Hospitalized Premature Infants Are Colonized by Related Bacterial Strains with Distinct Proteomic Profiles

Fructo-oligosaccharide supplementation enhances the growth of nursing dairy calves while stimulating the persistence of Bifidobacterium and hindgut microbiome's maturation

The colonization and development of the gut microbiome in dairy calves play a crucial role in their overall health and future productivity. Despite the widely proposed benefits of inulin-related products on the host, there is insufficient information about how supplementing fructo-oligosaccharides (FOS) affects the colonization and development of the gut microbiome in calves. In a randomized intervention trial involving newborn male Holstein dairy calves, we investigated the effect of FOS on the calf hindgut microbiome, short-chain fatty acids (SCFA), growth performance, and the incidence of diarrhea. The daily administration of FOS exhibited a time-dependent increase in the ADG and the concentration of SCFA. Concurrently, FOS delayed the natural decline of Bifidobacterium, promoting the maturation and stabilization of the hindgut microbiome. These findings not only contribute to a theoretical understanding of the judicious application of prebiotics but also hold significant practical implications for the design of early life dietary interventions in the rearing of dairy calves.

Gut microbiota of preterm infants in the neonatal intensive care unit: a study from a tertiary care center in northern India

Introduction

Disruptions of the gut microbiota of preterm infants admitted to the neonatal intensive care unit (NICU) during the first 2 weeks of life are of critical importance. These infants are prone to various complications, including necrotizing enterocolitis (NEC) and sepsis. Studying the gut microbiota will improve outcomes in preterm infants. In the present study, we examined the gut microbiota of preterm infants admitted to the NICU in the first month of life.

Methods

Neonates admitted to the NICU were recruited, and stool samples were collected weekly from the seventh day of the infant's life until the 30th day of life. DNA was extracted using a DNeasy Powersoil DNA isolation kit. 16S rRNA gene sequencing targeting the V3-V4 region was performed using the MiSeq platform. Sequenced reads were processed on DADA2 pipeline to obtain an amplicon sequence variant (ASV) table. All bioinformatic and statistical analyses were performed using different packages in the R statistical framework.

Results

Fourteen preterm infants were recruited, and 48 samples were collected. Alpha diversity metrics, observed ASV count, and Shannon index were found to have no differences in any clinical variables. Permutational multivariate analysis of variance (PERMANOVA) showed discrimination of neonates by gestational age and administration of probiotics. Differential abundance analysis showed a decreased abundance of Bifidobacterium Breve in extremely preterm infants (gestational age <28 weeks) compared to moderate preterm infants (gestational age 29-32 weeks). Supplementation with probiotics decreased Acinetobacter and increased Bifidobacterium in the gut of preterm neonates regardless of gestational age.

Conclusion

Gestational age and probiotic supplementation alter the gut microbiota of preterm infants admitted to the NICU.

Bifidobacterium: Host-Microbiome Interaction and Mechanism of Action in Preventing Common Gut-Microbiota-Associated Complications in Preterm Infants: A Narrative Review

The development and health of infants are intertwined with the protective and regulatory functions of different microorganisms in the gut known as the gut microbiota. Preterm infants born with an imbalanced gut microbiota are at substantial risk of several diseases including inflammatory intestinal diseases, necrotizing enterocolitis, late-onset sepsis, neurodevelopmental disorders, and allergies which can potentially persist throughout adulthood. In this review, we have evaluated the role of Bifidobacterium as commonly used probiotics in the development of gut microbiota and prevention of common diseases in preterm infants which is not fully understood yet. The application of Bifidobacterium as a therapeutical approach in the re-programming of the gut microbiota in preterm infants, the mechanisms of host-microbiome interaction, and the mechanism of action of this bacterium have also been investigated, aiming to provide new insights and opportunities in microbiome-targeted interventions in personalized medicine.

