CECT 7075T = CCUG 52065T) showing 98.2% and 98.1% sequence similarity, respectively. However, the average nucleotide identity (ANI) and in silico DNA-DNA hybridization (DDH) values among these three strains were 72.0%-74.8% and 18.3%-19.5% among related Neptuniibacter species, which were below 95% and 70%, respectively, confirming the novel status of PT1T. The average amino acid identity (AAI) values of PT1T showing 74-77% among those strains indicated PT1T is a new species in the genus Neptuniibacter. Based on the genome-based taxonomic approach, Neptuniibacter victor sp. nov. is proposed for PT1T. The type strain is PT1T (JCM 35563T = LMG 32868T).

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Vibrio eleionomae sp. nov., isolated from shrimp (Penaeus vannamei) pond water

A novel Vibrio strain (CAIM 722^T=SW9^T=DSM 24596^T) was isolated in 2003 from water of a shrimp (Penaeus vannamei) culture pond located in Los Mochis, Sinaloa, Mexico, and taxonomically characterized using a polyphasic approach. The 16S rRNA gene sequence clustered within those of the genus Vibrio, showing high similarity to the type strains of the Porteresiae clade. Multilocus sequence analysis using eight housekeeping genes (ftsZ, gapA, gyrB, mreB, pyrH, recA, rpoA, topA and 16S rRNA) and phylogenetic analysis with 139 single-copy genes showed that the strain forms an independent branch. Whole genome sequencing and genomic analyses (average nucleotide identity, OrthoANI, average amino acid identity and in silico DNA-DNA hybridization) produced values well below the thresholds for species delineation with all methods tested. In addition, a phenotypic characterization was performed to support the description and differentiation of the novel strain from related taxa. The results obtained demonstrate that the strain represent a novel species, for which the name Vibrio eleionomae sp. nov. is proposed.

Characterization and Antibiotic Resistance of Listeria monocytogenes Strains Isolated from Greek Myzithra Soft Whey Cheese and Related Food Processing Surfaces over Two-and-a-Half Years of Safety Monitoring in a Cheese Processing Facility

Listeriosis is a serious infectious disease with one of the highest case fatality rates (ca. 20%) among the diseases manifested from bacterial foodborne pathogens in humans, while dairy products are often implicated as sources of human infection with Listeria monocytogenes. In this study, we characterized phenotypically and genetically by whole-genome sequencing (WGS) 54 L. monocytogenes strains isolated from Myzithra, a traditional Greek soft whey cheese (48 isolates), and swabs collected from surfaces of a cheese processing plant (six isolates) in the Epirus region of Greece. All but one strain of L. monocytogenes belonged to the polymerase chain reaction (PCR) serogroups IIa (16.7%) and IIb (81.5%), corresponding to serotypes 1/2a, 3a and 1/2b, 3b, 7, respectively. The latter was identified as a PCR-serogroup IVb strain (1.8%) of serotypes 4b, 4d, 4e. Bioinformatics analysis revealed the presence of five sequence types (STs) and clonal complexes (CCs); ST1, ST3, ST121, ST 155, ST398 and CC1, CC3, CC121, CC155, CC398 were thus detected in 1.9, 83.3, 11.0, 1.9, and 1.9% of the L. monocytogenes isolates, respectively. Antibiograms of the pathogen against a panel of seven selected antibiotics (erythromycin, tetracycline, benzylpenicillin, trimethoprim-sulfamethoxazole, ampicillin, ciprofloxacin, and meropenem) showed that 50 strains (92.6%), the six surface isolates also included, were intermediately resistant to ciprofloxacin and susceptible to the rest of the six antimicrobial agents tested, whereas strong resistance against the use of a single from three implicated antibiotics was recorded to four strains (7.4%) of the pathogen isolated from Myzithra cheese samples. Thence, the minimum inhibitory concentrations (MICs) were determined for erythromycin (MIC = 0.19 μg/mL), ciprofloxacin (MIC ≥ 0.19 μg/mL), and meropenem (MIC = 0.64 μg/mL), and finally, just one strain was deemed resistant to the latter antibiotic. The phylogenetic positions of the L. monocytogenes strains and their genetic variability were determined through WGS, whilst also stress response and virulence gene analysis for the isolates was conducted. Findings of this work should be useful as they could be utilized for epidemiological investigations of L. monocytogenes in the food processing environment, revealing possible contamination scenarios, and acquired antimicrobial resistance along the food production chain.

Comparative Genomic Analysis Reveals the Functional Traits and Safety Status of Lactic Acid Bacteria Retrieved from Artisanal Cheeses and Raw Sheep Milk

