Profiling microbial strains in urban environments using metagenomic sequencing data

The indoors microbiome and human health

Indoor environments serve as habitat for humans and are replete with various reservoirs and niches for microorganisms. Microorganisms enter indoor spaces with their human and non-human hosts, as well as via exchange with outdoor sources, such as ventilation and plumbing. Once inside, many microorganisms do not survive, especially on dry, barren surfaces. Even reduced, this microbial biomass has critical implications for the health of human occupants. As urbanization escalates, exploring the intersection of the indoor environment with the human microbiome and health is increasingly vital. The indoor microbiome, a complex ecosystem of microorganisms influenced by human activities and environmental factors, plays a pivotal role in modulating infectious diseases and fostering healthy immune development. Recent advancements in microbiome research shed light on this unique ecological system, highlighting the need for innovative approaches in creating health-promoting living spaces. In this Review, we explore the microbial ecology of built environments - places where humans spend most of their lives - and its implications for immune, endocrine and neurological health. We further propose strategies to harness the indoor microbiome for better health outcomes.

The gut microbiota in persistent post-operative pain following breast cancer surgery

Persistent post-surgical pain (PPSP) is defined as pain which continues after a surgical operation in a significant form for at least three months (and is not related to pre-existing painful conditions). PPSP is a common, under-recognised, and important clinical problem which affects millions of patients worldwide. Preventative measures which are currently available include the selection of a minimally invasive surgical technique and an aggressive multimodal perioperative analgesic regimen. More recently, a role for the gut microbiota in pain modulation has become increasingly apparent. This study aims to investigate any relationship between the gut microbiota and PPSP. A prospective observational study of 68 female adult patients undergoing surgery for management of breast cancer was carried out. Stool samples from 45 of these patients were obtained to analyse the composition of the gut microbiota. Measures of pain and state-trait anxiety were also taken to investigate further dimensions in any relationship between the gut microbiota and PPSP. At 12 weeks postoperatively, 21 patients (51.2%) did not have any pain and 20 patients (48.8%) reported feeling pain that persisted at that time. Analysis of the gut microbiota revealed significantly lower alpha diversity (using three measures) in those patients reporting severe pain at the 60 min post-operative and the 12 weeks post-operative timepoints. A cluster of taxa represented by Bifidobacterium longum, and Faecalibacterium prausnitzii was closely associated with those individuals reporting no pain at 12 weeks postoperatively, while Megamonas hypermegale, Bacteroides pectinophilus, Ruminococcus bromii, and Roseburia hominis clustered relatively closely in the group of patients fulfilling the criteria for persistent post-operative pain. We report for the first time specific associations between the gut microbiota composition and the presence or absence of PPSP. This may provide further insights into mechanisms behind the role of the gut microbiota in the development of PPSP and could inform future treatment strategies.

An overview of the bacterial microbiome of public transportation systems-risks, detection, and countermeasures

When we humans travel, our microorganisms come along. These can be harmless but also pathogenic, and are spread by touching surfaces or breathing aerosols in the passenger cabins. As the pandemic with SARS-CoV-2 has shown, those environments display a risk for infection transmission. For a risk reduction, countermeasures such as wearing face masks and distancing were applied in many places, yet had a significant social impact. Nevertheless, the next pandemic will come and additional countermeasures that contribute to the risk reduction are needed to keep commuters safe and reduce the spread of microorganisms and pathogens, but also have as little impact as possible on the daily lives of commuters. This review describes the bacterial microbiome of subways around the world, which is mainly characterized by human-associated genera. We emphasize on healthcare-associated ESKAPE pathogens within public transport, introduce state-of-the art methods to detect common microbes and potential pathogens such as LAMP and next-generation sequencing. Further, we describe and discuss possible countermeasures that could be deployed in public transportation systems, as antimicrobial surfaces or air sterilization using plasma. Commuting in public transport can harbor risks of infection. Improving the safety of travelers can be achieved by effective detection methods, microbial reduction systems, but importantly by hand hygiene and common-sense hygiene guidelines.