Statistical Evaluation of Metaproteomics and 16S rRNA Amplicon Sequencing Techniques for Study of Gut Microbiota Establishment in Infants with Cystic Fibrosis

Newborn screening for cystic fibrosis (CF) can identify affected but asymptomatic infants. The selection of omic technique for gut microbiota study is crucial due to both the small amount of feces available and the low microorganism load. Our aims were to compare the agreement between 16S rRNA amplicon sequencing and metaproteomics by a robust statistical analysis, including both presence and abundance of taxa, to describe the sequential establishment of the gut microbiota during the first year of life in a small size sample (8 infants and 28 fecal samples). The taxonomic assignations by the two techniques were similar, whereas certain discrepancies were observed in the abundance detection, mostly the lower predicted relative abundance of Bifidobacterium and the higher predicted relative abundance of certain Firmicutes and Proteobacteria by amplicon sequencing. During the first months of life, the CF gut microbiota is characterized by a significant enrichment of Ruminococcus gnavus, the expression of certain virulent bacterial traits, and the detection of human inflammation-related proteins. Metaproteomics provides information on composition and functionality, as well as data on host-microbiome interactions. Its strength is the identification and quantification of Actinobacteria and certain classes of Firmicutes, but alpha diversity indices are not comparable to those of amplicon sequencing. Both techniques detected an aberrant microbiota in our small cohort of infants with CF during their first year of life, dominated by the enrichment of R. gnavus within a human inflammatory environment. IMPORTANCE In recent years, some techniques have been incorporated for the study of microbial ecosystems, being 16S rRNA gene sequencing being the most widely used. Metaproteomics provides the advantage of identifying the interaction between microorganisms and human cells, but the available databases are less extensive as well as imprecise. Few studies compare the statistical differences between the two techniques to define the composition of an ecosystem. Our work shows that the two methods are comparable in terms of microorganism identification but provide different results in alpha diversity analysis. On the other hand, we have studied newborns with cystic fibrosis, for whom we have described the establishment of an intestinal ecosystem marked by the inflammatory response of the host and the enrichment of Ruminococcus gnavus.

A novel approach for combining the metagenome, metaresistome, metareplicome and causal inference to determine the microbes and their antibiotic resistance gene repertoire that contribute to dysbiosis

The use of whole metagenomic data to infer the relative abundance of all its microbes is well established. The same data can be used to determine the replication rate of all eubacterial taxa with circular chromosomes. Despite their availability, the replication rate profiles (metareplicome) have not been fully exploited in microbiome analyses. Another relatively new approach is the application of causal inferencing to analyse microbiome data that goes beyond correlational studies. A novel scalable pipeline called MeRRCI (Metagenome, metaResistome, and metaReplicome for Causal Inferencing) was developed. MeRRCI combines efficient computation of the metagenome (bacterial relative abundance), metaresistome (antimicrobial gene abundance) and metareplicome (replication rates), and integrates environmental variables (metadata) for causality analysis using Bayesian networks. MeRRCI was applied to an infant gut microbiome data set to investigate the microbial community's response to antibiotics. Our analysis suggests that the current treatment stratagem contributes to preterm infant gut dysbiosis, allowing a proliferation of pathobionts. The study highlights the specific antibacterial resistance genes that may contribute to exponential cell division in the presence of antibiotics for various pathogens, namely Klebsiella pneumoniae, Citrobacter freundii, Staphylococcus epidermidis, Veilonella parvula and Clostridium perfringens. These organisms often contribute to the harmful long-term sequelae seen in these young infants.

Antibiotic resistance and host immune system-induced metal bactericidal control are key factors for microbial persistence in the developing human preterm infant gut microbiome

The human gut microbiome, which develops and stabilizes during the early stages of infant life, plays an essential role in host health through the production of metabolic resources and the stimulation and training of the immune system. To study colonization and community functional dynamics of the microbiota based on responses to host immune processes during the normal and dysbiotic establishment of the gut, metaproteomics was conducted on 91 fecal samples collected over the first 90 days of life from 17 hospitalized premature infants. Microbial responses to antibiotic administration and host-imposed metal bactericidal control correlated with community assembly and resiliency of microbes in the developing preterm gut. Specifically, proteins related to antibiotic resistance and metal homeostasis mechanisms were predominant in persisting members in the infant gut environment over the first several weeks of life. Overall, this metaproteomics study provides a unique approach to examine the temporal expansion and resilience of microbial colonization, as it allows simultaneous examination of both host and microbial metabolic activities. Understanding the interplay between host and microbes may elucidate the microbiome’s potential immunomodulatory roles relevant to necrotizing enterocolitis and other dysbiotic conditions in preterm infants.