Lactic acid bacteria (LAB) are valuable for the production of fermented dairy products. We investigated the functional traits of LAB isolated from artisanal cheeses and raw sheep milk, assessed their safety status, and explored the genetic processes underlying the fermentation of carbohydrates. Lactiplantibacillus plantarum had the largest and more functional genome compared to all other LAB, while most of its protein-encoding genes had unknown functions. A key finding of our analysis was the overall absence of acquired resistance genes (RGs), virulence genes (VGs), and prophages, denoting that all LAB isolates fulfill safety criteria and can be used as starter or adjunct cultures. In this regard, the identified mobile genetic elements found in LAB, rather than enabling the integration of RGs or VGs, they likely facilitate the uptake of genes involved in beneficial functions and in the adaptation of LAB in dairy matrices. Another important finding of our study was that bacteriocins and CAZymes were abundant in LAB though each species was associated with specific genes, which in turn had different activity spectrums and identified applications. Additionally, all isolates were able to metabolize glucose, lactose, maltose, and sucrose, but Lactiplantibacillus plantarum was strongly associated with the fermentation of rhamnose, mannose, cellobiose, and trehalose whereas Levilactobacillus brevis with the utilization of arabinose and xylose. Altogether these results suggest that to fully exploit the beneficial properties of LAB, a combination of strains as food additives may be necessary. Interestingly, biological processes involved in the metabolism of carbohydrates that are not of direct interest for the dairy industry may yield valuable metabolites or activate pathways associated with beneficial health effects. Our results provide useful information for the development of new probiotic artisanal cheeses and probiotic starter cultures.

The person-to-person transmission landscape of the gut and oral microbiomes

The human microbiome is an integral component of the human body and a co-determinant of several health conditions1,2. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown3,4. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies5, especially those on non-infectious, microbiome-associated diseases.

Genome analysis of a plastisphere-associated Oceanimonas sp. NSJ1 sequenced on Nanopore MinION platform

Oceanimonas sp. NSJ1 was isolated from macroplastic debris collected previously from Junput, an intertidal beach, facing the northeast coastal Bay of Bengal of the Northern Indian Ocean. The genome of this isolate is closely related to Oceanimonas doudoroffii with a genome size of 3.56 Mbp. The genome annotation confirmed the presence of 5919 total genes, out of which 5809 were CDSs (coding sequences) and all are protein-coding. The genome codes for 110 RNA with 25 rRNA, 84 tRNA (transfer RNA), and one tmRNA (transfer-messenger RNA). Analyses of the annotated genome of Oceanimonas sp. NSJ1 revealed the presence of enzymes involved in the degradation of polycyclic aromatic hydrocarbons. The presence of phthalate 4,5-dioxygenase oxygenase reductase subunit pht2 within the genome also highlights the novelty of this isolate and future functional potential for studying phthalate degradation in marine environment.

Genomics, Phylogeny, and in Silico Phenotyping of Nitrosopumilus Genus

The present study reports the first genome of Nitrosopumilus extracted from the marine sponge Thoosa mismalolli. The genomic study of Nitrosopumilus genus using seven genomes type strains (N. maritimus, N. piranensis, N. zosterae, N. ureiphilus, N. adriaticus, N. oxyclinae and N. cobalaminigenes), four genomes Candidatus species (Ca. N. koreensis, Ca. N. sp. AR2, Ca. N. salaria BD31, and SZUA-335), and six reference genomes (SI075, SI0036, SI0060, SI0034, SI0048, and bin36o) isolated from marine sponge, a tropical marine fish tank, dimly lit deep coastal waters, the lower euphotic zone of coastal waters, near-surface sediment, and MAG N. sp NMAG03 isolated from Thoosa mismalolli was performed. These genomes were characterized by means of a polyphasic approach comprising multilocus sequence analysis (MLSA) of 139 single-copy genes (SCG), core-pangenome, ANI, and in silico phenotypic characterization. We found that the genomes of the Nitrosopumilus genus formed three separate clusters (A, B, and C) based in 139 SCG sequence similarity. The genomes showed values between 75.2 and 99.5% for ANI, the core genome consisted of 168 gene families and the pangenome of 6,011 gene families. Based on the genomic analyses performed, the cluster A may contain a potential new species (NMAG03), and the cluster C could be represented by three new species of the genus. Finally, based on the results shown in this polyphasic approach, we support the use of the integrated approach for genomic analysis of poorly studied genera.

Functional and Safety Characterization of Weissella paramesenteroides Strains Isolated from Dairy Products through Whole-Genome Sequencing and Comparative Genomics

Strains belonging to the Weissella genus are frequently recovered from spontaneously fermented foods. Their functional, microbial-modulating, and probiotic traits enhance not only the sensorial properties but also the nutritional value, beneficial effects, and safety of fermented products. Sporadic cases of opportunistic pathogenicity and antibiotic resistance have deprived safety status from all Weissella species, which thus remain understudied. Our study increased the number of available high-quality and taxonomically accurate W. paramesenteroides genomes by 25% (9 genomes reported, leading to a total of 36 genomes). We conducted a phylogenetic and comparative genomic analysis of the most dominant Weissella species (W. cibaria, W. paramesenteroides, W. viridescens, W. soli, W. koreensis, W. hellenica and W. thailadensis). The phylogenetic tree corroborated species assignment but also revealed phylogenetic diversity within the Weissella species, which is likely related to the adaptation of Weissella in different niches. Using robust alignment criteria, we showed the overall absence of resistance and virulence genes in Weissella spp., except for one W. cibaria isolate carrying blaTEM-181. Enrichment analysis showed the association of Weissella species several CAZymes, which are essential for biotechnological applications. Additionally, the combination of CAZyme metabolites with probiotics can potentially lead to beneficial effects for hosts, such as the inhibition of inflammatory processes and the reduction of cholesterol levels. Bacteriocins and mobile genetic elements MGEs (Inc11 plasmid and ISS1N insertion sequence) were less abundant, however W. thailadensis and W. viridescens showed significant association with specific bacteriocin-encoding genes. Lastly, an analysis of phenotypic traits underlined the need to carefully evaluate W. cibaria strains before use as food additives and suggested the possibility of employing W. paramesenteroides and W. hellenica in the fermentation process of vegetable products. More studies providing high-resolution characterization of Weissella strains from various sources are necessary to elucidate the safety of Weissella spp. and exploit their beneficial characteristics.

