Use of omic technologies in early life gastrointestinal health and disease: from bench to bedside

Gut microbiome derived short chain fatty acids: Promising strategies in necrotising enterocolitis

Necrotising enterocolitis (NEC) is a devastating condition that poses a significant risk of morbidity and mortality, particularly among preterm babies. Extensive research efforts have been directed at identifying optimal treatment and diagnostic strategies but results from such studies remain unclear and controversial. Among the most promising candidates are prebiotics, probiotics and their metabolites, including short chain fatty acids (SCFAs). Such metabolites have been widely explored as possible biomarkers of gut health for different clinical conditions, with overall positive effects on the host observed. This review aims to describe the role of gut microbiome derived SCFAs in necrotising enterocolitis. Until now, information has been conflicting, with the primary focus on the main three SCFAs (acetic acid, propionic acid, and butyric acid). While numerous studies have indicated the relationship between SCFAs and NEC, the current evidence is insufficient to draw definitive conclusions about the use of these metabolites as NEC biomarkers or their potential in treatment strategies. Ongoing research in this area will help enhance both our understanding of SCFAs as valuable indicators of NEC and their practical application in clinical settings.

A pilot study of the use of the oral and faecal microbiota for the diagnosis of ulcerative colitis and Crohn's disease in a paediatric population

Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory bowel diseases (IBD) that affect the gastrointestinal tract. Changes in the microbiome and its interaction with the immune system are thought to play a key role in their development. The aim of this study was to determine whether metagenomic analysis is a feasible non-invasive diagnostic tool for IBD in paediatric patients. A pilot study of oral and faecal microbiota was proposed with 36 paediatric patients divided in three cohorts [12 with CD, 12 with UC and 12 healthy controls (HC)] with 6 months of follow-up. Finally, 30 participants were included: 13 with CD, 11 with UC and 8 HC (6 dropped out during follow-up). Despite the small size of the study population, a differential pattern of microbial biodiversity was observed between IBD patients and the control group. Twenty-one bacterial species were selected in function of their discriminant accuracy, forming three sets of potential markers of IBD. Although IBD diagnosis requires comprehensive medical evaluation, the findings of this study show that faecal metagenomics or a reduced set of bacterial markers could be useful as a non-invasive tool for an easier and earlier diagnosis.

Pre- and Postnatal Determinants Shaping the Microbiome of the Newborn in the Opinion of Pregnant Women from Silesia (Poland)

Pre- and postnatal factors influence the formation of the newborn's microbiome as early as birth and the intrauterine period has a substantial impact on the composition of the baby's gastrointestinal microbiota and its subsequent development. This study intends to measure pregnant women's knowledge of the importance of microbiota for the health of the newborn. The sample was selected based on defined inclusion and exclusion criteria. The assessment of women's knowledge was assessed by the Kolmogorov-Smirnov and Kruskal-Wallis statistical tests. This study population comprised 291 adult pregnant women with a mean age of 28.4 ± 4.7 years. A total of 34% (n = 99), 35% (n = 101), and 31.3% (n = 91) were at the 1-3 trimester, respectively. The results showed that 36.4% of the women were aware that the intrauterine period changes the makeup of the gastrointestinal microbiota, whereas 5.8% exhibited awareness of the composition of the child's normal gut microbiota. Most of the women surveyed-(72.1%)-know that colonization of the tract occurs as early as the birth period. Women with student status (those who will pursue higher education in the future) and those who had given birth to the most children exhibited higher levels of knowledge.

2022 Fleming Prize Lecture: diet-microbe-host interaction in early life

The last decade has witnessed a meteoric rise in research focused on characterizing the human microbiome and identifying associations with disease risk. The advent of sequencing technology has all but eradicated gel-based fingerprinting approaches for studying microbial ecology, while at the same time traditional microbiological culture is undergoing a renaissance. Although multiplexed high-throughput sequencing is relatively new, the discoveries leading to this are nearly 50 years old, coinciding with the inaugural Microbiology Society Fleming Prize lecture. It was an honour to give the 2022 Fleming Prize lecture and this review will cover the topics from that lecture. The focus will be on the bacterial community in early life, beginning with term infants before moving on to infants delivered prematurely. The review will discuss recent work showing how human milk oligosaccharides (HMOs), an abundant but non-nutritious component of breast milk, can modulate infant microbiome and promote the growth of Bifidobacterium spp. This has important connotations for preterm infants at risk of necrotizing enterocolitis, a devastating intestinal disease representing the leading cause of death and long-term morbidity in this population. With appropriate mechanistic studies, it may be possible to harness the power of breast milk bioactive factors and infant gut microbiome to improve short- and long-term health in infants.