Morphine and high-fat diet differentially alter the gut microbiota composition and metabolic function in lean versus obese mice

There are known associations between opioids, obesity, and the gut microbiome, but the molecular connection/mediation of these relationships is not understood. To better clarify the interplay of physiological, genetic, and microbial factors, this study investigated the microbiome and host inflammatory responses to chronic opioid administration in genetically obese, diet-induced obese, and lean mice. Samples of feces, urine, colon tissue, and plasma were analyzed using targeted LC-MS/MS quantification of metabolites, immunoassays of inflammatory cytokine levels, genome-resolved metagenomics, and metaproteomics. Genetic obesity, diet-induced obesity, and morphine treatment in lean mice each showed increases in distinct inflammatory cytokines. Metagenomic assembly and binning uncovered over 400 novel gut bacterial genomes and species. Morphine administration impacted the microbiome's composition and function, with the strongest effect observed in lean mice. This microbiome effect was less pronounced than either diet or genetically driven obesity. Based on inferred microbial physiology from the metaproteome datasets, a high-fat diet transitioned constituent microbes away from harvesting diet-derived nutrients and towards nutrients present in the host mucosal layer. Considered together, these results identified novel host-dependent phenotypes, differentiated the effects of genetic obesity versus diet induced obesity on gut microbiome composition and function, and showed that chronic morphine administration altered the gut microbiome.

Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota

Background

Necrotizing enterocolitis (NEC) is a common, potentially catastrophic intestinal disease among very low birthweight premature infants. Affecting up to 15% of neonates born weighing less than 1500 g, NEC causes sudden-onset, progressive intestinal inflammation and necrosis, which can lead to significant bowel loss, multi-organ injury, or death. No unifying cause of NEC has been identified, nor is there any reliable biomarker that indicates an individual patient’s risk of the disease. Without a way to predict NEC in advance, the current medical strategy involves close clinical monitoring in an effort to treat babies with NEC as quickly as possible before irrecoverable intestinal damage occurs. In this report, we describe a novel machine learning application for generating dynamic, individualized NEC risk scores based on intestinal microbiota data, which can be determined from sequencing bacterial DNA from otherwise discarded infant stool. A central insight that differentiates our work from past efforts was the recognition that disease prediction from stool microbiota represents a specific subtype of machine learning problem known as multiple instance learning (MIL).

Results

We used a neural network-based MIL architecture, which we tested on independent datasets from two cohorts encompassing 3595 stool samples from 261 at-risk infants. Our report also introduces a new concept called the “growing bag” analysis, which applies MIL over time, allowing incorporation of past data into each new risk calculation. This approach allowed early, accurate NEC prediction, with a mean sensitivity of 86% and specificity of 90%. True-positive NEC predictions occurred an average of 8 days before disease onset. We also demonstrate that an attention-gated mechanism incorporated into our MIL algorithm permits interpretation of NEC risk, identifying several bacterial taxa that past work has associated with NEC, and potentially pointing the way toward new hypotheses about NEC pathogenesis. Our system is flexible, accepting microbiota data generated from targeted 16S or “shotgun” whole-genome DNA sequencing. It performs well in the setting of common, potentially confounding preterm neonatal clinical events such as perinatal cardiopulmonary depression, antibiotic administration, feeding disruptions, or transitions between breast feeding and formula.

Conclusions

We have developed and validated a robust MIL-based system for NEC prediction from harmlessly collected premature infant stool. While this system was developed for NEC prediction, our MIL approach may also be applicable to other diseases characterized by changes in the human microbiota.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04618-w.