Codiversification of gut microbiota with humans

The gut microbiomes of human populations worldwide have many core microbial species in common. However, within a species, some strains can show remarkable population specificity. The question is whether such specificity arises from a shared evolutionary history (codiversification) between humans and their microbes. To test for codiversification of host and microbiota, we analyzed paired gut metagenomes and human genomes for 1225 individuals in Europe, Asia, and Africa, including mothers and their children. Between and within countries, a parallel evolutionary history was evident for humans and their gut microbes. Moreover, species displaying the strongest codiversification independently evolved traits characteristic of host dependency, including reduced genomes and oxygen and temperature sensitivity. These findings all point to the importance of understanding the potential role of population-specific microbial strains in microbiome-mediated disease phenotypes.

Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases

Fecal microbiota transplantation (FMT) is highly effective against recurrent Clostridioides difficile infection and is considered a promising treatment for other microbiome-related disorders, but a comprehensive understanding of microbial engraftment dynamics is lacking, which prevents informed applications of this therapeutic approach. Here, we performed an integrated shotgun metagenomic systematic meta-analysis of new and publicly available stool microbiomes collected from 226 triads of donors, pre-FMT recipients and post-FMT recipients across eight different disease types. By leveraging improved metagenomic strain-profiling to infer strain sharing, we found that recipients with higher donor strain engraftment were more likely to experience clinical success after FMT (P = 0.017) when evaluated across studies. Considering all cohorts, increased engraftment was noted in individuals receiving FMT from multiple routes (for example, both via capsules and colonoscopy during the same treatment) as well as in antibiotic-treated recipients with infectious diseases compared with antibiotic-naïve patients with noncommunicable diseases. Bacteroidetes and Actinobacteria species (including Bifidobacteria) displayed higher engraftment than Firmicutes except for six under-characterized Firmicutes species. Cross-dataset machine learning predicted the presence or absence of species in the post-FMT recipient at 0.77 average AUROC in leave-one-dataset-out evaluation, and highlighted the relevance of microbial abundance, prevalence and taxonomy to infer post-FMT species presence. By exploring the dynamics of microbiome engraftment after FMT and their association with clinical variables, our study uncovered species-specific engraftment patterns and presented machine learning models able to predict donors that might optimize post-FMT specific microbiome characteristics for disease-targeted FMT protocols.

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Biosynthetic potential of the global ocean microbiome

Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups¹, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds^2,3. However, studying this diversity to identify genomic pathways for the synthesis of such compounds⁴ and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters (‘Candidatus Eudoremicrobiaceae’) that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.

Global ocean microbiome survey reveals the bacterial family ‘Candidatus Eudoremicrobiaceae’, which includes some of the most biosynthetically diverse microorganisms in the ocean environment.

Genomic Insight Into Lacticaseibacillus paracasei SP5, Reveals Genes and Gene Clusters of Probiotic Interest and Biotechnological Potential

The Lacticaseibacillus paracasei species is comprised by nomadic bacteria inhabiting a wide variety of ecological niches, from fermented foodstuffs to host-associated microenvironments. Lc. paracasei SP5 is a novel strain, originally isolated from kefir grains that presents desirable probiotic and biotechnological attributes. In this study, we applied genomic tools to further characterize the probiotic and biotechnological potential of the strain. Firstly, whole genome sequencing and assembly, were performed to construct the chromosome map of the strain and determine its genomic stability. Lc. paracasei SP5 carriers several insertion sequences, however, no plasmids or mobile elements were detected. Furthermore, phylogenomic and comparative genomic analyses were utilized to study the nomadic attributes of the strain, and more specifically, its metabolic capacity and ability to withstand environmental stresses imposed during food processing and passage through the gastrointestinal (GI) tract. More specifically, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Carbohydrate-active enzyme (CAZymes) analyses provided evidence for the ability of the stain to utilize an array of carbohydrates as growth substrates. Consequently, genes for heat, cold, osmotic shock, acidic pH, and bile salt tolerance were annotated. Importantly bioinformatic analysis showed that the novel strain does not harbor acquired antimicrobial resistance genes nor virulence factors, in agreement with previous experimental data. Putative bacteriocin biosynthesis clusters were identified using BAGEL4, suggesting its potential antimicrobial activity. Concerning microbe-host interactions, adhesins, moonlighting proteins, exopolysaccharide (EPS) biosynthesis genes and pilins mediating the adhesive phenotype were, also, pinpointed in the genome of Lc. paracasei SP5. Validation of this phenotype was performed by employing a microbiological method and confocal microscopy. Conclusively, Lc. paracasei SP5 harbors genes necessary for the manifestation of the probiotic character and application in the food industry. Upcoming studies will focus on the mechanisms of action of the novel strain at multiple levels.