Biomarkers of necrotizing enterocolitis in the era of machine learning and omics

Necrotizing enterocolitis (NEC) continues to be a major cause of morbidity and mortality in preterm infants. Despite decades of research in NEC, no reliable biomarkers can accurately diagnose NEC or predict patient prognosis. The recent emergence of multi-omics could potentially shift NEC biomarker discovery, particularly when evaluated using systems biology techniques. Furthermore, the use of machine learning and artificial intelligence in analyzing this 'big data' could enable novel interpretations of NEC subtypes, disease progression, and potential therapeutic targets, allowing for integration with personalized medicine approaches. In this review, we evaluate studies using omics technologies and machine learning in the diagnosis of NEC. Future implications and challenges inherent to the field are also discussed.

Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.

The Metabolome and the Gut Microbiota for the Prediction of Necrotizing Enterocolitis and Spontaneous Intestinal Perforation: A Systematic Review

Necrotizing enterocolitis (NEC) is the most devastating gastrointestinal emergency in preterm neonates. Research on early predictive biomarkers is fundamental. This is a systematic review of studies applying untargeted metabolomics and gut microbiota analysis to evaluate the differences between neonates affected by NEC (Bell’s stage II or III), and/or by spontaneous intestinal perforation (SIP) versus healthy controls. Five studies applying metabolomics (43 cases, 95 preterm controls) and 20 applying gut microbiota analysis (254 cases, 651 preterm controls, 22 term controls) were selected. Metabolomic studies utilized NMR spectroscopy or mass spectrometry. An early urinary alanine/histidine ratio >4 showed good sensitivity and predictive value for NEC in one study. Samples collected in proximity to NEC diagnosis demonstrated variable pathways potentially related to NEC. In studies applying untargeted gut microbiota analysis, the sequencing of the V3−V4 or V3 to V5 regions of the 16S rRNA was the most used technique. At phylum level, NEC specimens were characterized by increased relative abundance of Proteobacteria compared to controls. At genus level, pre-NEC samples were characterized by a lack or decreased abundance of Bifidobacterium. Finally, at the species level Bacteroides dorei, Clostridium perfringens and perfringens-like strains dominated early NEC specimens, whereas Clostridium butyricum, neonatale and Propionibacterium acnei those at disease diagnosis. Six studies found a lower Shannon diversity index in cases than controls. A clear separation of cases from controls emerged based on UniFrac metrics in five out of seven studies. Importantly, no studies compared NEC versus SIP. Untargeted metabolomics and gut microbiota analysis are interrelated strategies to investigate NEC pathophysiology and identify potential biomarkers. Expression of quantitative measurements, data sharing via biorepositories and validation studies are fundamental to guarantee consistent comparison of results.

Null Function of Npr1 Disturbs Immune Response in Colonic Inflammation During Early Postnatal Stage

Natriuretic peptide receptor 1 (NPR1) is conventionally known as a regulator of vascular homeostasis. Here, we generated an Npr1 knockout mouse model with CRISPR/Cas9 technology and found that homozygous mice (Npr1^−/−) exhibited weight loss and poor survival rate during early postnatal stage. Careful examination revealed unexpectedly that Npr1^−/− mice developed colitis characterized by shortened colon, evident colonic mucosal damage, increased histopathological score, and higher colonic expression of proinflammatory cytokines interleukin-1B (IL1B) and -6 (IL6). RNA-sequencing analysis revealed that differentially expressed genes were prominently enriched in the biological pathways related to immune response in both spleen and colon of Npr1^−/− mice. Cytofluorimetric analysis demonstrated that leukocytes in the spleen were significantly increased, particularly, the populations of neutrophil and CD3⁺ T cell were elevated but CD4⁺ T cells were decreased in Npr1^−/− mice. Administration of 8-Br-cGMP, a downstream activator of NPR1, restored these immune-cell populations disturbed in Npr1^−/− mice and lessened the colitis-related phenotypes. To validate the involvement of Npr1 in colitis, we examined another mouse model induced by dextran sodium sulfate (DSS) and found a decreased Npr1 expression and shifted immune-cell populations as well. Importantly, 8-Br-cGMP treatment exhibited a similar effect in the restoration of immune-cell populations and attenuation of colonic inflammation in DSS mice. Our data indicate that loss of Npr1 possibly interrupts immune response, which is critical to the pathogenesis of colitis in the early life.