A systematic review of the factors influencing microbial colonization of the preterm infant gut

Prematurity coupled with the necessary clinical management of preterm (PT) infants introduces multiple factors that can interfere with microbial colonization. This study aimed to review the perinatal, physiological, pharmacological, dietary, and environmental factors associated with gut microbiota of PT infants. A total of 587 articles were retrieved from a search of multiple databases. Sixty studies were included in the review after removing duplicates and articles that did not meet the inclusion criteria. Review of this literature revealed that evidence converged on the effect of postnatal age, mode of delivery, use of antibiotics, and consumption of human milk in the composition of gut microbiota of PT infants. Less evidence was found for associations with race, sex, use of different fortifiers, macronutrients, and other medications. Future studies with rich metadata are needed to further explore the impact of the PT exposome on the development of the microbiota in this high-risk population.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.

Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics

Humans have a long-standing coexistence with microorganisms. In particular, the microbial community that populates the human gastrointestinal tract has emerged as a critical player in governing human health and disease. DNA and RNA sequencing techniques that map taxonomical composition and genomic potential of the gut community have become invaluable for microbiome research. However, deriving a biochemical understanding of how activities of the gut microbiome shape host development and physiology requires an expanded experimental design that goes beyond these approaches. In this review, we explore advances in high-throughput techniques based on liquid chromatography-mass spectrometry. These omics methods for the identification of proteins and metabolites have enabled direct characterisation of gut microbiome functions and the crosstalk with the host. We discuss current metaproteomics and metabolomics workflows for producing functional profiles, the existing methodological challenges and limitations, and recent studies utilising these techniques with a special focus on early life gut microbiome.

Unraveling the Microbiome of Necrotizing Enterocolitis: Insights in Novel Microbial and Metabolomic Biomarkers

Necrotizing enterocolitis (NEC) is among the most relevant gastrointestinal diseases affecting mostly prematurely born infants with low birth weight. While intestinal dysbiosis has been proposed as one of the possible factors involved in NEC pathogenesis, the role of the gut microbiota remains poorly understood. In this study, the gut microbiota of preterm infants was explored to highlight differences in the composition between infants affected by NEC and infants prior to NEC development. A large-scale gut microbiome analysis was performed, including 47 shotgun sequencing data sets generated in the framework of this study, along with 124 retrieved from publicly available repositories. Meta-analysis led to the identification of preterm community state types (PT-CSTs), which recur in healthy controls and NEC infants. Such analyses revealed an overgrowth of a range of opportunistic microbial species accompanying the loss of gut microbial biodiversity in NEC subjects. Moreover, longitudinal insights into preterm infants prior to NEC development indicated Clostridium neonatale and Clostridium perfringens species as potential biomarkers for predictive early diagnosis of this disease. Furthermore, functional investigation of the enzymatic reaction profiles associated with pre-NEC condition suggested DL-lactate as a putative metabolic biomarker for early detection of NEC onset.

IMPORTANCE Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease occurring predominantly in premature infants whose etiology is still not fully understood. In this study, the analysis of infant fecal samples through shotgun metagenomics approaches revealed a marked reduction of the intestinal (bio)diversity and an overgrowth of (opportunistic) pathogens associated with the NEC development. In particular, dissection of the infant’s gut microbiome before NEC diagnosis highlighted the potential involvement of Clostridium genus members in the progression of NEC. Remarkably, our analyses highlighted a gastrointestinal DL-lactate accumulation among NEC patients that might represent a novel potential functional biomarker for the early diagnosis of NEC.

Viability-Resolved Metagenomics Reveals Antagonistic Colonization Dynamics of Staphylococcus epidermidis Strains on Preterm Infant Skin

Preterm infants are at increased risk of infections caused by coagulase-negative staphylococci (CoNS) that colonize skin. Technical barriers in sequencing low-microbial-biomass skin swabs from preterm infants hinder attempts to gain a strain-level understanding of CoNS colonization dynamics within their developing skin microbiome. Here, the microbiome of five skin sites and available stool was studied from four preterm infants hospitalized over their first 2 months of life. We used propidium monoazide treatment of samples to enrich for the viable microbiome and metagenomic shotgun sequencing to resolve species and strains. The microbiome of different skin sites overlapped with each other, was dominated by the CoNS species Staphylococcus epidermidis and Staphylococcus capitis, and was distinct from stool. Species diversity on skin increased over time despite antibiotic exposure. Evidence of antagonism between the most common S. epidermidis strains, ST2 and ST59, included negative relationships for species correlation networks and in situ replication rates and that ST2 colonized skin earlier but was often replaced by ST59 over time. Experiments done with reference isolates showed that ST2 produced more biofilm than ST59 on plastic surfaces, which was reduced in mixed culture. We also discovered that a rare S. epidermidis strain, ST5, grew rapidly in stool in association with Stenotrophomonas maltophilia from a suspected episode of infection. Viability treatment of samples and moderate throughput shotgun sequencing provides strain-level information about CoNS colonization dynamics of preterm infant skin that ultimately might be exploited to prevent infections.