Strategies for mitigation of pesticides from the environment through alternative approaches: A review of recent developments and future prospects

Chemical-based peticides are having negative impacts on both the healths of human beings and plants as well. The World Health Organisation (WHO), reported that each year, >25 million individuals in poor nations are having acute pesticide poisoning cases along with 20,000 fatal injuries at global level. Normally, only ∼0.1% of the pesticide reaches to the intended targets, and rest amount is expected to come into the food chain/environment for a longer period of time. Therefore, it is crucial to reduce the amounts of pesticides present in the soil. Physical or chemical treatments are either expensive or incapable to do so. Hence, pesticide detoxification can be achieved through bioremediation/biotechnologies, including nano-based methodologies, integrated approaches etc. These are relatively affordable, efficient and environmentally sound methods. Therefore, alternate strategies like as advanced biotechnological tools like as CRISPR Cas system, RNAi and genetic engineering for development of insects and pest resistant plants which are directly involved in the development of disease- and pest-resistant plants and indirectly reduce the use of pesticides. Omics tools and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation of pesticides also discussed from the literatures. Overall, the review focuses on the most recent advancements in bioremediation methods to lessen the effects of pesticides along with the role of microorganisms in pesticides elimination. Further, pesticide detection is also a big challenge which can be done by using HPLC, GC, SERS, and LSPR ELISA etc. which have also been described in this review.

Metabarcoding approach to identify bacterial community profiling related to nosocomial infection and bacterial trafficking-routes in hospital environments

Nosocomial infections (NIs) appear in patients under medical care in the hospital. The surveillance of the bacterial communities employing high-resolution 16S rRNA profiling, known as metabarcoding, represents a reliable method to establish factors that may influence the composition of the bacterial population during NIs. The present study aimed to utilize high-resolution 16S rRNA profiling to identify high bacterial diversity by analyzing 11 inside and 10 outside environments from the General Hospital of Ribeirão Preto Medical School, Brazil. Our results identified a high bacterial diversity, and among these, the most abundant bacterial genera linked to NIs were Cutibacterium, Streptococcus, Staphylococcus, and Corynebacterium. A Acinetobacter was detected in cafeterias, bus stops, and adult and pediatric intensive care units (ICUs). Data suggest an association between transport and alimentation areas proximal to the hospital ICU environment. Interestingly, the correlation and clusterization analysis showed the potential of the external areas to directly influence the ICU pediatric department microbial community, including the outpatient's clinic, visitor halls, patient reception, and the closest cafeterias. Our results demonstrate that high-resolution 16S rRNA profiling is a robust and reliable tool for bacterial genomic surveillance. In addition, the metabarcoding approach might help elaborate decontamination policies, and consequently reduce NIs.

The Future Is Now: Unraveling the Expanding Potential of Human (Necro)Microbiome in Forensic Investigations

The relevance of postmortem microbiological examinations has been controversial for decades, but the boom in advanced sequencing techniques over the last decade is increasingly demonstrating their usefulness, namely for the estimation of the postmortem interval. This comprehensive review aims to present the current knowledge about the human postmortem microbiome (the necrobiome), highlighting the main factors influencing this complex process and discussing the principal applications in the field of forensic sciences. Several limitations still hindering the implementation of forensic microbiology, such as small-scale studies, the lack of a universal/harmonized workflow for DNA extraction and sequencing technology, variability in the human microbiome, and limited access to human cadavers, are discussed. Future research in the field should focus on identifying stable biomarkers within the dominant Bacillota and Pseudomonadota phyla, which are prevalent during postmortem periods and for which standardization, method consolidation, and establishment of a forensic microbial bank are crucial for consistency and comparability. Given the complexity of identifying unique postmortem microbial signatures for robust databases, a promising future approach may involve deepening our understanding of specific bacterial species/strains that can serve as reliable postmortem interval indicators during the process of body decomposition. Microorganisms might have the potential to complement routine forensic tests in judicial processes, requiring robust investigations and machine-learning models to bridge knowledge gaps and adhere to Locard's principle of trace evidence.