Metagenomics Approaches to Investigate the Neonatal Gut Microbiome

Early infancy is critical for the development of an infant's gut flora. Many factors can influence microbiota development during the pre- and postnatal periods, including maternal factors, antibiotic exposure, mode of delivery, dietary patterns, and feeding type. Therefore, investigating the connection between these variables and host and microbiome interactions in neonatal development would be of great interest. As the "unculturable" era of microbiome research gives way to an intrinsically multidisciplinary field, microbiome research has reaped the advantages of technological advancements in next-generation sequencing, particularly 16S rRNA gene amplicon and shotgun sequencing, which have considerably expanded our knowledge about gut microbiota development during early life. Using omics approaches to explore the neonatal microbiome may help to better understand the link between the microbiome and newborn diseases. Herein, we summarized the metagenomics methods and tools used to advance knowledge on the neonatal microbiome origin and evolution and how the microbiome shapes early and late individuals' lives for health and disease. The way to overcome limitations in neonatal microbiome studies will be discussed.

Machine Learning Advances in Microbiology: A Review of Methods and Applications

Microorganisms play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimize variational functions to aid microbiology using various types of available data to help humans organize and apply collective knowledge of various research objects in a systematic and scaled manner. Classification and prediction have become the main achievements in the development of microbial community research in the direction of computational biology. This review summarizes the application and development of machine learning and deep learning in the field of microbiology and shows and compares the advantages and disadvantages of different algorithm tools in four fields: microbiome and taxonomy, microbial ecology, pathogen and epidemiology, and drug discovery.

Development of the gut microbiome in early life

New Findings

What is the topic of this review?

The importance of the early life gut microbiome, with a focus on preterm infants and microbially related diseases. Current techniques to study the preterm gut microbiome are appraised, and the potential of recent methodological advancements is discussed.

What advances does it highlight?

Recent findings in the field achieved by the application of advanced technologies, the applicability of intestinally derived organoid models to study host–microbiome interactions in the preterm gut, and recent developments in enhancing the physiological relevance of such models. Preterm intestinally derived organoids may provide novel insights into the mechanisms underlying preterm disease, as well as diagnosis and treatment opportunities. These models have huge translational potential, offering a step towards precision medicine.

Abstract

Accumulating evidence affirms the importance of the gut microbiome in both health and disease. In early life, there exists a critical period in which the composition of gut microbes is particularly malleable and subject to a wide range of influencing factors. Disturbances to microbial communities during this time may be beneficial or detrimental to short and long‐term health outcomes. For infants born prematurely, naïve immune systems, immature gastrointestinal tracts and additional clinical needs put this population at high risk of abnormal microbial colonisation, resulting in increased susceptibility to diseases including necrotising enterocolitis (NEC) and late‐onset sepsis (LOS). Traditional cell culture methods, gnotobiotic animals, molecular sequencing techniques (16S rRNA gene sequencing and metagenomics) and advanced ‘omics’ technologies (transcriptomics, proteomics and metabolomics) have been fundamental in exploring the associations between diet, gut microbes, microbial functions and disease. Despite significant investment and ongoing research efforts, prevention and treatment strategies in NEC and LOS remain limited. Recent endeavours have focused on searching for new, more physiologically relevant models to simulate the preterm intestine. Preterm intestinally derived organoids represent a promising in vitro approach in the study of host–microbiome interactions in the preterm infant gut, offering new and exciting possibilities in this field.