IMPORTANCE The skin is a habitat for microbes that commonly infect preterm infants, but the use of sequencing for fine-scale study of the microbial communities of skin that develop in these infants has been limited by technical barriers. We treated skin swabs of preterm infants with a photoreactive dye that eliminates DNA from nonviable microbes and then sequenced the remaining DNA. We found that two strains of the most common species, Staphylococcus epidermidis, showed an antagonistic relationship on skin by cooccurring with different species, replicating fastest in different samples, and dominating skin sites at different times. Representatives of these strains also differed in their ability to stick to plastic surfaces—an important pathogenicity trait of this species. Our study shows the feasibility of gaining detailed information about strain colonization dynamics from this difficult-to-sequence body site of preterm infants, which might be used to guide novel approaches to prevent infections.

Genetic and behavioral adaptation of Candida parapsilosis to the microbiome of hospitalized infants revealed by in situ genomics, transcriptomics, and proteomics

BACKGROUND

Candida parapsilosis is a common cause of invasive candidiasis, especially in newborn infants, and infections have been increasing over the past two decades. C. parapsilosis has been primarily studied in pure culture, leaving gaps in understanding of its function in a microbiome context.

RESULTS

Here, we compare five unique C. parapsilosis genomes assembled from premature infant fecal samples, three of which are newly reconstructed, and analyze their genome structure, population diversity, and in situ activity relative to reference strains in pure culture. All five genomes contain hotspots of single nucleotide variants, some of which are shared by strains from multiple hospitals. A subset of environmental and hospital-derived genomes share variants within these hotspots suggesting derivation of that region from a common ancestor. Four of the newly reconstructed C. parapsilosis genomes have 4 to 16 copies of the gene RTA3, which encodes a lipid translocase and is implicated in antifungal resistance, potentially indicating adaptation to hospital antifungal use. Time course metatranscriptomics and metaproteomics on fecal samples from a premature infant with a C. parapsilosis blood infection revealed highly variable in situ expression patterns that are distinct from those of similar strains in pure cultures. For example, biofilm formation genes were relatively less expressed in situ, whereas genes linked to oxygen utilization were more highly expressed, indicative of growth in a relatively aerobic environment. In gut microbiome samples, C. parapsilosis co-existed with Enterococcus faecalis that shifted in relative abundance over time, accompanied by changes in bacterial and fungal gene expression and proteome composition.

CONCLUSIONS

The results reveal potentially medically relevant differences in Candida function in gut vs. laboratory environments, and constrain evolutionary processes that could contribute to hospital strain persistence and transfer into premature infant microbiomes. Video abstract.

Maturation of the preterm gastrointestinal tract can be defined by host and microbial markers for digestion and barrier defense

Functionality of the gastrointestinal tract is essential for growth and development of newborns. Preterm infants have an immature gastrointestinal tract, which is a major challenge in neonatal care. This study aims to improve the understanding of gastrointestinal functionality and maturation during the early life of preterm infants by means of gastrointestinal enzyme activity assays and metaproteomics. In this single-center, observational study, preterm infants born between 24 and 33 weeks (n = 40) and term infants born between 37 and 42 weeks (n = 3), who were admitted to Isala (Zwolle, the Netherlands), were studied. Enzyme activity analyses identified active proteases in gastric aspirates of preterm infants. Metaproteomics revealed human milk, digestive and immunological proteins in gastric aspirates of preterm infants and feces of preterm and term infants. The fecal proteome of preterm infants was deprived of gastrointestinal barrier-related proteins during the first six postnatal weeks compared to term infants. In preterm infants, bacterial oxidative stress proteins were increased compared to term infants and higher birth weight correlated to higher relative abundance of bifidobacterial proteins in postnatal week 3 to 6. Our findings indicate that gastrointestinal and beneficial microbial proteins involved in gastrointestinal maturity are associated with gestational and postnatal age.