Understanding the bacterial compositional network associations between oral and gut microbiome within healthy Koreans

Oral microbial ecosystem could influence intestinal diseases, but there have been insufficient studies demonstrating the association of microbial composition between the oral cavity and the intestinal system. Thus, we aimed to investigate the compositional network within the oral microbiome related to gut enterotype from saliva and stool samples collected from 112 healthy Korean subjects. Here, we performed bacterial 16S amplicon sequencing from clinical samples. Then, we determined oral microbiome type related to individual's gut enterotype for healthy Korean. The co-occurrence analysis was performed to interactivity prediction of microbiome within saliva samples. As a result, it could be classified into two Korean oral microbiome types (KO) and four oral-gut-associated microbiome types (KOGA) according to distribution and significant differences of oral microflora. The co-occurrence analysis showed various bacterial compositional networks linked around Streptococcus and Haemophilus within healthy subjects. The present study was first approach in healthy Koreans to identify the oral microbiome types related to the gut microbiome and investigate their characteristics. Hence, we suggest that our results could be potential healthy control data for identifying differences in microbial composition between healthy people and oral disease patients and studying microbial association with the gut microbial environment (oral-gut microbiome axis).

Metagenomic Approaches Reveal Strain Profiling and Genotyping of Klebsiella pneumoniae from Hospitalized Patients in China

Klebsiella pneumoniae is a leading cause of highly drug-resistant infections in hospitals worldwide. Strain-level bacterial identification on the genetic determinants of multidrug resistance and high pathogenicity is critical for the surveillance and treatment of this clinically relevant pathogen. In this study, metagenomic next-generation sequencing was performed for specimens collected from August 2020 to May 2021 in Ruijin Hospital, Ningbo Women and Children’s Hospital, and the Second Affiliated Hospital of Harbin Medical University. Genome biology of K. pneumoniae prevalent in China was characterized based on metagenomic data. Thirty K. pneumoniae strains derived from 14 sequence types were identified by multilocus sequence typing. The hypervirulent ST11 K. pneumoniae strains carrying the KL64 capsular locus were the most prevalent in the hospital population. The phylogenomic analyses revealed that the metagenome-reconstructed strains and public isolate genomes belonging to the same STs were closely related in the phylogenetic tree. Furthermore, the pangenome structure of the detected K. pneumoniae strains was analyzed, particularly focusing on the distribution of antimicrobial resistance genes and virulence genes across the strains. The genes encoding carbapenemases and extended-spectrum beta-lactamases were frequently detected in the strains of ST11 and ST15. The highest numbers of virulence genes were identified in the well-known hypervirulent strains affiliated to ST23 bearing the K1 capsule. In comparison to traditional cultivation and identification, strain-level metagenomics is advantageous to understand the mechanisms underlying resistance and virulence of K. pneumoniae directly from clinical specimens. Our findings should provide novel clues for future research into culture-independent metagenomic surveillance for bacterial pathogens.

IMPORTANCE Routine culture and PCR-based molecular testing in the clinical microbiology laboratory are unable to recognize pathogens at the strain level and to detect strain-specific genetic determinants involved in virulence and resistance. To address this issue, we explored the strain-level profiling of K. pneumoniae prevalent in China based on metagenome-sequenced patient materials. Genome biology of the targeted bacterium can be well characterized through decoding sequence signatures and functional gene profiles at the single-strain resolution. The in-depth metagenomic analysis on strain profiling presented here shall provide a promising perspective for culture-free pathogen surveillance and molecular epidemiology of nosocomial infections.

Where environmental microbiome meets its host: subway and passenger microbiome relationships

Subways are urban transport systems with high capacity. Every day around the world, there are more than 150 million subway passengers. Since 2013, thousands of microbiome samples from various subways worldwide have been sequenced. Skin bacteria and environmental organisms dominate the subway microbiomes. The literature has revealed common bacterial groups in subway systems; even so, it is possible to identify cities by their microbiome. Low-frequency bacteria are responsible for specific bacterial fingerprints of each subway system. Furthermore, daily subway commuters leave their microbial clouds and interact with other passengers. Microbial exchange is quite fast; the hand microbiome changes within minutes, and after cleaning the handrails, the bacteria are re-established within minutes. To investigate new taxa and metabolic pathways of subway microbial communities, several high-quality metagenomic-assembled genomes (MAG) have been described. Subways are harsh environments unfavorable for microorganism growth. However, recent studies have observed a wide diversity of viable and metabolically active bacteria. Understanding which bacteria are living, dormant, or dead allows us to propose realistic ecological interactions. Questions regarding the relationship between humans and the subway microbiome, particularly the microbiome effects on personal and public health, remain unanswered. This review summarizes our knowledge of subway microbiomes and their relationship with passenger microbiomes.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Evidence for the Widespread Occurrence of Bacteria Implicated in Acute Oak Decline from Incidental Genetic Sampling