A Comparative Genomic and Safety Assessment of Six Lactiplantibacillus plantarum subsp. argentoratensis Strains Isolated from Spontaneously Fermented Greek Wheat Sourdoughs for Potential Biotechnological Application

The comparative genome analysis of six Lactiplantibacillus plantarum subsp. argentoratensis strains previously isolated from spontaneously fermented Greek wheat sourdoughs is presented. Genomic attributes related to food safety have been studied according to the European Food Safety Authority (EFSA) suggestions for the use of lactic acid bacteria (LAB) in the production of foods. Bioinformatic analysis revealed a complete set of genes for maltose, sucrose, glucose, and fructose fermentation; conversion of fructose to mannitol; folate and riboflavin biosynthesis; acetoin production; conversion of citrate to oxaloacetate; and the ability to produce antimicrobial compounds (plantaricins). Pathogenic factors were absent but some antibiotic resistance genes were detected. CRISPR and cas genes were present as well as various mobile genetic elements (MGEs) such as plasmids, prophages, and insertion sequences. The production of biogenic amines by these strains was not possible due to the absence of key genes in their genome except lysine decarboxylase associated with cadaverine; however, potential degradation of these substances was identified due to the presence of a blue copper oxidase precursor and a multicopper oxidase protein family. Finally, comparative genomics and pan-genome analysis showed genetic differences between the strains (e.g., variable pln locus), and it facilitated the identification of various phenotypic and probiotic-related properties.

Toward shotgun metagenomic approaches for microbial source tracking sewage spills based on laboratory mesocosms

Little is known about the genomic diversity of the microbial communities associated with raw municipal wastewater (sewage), including whether microbial populations specific to sewage exist and how such populations could be used to improve source attribution and apportioning in contaminated waters. Herein, we used the influent of three wastewater treatment plants in Atlanta, Georgia (USA) to perturb laboratory freshwater mesocosms, simulating sewage contamination events, and followed these mesocosms with shotgun metagenomics over a 7-day observational period. We describe 15 abundant non-redundant bacterial metagenome-assembled genomes (MAGs) ubiquitous within all sewage inocula yet absent from the unperturbed freshwater control at our analytical limit of detection. Tracking the dynamics of the populations represented by these MAGs revealed varied decay kinetics, depending on (inferred) phenotypes, e.g., anaerobes decayed faster than aerobes under the well-aerated incubation conditions. Notably, a portion of these populations showed decay patterns similar to those of common markers, Enterococcus and HF183. Despite the apparent decay of these populations, the abundance of β-lactamase encoding genes remained high throughout incubation relative to the control. Lastly, we constructed genomic libraries representing several different fecal sources and outline a bioinformatic approach which leverages these libraries for identifying and apportioning contamination signal among multiple probable sources using shotgun metagenomic data.

Effects of phenotypic variation on consumer coexistence and prey community structure

A popular idea in ecology is that trait variation among individuals from the same species may promote the coexistence of competing species. However, theoretical and empirical tests of this idea have yielded inconsistent findings. We manipulated intraspecific trait diversity in a ciliate competing with a nematode for bacterial prey in experimental microcosms. We found that intraspecific trait variation inverted the original competitive hierarchy to favour the consumer with variable traits, ultimately resulting in competitive exclusion. This competitive outcome was driven by foraging traits (size, speed and directionality) that increased the ciliate's fitness ratio and niche overlap with the nematode. The interplay between consumer trait variation and competition resulted in non-additive cascading effects-mediated through prey defence traits-on prey community assembly. Our results suggest that predicting consumer competitive population dynamics and the assembly of prey communities will require understanding the complexities of trait variation within consumer species.

Application and Challenge of 3rd Generation Sequencing for Clinical Bacterial Studies

Over the past 25 years, the powerful combination of genome sequencing and bioinformatics analysis has played a crucial role in interpreting information encoded in bacterial genomes. High-throughput sequencing technologies have paved the way towards understanding an increasingly wide range of biological questions. This revolution has enabled advances in areas ranging from genome composition to how proteins interact with nucleic acids. This has created unprecedented opportunities through the integration of genomic data into clinics for the diagnosis of genetic traits associated with disease. Since then, these technologies have continued to evolve, and recently, long-read sequencing has overcome previous limitations in terms of accuracy, thus expanding its applications in genomics, transcriptomics and metagenomics. In this review, we describe a brief history of the bacterial genome sequencing revolution and its application in public health and molecular epidemiology. We present a chronology that encompasses the various technological developments: whole-genome shotgun sequencing, high-throughput sequencing, long-read sequencing. We mainly discuss the application of next-generation sequencing to decipher bacterial genomes. Secondly, we highlight how long-read sequencing technologies go beyond the limitations of traditional short-read sequencing. We intend to provide a description of the guiding principles of the 3rd generation sequencing applications and ongoing improvements in the field of microbial medical research.

Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomes

Tree-based diversity measures incorporate phylogenetic or functional relatedness into comparisons of microbial communities. This can improve the identification of explanatory factors compared to tree-agnostic diversity measures. However, applying tree-based diversity measures to metagenome data is more challenging than for single-locus sequencing (e.g., 16S rRNA gene). Utilizing the Genome Taxonomy Database (GTDB) for species-level metagenome profiling allows for functional diversity measures based on genomic content or traits inferred from it. Still, it is unclear how metagenome-based assessments of microbiome diversity benefit from incorporating phylogeny or function into measures of diversity. We assessed this by measuring phylogeny-based, function-based, and tree-agnostic diversity measures from a large, global collection of human gut metagenomes composed of 30 studies and 2943 samples. We found tree-based measures to explain phenotypic variation (e.g., westernization, disease status, and gender) better or equivalent to tree-agnostic measures. Ecophylogenetic and functional diversity measures provided unique insight into how microbiome diversity was partitioned by phenotype. Tree-based measures greatly improved machine learning model performance for predicting westernization, disease status, and gender, relative to models trained solely on tree-agnostic measures. Our findings illustrate the usefulness of tree- and function-based measures for metagenomic assessments of microbial diversity, which is a fundamental component of microbiome science. This article is protected by copyright. All rights reserved.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

BacDive in 2022: the knowledge base for standardized bacterial and archaeal data

The bacterial metadatabase BacDive (https://bacdive.dsmz.de) has developed into a leading database for standardized prokaryotic data on strain level. With its current release (07/2021) the database offers information for 82 892 bacterial and archaeal strains covering taxonomy, morphology, cultivation, metabolism, origin, and sequence information within 1048 data fields. By integrating high-quality data from additional culture collections as well as detailed information from species descriptions, the amount of data provided has increased by 30% over the past three years. A newly developed query builder tool in the advanced search now allows complex database queries. Thereby bacterial strains can be systematically searched based on combinations of their attributes, e.g. growth and metabolic features for biotechnological applications or to identify gaps in the present knowledge about bacteria. A new interactive dashboard provides a statistic overview over the most important data fields. Additional new features are improved genomic sequence data, integrated NCBI TaxIDs and links to BacMedia, the new sister database on cultivation media. To improve the findability and interpretation of data through search engines, data in BacDive are annotated with bioschemas.org terms.

Phylogenetically divergent bacteria consortium from neutral activated sludge showed heightened potential on bioleaching spent lithium-ion batteries

Recycling of spent lithium-ion batteries (LIBs) has become a global issue because of the potential environment risks raised by spent LIBs as well as high valuable metal content remaining in them. Although bioleaching is an environmentally friendly method to recover metals from spent LIBs, the commonly utilized bioleaching bacterial consortia or strains enriched/isolated from acidic environments cannot be applied at large scales owing to their long leaching cycle and poor tolerance to organic compounds. Here, two bioleaching consortia were enriched in 60 days from neutral activated sludge and were identified phylogenetically divergent from the documented bioleaching bacteria. The results showed that the novel consortia shortened the leaching cycle almost by half when compared to the previous reported consortia or strains, of which one consortium dominated by Acidithiobacillus ferrooxidans displayed high bioleaching efficiency on LiMn₂O₄, as 69.46% lithium (Li) and 67.60% manganese (Mn) were leached out in seven days. This consortium was further domesticated using cathodic materials for 100 days and proved consisted of three mixotrophs and two chemoautotrophs, three of which were novel species from the genera Sulfobacillus and Leptospirillum. More genes coding for proteins that utilize organic compounds were annotated in the metagenomic assembled genomes (MAGs) than previously reported. A mutualistic relationship between mixotrophs and chemoautotrophs was suggested to help the consortium surviving under either organic- rich or shortage environments. The results discovered that novel bioleaching bacteria with shorter leaching cycle and higher tolerance to organics could be enriched from non-acidic environments, which showed high potential for the metal recovering from spent LIBs or other organic-rich environments.

Culturomics Approaches Expand the Diagnostic Accuracy for Sexually Transmitted Infections

Sexually transmitted infections (STIs) are a major health concern with clinical manifestations being acknowledged to cause severe reproductive impairment. Research in infectious diseases has been centered around the known major pathogens for decades. However, we have just begun to understand that the microbiota of the female genital tract is of particular importance for disease initiation, infection progression, and pathological outcome. Thus, we are now aware that many poorly described, partially not yet known, or cultured bacteria may pave the way for an infection and/or contribute to disease severity. While sequencing-based methods are an important step in diagnosing STIs, culture-based methods are still the gold-standard method in diagnostic routine, providing the opportunity to distinguish phenotypic traits of bacteria. However, current diagnostic culture routines suffer from several limitations reducing the content of information about vaginal microbiota. A detailed characterization of microbiota-associated factors is needed to assess the impact of single-bacterial isolates from the vaginal community on vaginal health and the containment of STIs. Here we provide current concepts to enable modern culture routines and create new ideas to improve diagnostic approaches with a conjunct usage of bioinformatics. We aim to enable scientists and physicians alike to overcome long-accepted limitations in culturing bacteria of interest to the human health. Eventually, this may improve the quality of culture-based diagnostics, facilitate a research interface, and lead to a broader understanding of the role of vaginal microbiota in reproductive health and STIs.

Genome-resolved metagenome and metatranscriptome analyses of thermophilic composting reveal key bacterial players and their metabolic interactions

BACKGROUND

Composting is an important technique for environment-friendly degradation of organic material, and is a microbe-driven process. Previous metagenomic studies of composting have presented a general description of the taxonomic and functional diversity of its microbial populations, but they have lacked more specific information on the key organisms that are active during the process.