A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome

Understanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example, species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e., nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.

Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease that primarily affects preterm infants during their first weeks after birth. Mortality rates associated with NEC are 15-30%, and surviving infants are susceptible to multiple serious, long-term complications. The disease is sporadic and, with currently available tools, unpredictable. We are creating an early warning system that uses stool microbiome features, combined with clinical and demographic information, to identify infants at high risk of developing NEC. Our approach uses a multiple instance learning, neural network-based system that could be used to generate daily or weekly NEC predictions for premature infants. The approach was selected to effectively utilize sparse and weakly annotated datasets characteristic of stool microbiome analysis. Here we describe initial validation of our system, using clinical and microbiome data from a nested case-control study of 161 preterm infants. We show receiver-operator curve areas above 0.9, with 75% of dominant predictive samples for NEC-affected infants identified at least 24 hours prior to disease onset. Our results pave the way for development of a real-time early warning system for NEC using a limited set of basic clinical and demographic details combined with stool microbiome data.

Combined analysis of microbial metagenomic and metatranscriptomic sequencing data to assess in situ physiological conditions in the premature infant gut

Microbes alter their transcriptomic profiles in response to the environment. The physiological conditions experienced by a microbial community can thus be inferred using meta-transcriptomic sequencing by comparing transcription levels of specifically chosen genes. However, this analysis requires accurate reference genomes to identify the specific genes from which RNA reads originate. In addition, such an analysis should avoid biases in transcript counts related to differences in organism abundance. In this study we describe an approach to address these difficulties. Sample-specific meta-genomic assembled genomes (MAGs) were used as reference genomes to accurately identify the origin of RNA reads, and transcript ratios of genes with opposite transcription responses were compared to eliminate biases related to differences in organismal abundance, an approach hereafter named the "diametric ratio" method. We used this approach to probe the environmental conditions experienced by Escherichia spp. in the gut of 4 premature infants, 2 of whom developed necrotizing enterocolitis (NEC), a severe inflammatory intestinal disease. We analyzed twenty fecal samples taken from four premature infants (4-6 time points from each infant), and found significantly higher diametric ratios of genes associated with low oxygen levels in samples of infants later diagnosed with NEC than in samples without NEC. We also show this method can be used for examining other physiological conditions, such as exposure to nitric oxide and osmotic pressure. These study results should be treated with caution, due to the presence of confounding factors that might also distinguish between NEC and control infants. Nevertheless, together with benchmarking analyses, we show here that the diametric ratio approach can be applied for evaluating the physiological conditions experienced by microbes in situ. Results from similar studies can be further applied for designing diagnostic methods to detect NEC in its early developmental stages.

Probiotics of Diverse Origin and Their Therapeutic Applications: A Review

The increased awareness about the harmful effects of excessive use of antibiotics has created an interest in probiotics due to its beneficial effects on gut microbiota. These advantages of probiotics have attracted researchers to find out effects on human metabolism and their role in the treatment of diverse types of diseases or disorders. Additionally, they are clinically used as biocontrol agents in the treatment of mental disorders, anticancer agents and in decreasing the threat of necrotizing enterocolitis in premature infants. In this review, we have focused on various kinds of probiotics and various nondairy substrates for their production. We have also included the importance of probiotics in the treatment of metabolic disorders, type II diabetes and infectious diseases. Furthermore, this review emphasizes applications of probiotics originated from different organisms. Their future health perspectives are discussed to gain insight into their applications.KEY TEACHING POINTSThe global market of probiotics is enormously rising day by day due to its highly beneficial effect on human microbiota.Additionally, these are used as biocontrol agents; mental disorders prevent cancer and decrease the threat of necrotizing enterocolitis (NEC) in premature infants.This review focuses on various kinds of sources of probiotics and various non-dairy substrates for the production of probiotics.The importance of probiotics in the treatment of metabolic disorders, type II diabetes control, cancer and treatment of infectious diseases are also described.It emphasizes diversified probiotics and their applications in various human health aspects and future perspectives.