Acute Oak Decline (AOD) is complex syndrome affecting Britain’s keystone native oak species, (Quercus robur L. and Q. petraea L. (Matt.) Liebl.), in some cases causing mortality within five years of symptom development. The most distinguishable symptom is weeping stem lesions, from which four species of bacteria have been isolated: Brenneria goodwinii, Gibbsiella quercinecans, Lonsdalea britannica and Rahnella victoriana. We do not yet know where else these bacteria exist, and little is known about the relationship of the wider oak leaf microbiome (phyllosphere) to acute oak decline. Here we investigate whether incidental evidence from a large oak genome re-sequencing dataset could be used to detect these bacteria in oak foliage, and whether bacterial incidence co-varied with AOD status or location. Oak leaves and buds were sampled from 421 trees at five sites in England. Whole genomic DNA from these samples was shot-gun sequenced with short reads. Non-oak reads were extracted from these data and queried to microbial databases. Reads uniquely matching AOD-associated bacterial genomes were found to be present on trees from all five sites and included trees with active lesions, trees with historic lesions and trees without AOD symptoms. The abundance of the AOD-associated bacteria did not differ between tree health categories but did differ among sites. We conclude that the AOD-associated bacteria may be members of the normal oak microbiome, whose presence on a tree is not sufficient to cause AOD symptoms.

Specific metagenomic asset drives the spontaneous fermentation of Italian sausages

Metagenomics is a powerful tool to study and understand the microbial dynamics that occur during food fermentation and allows to close the link between microbial diversity and final sensory characteristics. Each food matrix can be colonized by different microbes, but also by different strains of the same species. In this study, using an innovative integrated approach combining culture-dependent method with a shotgun sequencing, we were able to show how strain-level biodiversity could influence the quality characteristics of the final product. The attention was placed on a model food fermentation process: Salame Piemonte, a Protected Geographical Indication (PGI) Italian fermented sausage. Three independent batches produced in February, March and May 2018 were analysed. The sausages were manufactured, following the production specification, in a local meat factory in the area of Turin (Italy) without the use of starter cultures. A pangenomic approach was applied in order to identify and evaluate the lactic acid bacteria (LAB) population driving the fermentation process. It was observed that all batches were characterized by the presence of few LAB species, namely Pediococcus pentosaceus, Latilactobacillus curvatus and Latilactobacillus sakei. Sausages from the different batches were different when the volatilome was taken into consideration, and a strong association between quality attributes and strains present was determined. In particular, different strains of L. sakei, showing heterogeneity at genomic level, colonized the meat at the beginning of each production and deeply influenced the fermentation process by distinctive metabolic pathways that affected the fermentation process and the final sensory aspects.

Diversity and composition of gut microbiome of cervical cancer patients: Do results of 16S rRNA sequencing and whole genome sequencing approaches align?

BACKGROUND

Next generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples.

RESULTS

Comparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all P-value>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables.

CONCLUSION

Diversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S.

Microbiome in Death and Beyond: Current Vistas and Future Trends

Forensic medicine has, for a long time, been relying on biochemical, anthropologic, and histopathologic evidences in solving various investigations. However, depending on the method used, lengthy sample processing time, scanty sample, and less sensitivity and accuracy pervade these procedures. Accordingly, newer arenas such as the thanatomicrobiome have come forward to aid in its quandaries; furthermore, the parallel advances in genomic and proteomic techniques have complemented and are still emerging to be used in forensic experiments and investigations. Postmortem interval (PMI) is one of the most important aspects of medico-legal investigations. The current trend in PMI estimation is toward genomic analyses of autopsy samples. Similarly, determination of cause of death, although a domain of medical sciences, is being targeted as the next level of forensic casework. With the current trend in laboratory sciences moving to the discovery of newer disease-specific markers for diagnostic and prognostic purposes, the same is being explored for the determination of the cause of death by using techniques such as Real-Time PCR, DNA micro-array, to Next-Gen Sequencing. Establishing an individual’s biological profile has been done using medicolegal methods and anthropology as well as bar-bodies/Davidson bodies (gender determination); and in cases where the determination of age/gender is a challenge using morphological characteristics; the recent advances in the field of genomics and proteomics have played a significant role, e.g., use of mitochondrial DNA in age estimation and in maternity disputes. The major hurdle forensic medical research faces is the fact that most of the studies are conducted in animal models, which are often difficult to mimic in human and real-time scenarios. Additionally, the high accuracy required in criminal investigations to be used in a court of law as evidence has prevented these results to come out of the labs and be used to the optimum. The current review aims at giving a comprehensive and critical account of the various molecular biology techniques including “thanatogenomics,” currently being utilized in the veritable fields of forensic medicine.