RESULTS

Here we present and analyze 60 mostly high-quality metagenome-assembled genomes (MAGs) recovered from time-series samples of two thermophilic composting cells, of which 47 are potentially new bacterial species; 24 of those did not have any hits in two public MAG datasets at the 95% average nucleotide identity level. Analyses of gene content and expressed functions based on metatranscriptome data for one of the cells grouped the MAGs in three clusters along the 99-day composting process. By applying metabolic modeling methods, we were able to predict metabolic dependencies between MAGs. These models indicate the importance of coadjuvant bacteria that do not carry out lignocellulose degradation but may contribute to the management of reactive oxygen species and with enzymes that increase bioenergetic efficiency in composting, such as hydrogenases and N2O reductase. Strong metabolic dependencies predicted between MAGs revealed key interactions relying on exchange of H+, NH3, O2 and CO2, as well as glucose, glutamate, succinate, fumarate and others, highlighting the importance of functional stratification and syntrophic interactions during biomass conversion. Our model includes 22 out of 49 MAGs recovered from one composting cell data. Based on this model we highlight that Rhodothermus marinus, Thermobispora bispora and a novel Gammaproteobacterium are dominant players in chemolithotrophic metabolism and cross-feeding interactions.

CONCLUSIONS

The results obtained expand our knowledge of the taxonomic and functional diversity of composting bacteria and provide a model of their dynamic metabolic interactions.

Veronia nyctiphanis gen. nov., sp. nov., Isolated from the Stomach of the Euphausiid Nyctiphanes simplex (Hansen, 1911) in the Gulf of California, and Reclassification of Enterovibrio pacificus as Veronia pacifica comb. nov

The bacterial strain 42Xb2 ^T was isolated from a female adult krill Nyctiphanes simplex infected with the apostome parasitoid ciliate Pseudocollinia brintoni in January 2007 in the Gulf of California. The strain has the morphological, phenotypic, and molecular characteristics of the bacteria of the family Vibrionaceae. The 16S rRNA gene sequence has a similarity of 97.7% with Enterovibrio pacificus SW014 ^T and 96.1% similarity with Enterovibrio norvegicus LMG 19839 ^T. A phylogenomic and a multilocus sequence analyses placed this strain close to the genera Enterovibrio, Grimontia, and Salinivibrio, but clearly forming a separate branch from these bacterial genera. Genomic analyses presented further support this result. A novel genus Veronia gen. nov. and a species Veronia nyctiphanis sp. nov. is here described with CAIM 600 ^T (= DSM 24592 ^T = CECT 7578 ^T) as the type strain. Morphological, physiological, and genetic evidence presented here support the unification of Enterovibrio pacificus and Veronia nyctiphanis in the new genus Veronia. Enterovibrio pacificus is reclassified as Veronia pacifica. V. pacifica is assigned as the type species of the new genus Veronia.Genome Sequencing Data The GenBank/EMBL/DDBJ accession numbers for the genome sequence of Veronia nyctiphanis CAIM 600 ^T is PEIB01 and of Enterovibrio pacificus CAIM 1920 ^T is LYBM01. The 16S rRNA gene sequence of V. nyctiphanis CAIM 600 ^T is JX129353.

Metagenome-Assembled Genomes Contribute to Unraveling of the Microbiome of Cocoa Fermentation

Metagenomic studies about cocoa fermentation have mainly reported on the analysis of short reads for determination of operational taxonomic units. However, it is also important to determine metagenome-assembled genomes (MAGs), which are genomes deriving from the assembly of metagenomics. For this research, all the cocoa metagenomes from public databases were downloaded, resulting in five data sets: one from Ghana and four from Brazil. In addition, in silico approaches were used to describe putative phenotypes and the metabolic potential of MAGs. A total of 17 high-quality MAGs were recovered from these microbiomes, as follows: (i) for fungi, Yamadazyma tenuis (n = 1); (ii) lactic acid bacteria, Limosilactobacillus fermentum (n = 5), Liquorilactobacillus cacaonum (n = 1), Liquorilactobacillus nagelli (n = 1), Leuconostoc pseudomesenteroides (n = 1), and Lactiplantibacillus plantarum subsp. plantarum (n = 1); (iii) acetic acid bacteria, Acetobacter senegalensis (n = 2) and Kozakia baliensis (n = 1); and (iv) Bacillus subtilis (n = 1), Brevundimonas sp. (n = 2), and Pseudomonas sp. (n = 1). Medium-quality MAGs were also recovered from cocoa microbiomes, including some that, to our knowledge, were not previously detected in this environment (Liquorilactobacillus vini, Komagataeibacter saccharivorans, and Komagataeibacter maltaceti) and others previously described (Fructobacillus pseudoficulneus and Acetobacter pasteurianus). Taken together, the MAGs were useful for providing an additional description of the microbiome of cocoa fermentation, revealing previously overlooked microorganisms, with prediction of key phenotypes and biochemical pathways. IMPORTANCE The production of chocolate starts with the harvesting of cocoa fruits and the spontaneous fermentation of the seeds in a microbial succession that depends on yeasts, lactic acid bacteria, and acetic acid bacteria in order to eliminate bitter and astringent compounds present in the raw material, which will be further roasted and grinded to originate the cocoa powder that will enter the food processing industry. The microbiota of cocoa fermentation is not completely known, and yet it advanced from culture-based studies to the advent of next-generation DNA sequencing, with the generation of a myriad of data that need bioinformatic approaches to be properly analyzed. Although the majority of metagenomic studies have been based on short reads (operational taxonomic units), it is also important to analyze entire genomes to determine more precisely possible ecological roles of different species. Metagenome-assembled genomes (MAGs) are very useful for this purpose; here, MAGs from cocoa fermentation microbiomes are described, and the possible implications of their phenotypic and metabolic potentials are discussed.