The role of the preterm intestinal microbiome in sepsis and necrotising enterocolitis

Late-onset sepsis (LOS) and necrotising enterocolitis (NEC) account for the highest number of deaths in premature infants and often cause severe morbidity in survivors. NEC is an inflammatory mediated condition, but its pathophysiology remains poorly understood. There is increasing evidence that in LOS the causative organism most often translocates from the gut. No causative microorganism has been consistently associated with either LOS or NEC, but an aberrant gut microbiome development could play a pivotal role. A low bacterial diversity and a delay in anaerobic bacteria colonization may predispose preterm infants to disease development. Conversely, a predominance of Bifidobacterium species and breast milk feeding might help to prevent disease onset. With numerous studies reporting conflicting results, further research is needed to better understand the role of microorganisms and type of feeding in the health status of preterm infants.

A Robust and Universal Metaproteomics Workflow for Research Studies and Routine Diagnostics Within 24 h Using Phenol Extraction, FASP Digest, and the MetaProteomeAnalyzer

The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Genome-resolved metagenomics of eukaryotic populations during early colonization of premature infants and in hospital rooms

BACKGROUND

Fungal infections are a significant cause of mortality and morbidity in hospitalized preterm infants, yet little is known about eukaryotic colonization of infants and of the neonatal intensive care unit as a possible source of colonizing strains. This is partly because microbiome studies often utilize bacterial 16S rRNA marker gene sequencing, a technique that is blind to eukaryotic organisms. Knowledge gaps exist regarding the phylogeny and microdiversity of eukaryotes that colonize hospitalized infants, as well as potential reservoirs of eukaryotes in the hospital room built environment.

RESULTS

Genome-resolved analysis of 1174 time-series fecal metagenomes from 161 premature infants revealed fungal colonization of 10 infants. Relative abundance levels reached as high as 97% and were significantly higher in the first weeks of life (p = 0.004). When fungal colonization occurred, multiple species were present more often than expected by random chance (p = 0.008). Twenty-four metagenomic samples were analyzed from hospital rooms of six different infants. Compared to floor and surface samples, hospital sinks hosted diverse and highly variable communities containing genomically novel species, including from Diptera (fly) and Rhabditida (worm) for which genomes were assembled. With the exception of Diptera and two other organisms, zygosity of the newly assembled diploid eukaryote genomes was low. Interestingly, Malassezia and Candida species were present in both room and infant gut samples.

CONCLUSIONS

Increased levels of fungal co-colonization may reflect synergistic interactions or differences in infant susceptibility to fungal colonization. Discovery of eukaryotic organisms that have not been sequenced previously highlights the benefit of genome-resolved analyses, and low zygosity of assembled genomes could reflect inbreeding or strong selection imposed by room conditions.

Effects of early life NICU stress on the developing gut microbiome

Succession of gut microbial community structure for newborns is highly influenced by early life factors. Many preterm infants cared for in the NICU are exposed to parent-infant separation, stress, and pain from medical care procedures. The purpose of the study was to investigate the impact of early life stress on the trajectory of gut microbial structure. Stool samples from very preterm infants were collected weekly for 6 weeks. NICU stress exposure data were collected daily for 6 weeks. V4 region of the 16S rRNA gene was amplified by PCR and sequenced. Zero-inflated beta regression model with random effects was used to assess the impact of stress on gut microbiome trajectories. Week of sampling was significant for Escherichia, Staphylococcus, Enterococcus, Bifidobacterium, Proteus, Streptococcus, Clostridium butyricum, and Clostridium perfringens. Antibiotic usage was significant for Proteus, Citrobacter, and C. perfringens. Gender was significant for Proteus. Stress exposure occurring 1 and 2 weeks prior to sampling had a significant effect on Proteus and Veillonella. NICU stress exposure had a significant effect on Proteus and Veillonella. An overall dominance of Gammaproteobacteria was found. Findings suggest early life NICU stress may significantly influence the developing gut microbiome, which is important to NICU practice and future microbiome research.