Preparing for Emerging Zoonotic Viruses

The risk of emergence and spread of novel human pathogens originating from an animal reservoir has increased in the past decades. However, the unpredictable nature of disease emergence makes surveillance and preparedness challenging. Knowledge of general risk factors for emergence and spread, combined with local level data is needed to develop a risk-based methodology for early detection. This involves the implementation of the One Health approach, integrating human, animal and environmental health sectors, as well as social sciences, bioinformatics and more. Recent technical advances, such as metagenomic sequencing, will aid the rapid detection of novel pathogens on the human-animal interface.

Liquid biopsies and cancer omics

The development of the sequencing technologies allowed the generation of huge amounts of molecular data from a single cancer specimen, allowing the clinical oncology to enter the era of the precision medicine. This massive amount of data is highlighting new details on cancer pathogenesis but still relies on tissue biopsies, which are unable to capture the dynamic nature of cancer through its evolution. This assumption led to the exploration of non-tissue sources of tumoral material opening the field of liquid biopsies. Blood, together with body fluids such as urines, or stool, from cancer patients, are analyzed applying the techniques used for the generation of omics data. With blood, this approach would allow to take into account tumor heterogeneity (since the circulating components such as CTCs, ctDNA, or ECVs derive from each cancer clone) in a time dependent manner, resulting in a somehow "real-time" understanding of cancer evolution. Liquid biopsies are beginning nowdays to be applied in many cancer contexts and are at the basis of many clinical trials in oncology.

A machine learning framework to determine geolocations from metagenomic profiling

BACKGROUND

Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples' geolocations. In this paper, we present a machine learning framework to determine the geolocations from metagenomics profiling of microbial samples.

RESULTS

Our method was applied to the multi-source microbiome data from MetaSUB (The Metagenomics and Metadesign of Subways and Urban Biomes) International Consortium for the CAMDA 2019 Metagenomic Forensics Challenge (the Challenge). The goal of the Challenge is to predict the geographical origins of mystery samples by constructing microbiome fingerprints.First, we extracted features from metagenomic abundance profiles. We then randomly split the training data into training and validation sets and trained the prediction models on the training set. Prediction performance was evaluated on the validation set. By using logistic regression with L2 normalization, the prediction accuracy of the model reaches 86%, averaged over 100 random splits of training and validation datasets.The testing data consists of samples from cities that do not occur in the training data. To predict the "mystery" cities that are not sampled before for the testing data, we first defined biological coordinates for sampled cities based on the similarity of microbial samples from them. Then we performed affine transform on the map such that the distance between cities measures their biological difference rather than geographical distance. After that, we derived the probabilities of a given testing sample from unsampled cities based on its predicted probabilities on sampled cities using Kriging interpolation. Results show that this method can successfully assign high probabilities to the true cities-of-origin of testing samples.

CONCLUSION

Our framework shows good performance in predicting the geographic origin of metagenomic samples for cities where training data are available. Furthermore, we demonstrate the potential of the proposed method to predict metagenomic samples' geolocations for samples from locations that are not in the training dataset.