Genomic characterization and computational phenotyping of nitrogen-fixing bacteria isolated from Colombian sugarcane fields

Previous studies have shown the sugarcane microbiome harbors diverse plant growth promoting microorganisms, including nitrogen-fixing bacteria (diazotrophs), which can serve as biofertilizers. The genomes of 22 diazotrophs from Colombian sugarcane fields were sequenced to investigate potential biofertilizers. A genome-enabled computational phenotyping approach was developed to prioritize sugarcane associated diazotrophs according to their potential as biofertilizers. This method selects isolates that have potential for nitrogen fixation and other plant growth promoting (PGP) phenotypes while showing low risk for virulence and antibiotic resistance. Intact nitrogenase (nif) genes and operons were found in 18 of the isolates. Isolates also encode phosphate solubilization and siderophore production operons, and other PGP genes. The majority of sugarcane isolates showed uniformly low predicted virulence and antibiotic resistance compared to clinical isolates. Six strains with the highest overall genotype scores were experimentally evaluated for nitrogen fixation, phosphate solubilization, and the production of siderophores, gibberellic acid, and indole acetic acid. Results from the biochemical assays were consistent and validated computational phenotype predictions. A genotypic and phenotypic threshold was observed that separated strains by their potential for PGP versus predicted pathogenicity. Our results indicate that computational phenotyping is a promising tool for the assessment of bacteria detected in agricultural ecosystems.

DeepNOG: fast and accurate protein orthologous group assignment

MOTIVATION

Protein orthologous group databases are powerful tools for evolutionary analysis, functional annotation or metabolic pathway modeling across lineages. Sequences are typically assigned to orthologous groups with alignment-based methods, such as profile hidden Markov models, which have become a computational bottleneck.

RESULTS

We present DeepNOG, an extremely fast and accurate, alignment-free orthology assignment method based on deep convolutional networks. We compare DeepNOG against state-of-the-art alignment-based (HMMER, DIAMOND) and alignment-free methods (DeepFam) on two orthology databases (COG, eggNOG 5). DeepNOG can be scaled to large orthology databases like eggNOG, for which it outperforms DeepFam in terms of precision and recall by large margins. While alignment-based methods still provide the most accurate assignments among the investigated methods, computing time of DeepNOG is an order of magnitude lower on CPUs. Optional GPU usage further increases throughput massively. A command-line tool enables rapid adoption by users.

AVAILABILITYAND IMPLEMENTATION

Source code and packages are freely available at https://github.com/univieCUBE/deepnog. Install the platform-independent Python program with $pip install deepnog.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reclassification of Catabacter hongkongensis as Christensenella hongkongensis comb. nov. based on whole genome analysis

The genera Catabacter (family 'Catabacteraceae') and Christensenella (family Christensenellaceae) are close relatives within the phylum Firmicutes. Members of these genera are strictly anaerobic, non-spore-forming and short straight rods with diverse phenotypes. Phylogenetic analysis of 16S rRNA genes suggest that Catabacter splits Christensenella into a polyphyletic clade. In an effort to ensure that family/genus names represent monophyletic clades, we performed a whole-genome based analysis of the genomes available for the cultured representatives of these genera: four species of Christensenella and two strains of Catabacter hongkongensis. A concatenated alignment of 135 shared protein sequences of single-copy core genes present in the included strains indicates that C. hongkongensis is indeed nested within the Christensenella clade. Based on their evolutionary relationship, we propose the transfer of Catabacter hongkongensis to the genus Christensenella as Christensenella hongkongensis comb. nov.

Machine learning predicts and provides insights into milk acidification rates of Lactococcus lactis

Lactococcus lactis strains are important components in industrial starter cultures for cheese manufacturing. They have many strain-dependent properties, which affect the final product. Here, we explored the use of machine learning to create systematic, high-throughput screening methods for these properties. Fast acidification of milk is such a strain-dependent property. To predict the maximum hourly acidification rate (Vmax), we trained Random Forest (RF) models on four different genomic representations: Presence/absence of gene families, counts of Pfam domains, the 8 nucleotide long subsequences of their DNA (8-mers), and the 9 nucleotide long subsequences of their DNA (9-mers). Vmax was measured at different temperatures, volumes, and in the presence or absence of yeast extract. These conditions were added as features in each RF model. The four models were trained on 257 strains, and the correlation between the measured Vmax and the predicted Vmax was evaluated with Pearson Correlation Coefficients (PC) on a separate dataset of 85 strains. The models all had high PC scores: 0.83 (gene presence/absence model), 0.84 (Pfam domain model), 0.76 (8-mer model), and 0.85 (9-mer model). The models all based their predictions on relevant genetic features and showed consensus on systems for lactose metabolism, degradation of casein, and pH stress response. Each model also predicted a set of features not found by the other models.

Foretelling the Phenotype of a Genomic Sequence

Estimating phenotypic features (physical and biochemical traits) in a biological organism from their genomic sequence alone and/or environmental conditions has major applications in anthropological paleontology and criminal forensics, for example. To what extent do genomic sequences generally and causally determine phenotypic features of organisms, environmental conditions aside? We present results of two studies, one in blackfly (Insecta:Diptera:Simuliidae) larvae in two species (Simulium ignescens and S. tunja) with four phenotypic features, including the area and spot pattern of the cephalic apotome (in the form of a latin cross on the dorsal side of the head), the postgenal cleft (area under the head on the ventral side) and general body color in larva specimens; the second in strains of Arabidopsis thaliana. They establish that a substantial component of these phenotypic features (over 75 percent) are at least logically inferable, if not causally determined, by genomic fragments alone, despite the fact that these phenotypic features are not 100 percent determined entirely by genetic traits. These results suggest that it is possible to infer the genetic contribution in the determination of specific phenotypic features of a biological organism, without recourse to the causal chain of metabolomics and proteomic events leading to them from genomic sequences.

Deciphering a Marine Bone-Degrading Microbiome Reveals a Complex Community Effort

The marine bone biome is a complex assemblage of macro- and microorganisms; however, the enzymatic repertoire to access bone-derived nutrients remains unknown. The bone matrix is a composite material made up mainly of organic collagen and inorganic hydroxyapatite. We conducted field experiments to study microbial assemblages that can use organic bone components as nutrient source. Bovine and turkey bones were deposited at 69 m depth in a Norwegian fjord (Byfjorden, Bergen). Metagenomic sequence analysis was used to assess the functional potential of microbial assemblages from bone surface and the bone-eating worm Osedax mucofloris, which is a frequent colonizer of whale falls and known to degrade bone. The bone microbiome displayed a surprising taxonomic diversity revealed by the examination of 59 high-quality metagenome-assembled genomes from at least 23 bacterial families. Over 700 genes encoding enzymes from 12 relevant enzymatic families pertaining to collagenases, peptidases, and glycosidases putatively involved in bone degradation were identified. Metagenome-assembled genomes (MAGs) of the class Bacteroidia contained the most diverse gene repertoires. We postulate that demineralization of inorganic bone components is achieved by a timely succession of a closed sulfur biogeochemical cycle between sulfur-oxidizing and sulfur-reducing bacteria, causing a drop in pH and subsequent enzymatic processing of organic components in the bone surface communities. An unusually large and novel collagen utilization gene cluster was retrieved from one genome belonging to the gammaproteobacterial genus Colwellia IMPORTANCE Bones are an underexploited, yet potentially profitable feedstock for biotechnological advances and value chains, due to the sheer amounts of residues produced by the modern meat and poultry processing industry. In this metagenomic study, we decipher the microbial pathways and enzymes that we postulate to be involved in bone degradation in the marine environment. We here demonstrate the interplay between different bacterial community members, each supplying different enzymatic functions with the potential to cover an array of reactions relating to the degradation of bone matrix components. We identify and describe a novel gene cluster for collagen utilization, which is a key function in this unique environment. We propose that the interplay between the different microbial taxa is necessary to achieve the complex task of bone degradation in the marine environment.

Myceligenerans indicum sp. nov., an actinobacterium isolated from mangrove sediment of Sundarbans, India

A new actinobacterial species of the genus Myceligenerans has been isolated from the intertidal sediment of Indian Sundarbans mangrove ecosystem. The isolate has been characterized based on polyphasic approaches. The isolate exhibit well-developed substrate mycelia along with the presence of cocci- and rod-shaped elements. The organism can grow across a wide range of temperature, salinity, and pH as well as on different carbon sources. Phylogenetic analyses based on 16S rRNA showed that this isolate is closely related to Myceligenerans salitolerans XHU 5031 (99% identity; 100% coverage). Presence of ketosynthase domain representing polyketide synthases in the isolate provides evidence of its potential ability to produce secondary metabolites. Multigene phylogeny based on atpD and rpoB gene sequences confirmed it as a new species within the family Promicromonosporaceae (Phylum Actinobacteria). The DNA G + C content of the isolate has been determined as 72 mol%. The peptidoglycan type was A4α and the whole-cell hydrolysates contained glucose, galactose, and mannose. The polar lipids were represented by diphosphatidylglycerol, one unknown phospholipid and one unknown glycolipid. Major fatty acids present in the isolate are anteiso-C₁₅, iso-C₁₅, iso-C₁₆, and anteiso-C_17. Whole-genome sequence indicates the size of genome is ~ 5 Mbp. GGDC (%), orthoANIu (%), and AAI of I2 genome indicated 28.9%, 77.44% and 0.859 identity with the genome of Myceligenerans xiligouense strain DSM 15,700. The isolate I2 has been proposed as a new species, Myceligenerans indicum sp. nov. The genome sequence has been deposited to GenBank/ENA/DDBJ under the accession number JABBYC000000000.

The large genome of Synechococcus moorigangaii CMS01 isolated from a mangrove ecosystem- evidences of motility and adaptive features

The whole genome of Synechococcus moorigangaii CMS01 isolated from Indian Sundarbans mangroves of Bay of Bengal is about 5.5 Mbp in size and contains approximately 0.5 Mbp plasmids. Genome annotation revealed total of 5806 genes out of which 5701 were CDSs. Of these, 5616 coding genes with 5616 protein coding CDSs were found. Along with genes coding for essential metabolic proteins, transport proteins and other cellular apparatus, genome also codes for proteins involved in flagella and pilus formation which has not been widely reported before in any coastal species of Synechococcus. The genome contains one incomplete prophage sequence. The genome analysis revealed adaptive features of S. moorigangaii CMS01 and establishes its ubiquitous distribution in coastal water of Bay of Bengal.