Commensal microbes and p53 in cancer progression

Aetiogenesis of cancer has not been fully determined. Recent advances have clearly defined a role for microenvironmental factors in cancer progression and initiation; in this context, microbiome has recently emerged with a number of reported correlative and causative links implicating alterations of commensal microbes in tumorigenesis. Bacteria appear to have the potential to directly alter physiological pathways of host cells and in specific circumstances, such as the mutation of the tumour suppressive factor p53, they can also directly switch the function of a gene from oncosuppressive to oncogenic. In this minireview, we report a number of examples on how commensal microbes alter the host cell biology, affecting the oncogenic process. We then discuss more in detail how interaction with the gut microbiome can affect the function of p53 mutant in the intestinal tumorigenesis.

Cancer predictive studies

The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients.

Thanatomicrobiome and epinecrotic community signatures for estimation of post-mortem time interval in human cadaver

Estimation of post-mortem time interval (PMI) is a key parameter in the forensic investigation which poses a huge challenge to the medico-legal experts. The succession of microbes within different parts of the human body after death has shown huge potential in the determination of PMI. Human body harbors trillions of microorganisms as commensals. With the death of an individual when biological functions are stopped, these microorganisms behave contrarily along with the invasion of degrading microbes from the environment. Human cadaver becomes a rich source of nutrients due to autolysis of cells, which attracts various invading microorganisms as well as macroorganisms. At different stages of degradation, the succession of microorganisms differs significantly which can be explored for accurate PMI estimation. With the advent of microbial genomics technique and reduction in the cost of DNA sequencing, thanatomicrobiome and epinecrotic community analysis have gained huge attention in PMI estimation. The article summarizes different sources of microorganisms in a human cadaver, their succession pattern, and analytical techniques for application in the field of microbial forensics. KEY POINTS: • Thanatomicrobiome and epinecrotic microbiome develop in postmortem human body. • Lack of metabolic, immune, neuroendocrine systems facilitate microbial succession. • Analysis of postmortem microbial communities predicts accurate PMI.

Untargeted Metagenomic Investigation of the Airway Microbiome of Cystic Fibrosis Patients with Moderate-Severe Lung Disease

Although the cystic fibrosis (CF) lung microbiota has been characterized in several studies, little is still known about the temporal changes occurring at the whole microbiome level using untargeted metagenomic analysis. The aim of this study was to investigate the taxonomic and functional temporal dynamics of the lower airway microbiome in a cohort of CF patients. Multiple sputum samples were collected over 15 months from 22 patients with advanced lung disease regularly attending three Italian CF Centers, given a total of 79 samples. DNA extracted from samples was subjected to shotgun metagenomic sequencing allowing both strain-level taxonomic profiling and assessment of the functional metagenomic repertoire. High inter-patient taxonomic heterogeneity was found with short-term compositional changes across clinical status. Each patient exhibited distinct sputum microbial communities at the taxonomic level, and strain-specific colonization of both traditional and atypical CF pathogens. A large core set of genes, including antibiotic resistance genes, were shared across patients despite observed differences in clinical status, and consistently detected in the lung microbiome of all subjects independently from known antibiotic exposure. In conclusion, an overall stability in the microbiome-associated genes was found despite taxonomic fluctuations of the communities.

Genome-resolved metagenomics to study co-occurrence patterns and intraspecific heterogeneity among plant pathogen metapopulations

Assessment of pathogen diversity in agricultural fields is essential for informing management decisions and the development of resistant plant varieties. However, many population genomic studies have relied on culture-based approaches that do not provide quantitative assessment of pathogen populations at the field-level or the associated host microbiome. Here, we applied whole-genome shotgun sequencing of microbial DNA extracted directly from the washings of pooled leaf samples, collected from individual tomato and pepper fields in Alabama that displayed the classical symptoms of bacterial spot disease caused by Xanthomonas spp. Our results revealed that while the occurrence of both X. perforans and X. euvesicatoria within fields was limited, evidence of co-occurrence of up to three distinct X. perforans genotypes was obtained in 7 of 10 tomato fields sampled. These population dynamics were accompanied by the corresponding type 3 secreted effector repertoires associated with the co-occurring X. perforans genotypes, indicating that metapopulation structure within fields should be considered when assessing the adaptive potential of X. perforans. Finally, analysis of microbial community composition revealed that co-occurrence of the bacterial spot pathogens Pseudomonas cichorii and Xanthomonas spp. is common in Alabama fields and provided evidence for the non-random association of several other human and plant opportunists.

Environmental metagenome classification for constructing a microbiome fingerprint

BACKGROUND

Nowadays, not only are single genomes commonly analyzed, but also metagenomes, which are sets of, DNA fragments (reads) derived from microbes living in a given environment. Metagenome analysis is aimed at extracting crucial information on the organisms that have left their traces in an investigated environmental sample.In this study we focus on the MetaSUB Forensics Challenge (organized within the CAMDA 2018 conference) which consists in predicting the geographical origin of metagenomic samples. Contrary to the existing methods for environmental classification that are based on taxonomic or functional classification, we rely on the similarity between a sample and the reference database computed at a reads level.

RESULTS

We report the results of our extensive experimental study to investigate the behavior of our method and its sensitivity to different parameters. In our tests, we have followed the protocol of the MetaSUB Challenge, which allowed us to compare the obtained results with the solutions based on taxonomic and functional classification.

CONCLUSIONS

The results reported in the paper indicate that our method is competitive with those based on taxonomic classification. Importantly, by measuring the similarity at the reads level, we avoid the necessity of using large databases with annotated gene sequences. Hence our main finding is that environmental classification of metagenomic data can be proceeded without using large databases required for taxonomic or functional classification.

REVIEWERS

This article was reviewed by Eran Elhaik, Alexandra Bettina Graf, Chengsheng Zhu, and Andre Kahles.

Fingerprinting cities: differentiating subway microbiome functionality

BACKGROUND

Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now.

RESULTS

As a part of the 2018 CAMDA challenge, we functionally profiled the available ~ 400 subway metagenomes and built predictor for city origin. In cross-validation, our model reached 81% accuracy when only the top-ranked city assignment was considered and 95% accuracy if the second city was taken into account as well. Notably, this performance was only achievable if the similarity of distribution of cities in the training and testing sets was similar. To assure that our methods are applicable without such biased assumptions we balanced our training data to account for all represented cities equally well. After balancing, the performance of our method was slightly lower (76/94%, respectively, for one or two top ranked cities), but still consistently high. Here we attained an added benefit of independence of training set city representation. In testing, our unbalanced model thus reached (an over-estimated) performance of 90/97%, while our balanced model was at a more reliable 63/90% accuracy. While, by definition of our model, we were not able to predict the microbiome origins previously unseen, our balanced model correctly judged them to be NOT-from-training-cities over 80% of the time. Our function-based outlook on microbiomes also allowed us to note similarities between both regionally close and far-away cities. Curiously, we identified the depletion in mycobacterial functions as a signature of cities in New Zealand, while photosynthesis related functions fingerprinted New York, Porto and Tokyo.

CONCLUSIONS

We demonstrated the power of our high-speed function annotation method, mi-faser, by analysing ~ 400 shotgun metagenomes in 2 days, with the results recapitulating functional signals of different city subway microbiomes. We also showed the importance of balanced data in avoiding over-estimated performance. Our results revealed similarities between both geographically close (Ofa and Ilorin) and distant (Boston and Porto, Lisbon and New York) city subway microbiomes. The photosynthesis related functional signatures of NYC were previously unseen in taxonomy studies, highlighting the strength of functional analysis.

Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples

BACKGROUND

The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.

RESULTS

Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.

CONCLUSIONS

We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.

REVIEWERS

Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.

Gene Editing and Systems Biology Tools for Pesticide Bioremediation: A Review

Bioremediation is the degradation potential of microorganisms to dissimilate the complex chemical compounds from the surrounding environment. The genetics and biochemistry of biodegradation processes in datasets opened the way of systems biology. Systemic biology aid the study of interacting parts involved in the system. The significant keys of system biology are biodegradation network, computational biology, and omics approaches. Biodegradation network consists of all the databases and datasets which aid in assisting the degradation and deterioration potential of microorganisms for bioremediation processes. This review deciphers the bio-degradation network, i.e., the databases and datasets (UM-BBD, PAN, PTID, etc.) aiding in assisting the degradation and deterioration potential of microorganisms for bioremediation processes, computational biology and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation experiments. Besides, the present review also describes the gene editing tools like CRISPR Cas, TALEN, and ZFNs which can possibly make design microbe with functional gene of interest for degradation of particular recalcitrant for improved bioremediation.