Advancing functional and translational microbiome research using meta-omics approaches

Host microbiota can facilitate pathogen infection

Animals live in symbiosis with numerous microbe species. While some can protect hosts from infection and benefit host health, components of the microbiota or changes to the microbial landscape have the potential to facilitate infections and worsen disease severity. Pathogens and pathobionts can exploit microbiota metabolites, or can take advantage of a depletion in host defences and changing conditions within a host, to cause opportunistic infection. The microbiota might also favour a more virulent evolutionary trajectory for invading pathogens. In this review, we consider the ways in which a host microbiota contributes to infectious disease throughout the host's life and potentially across evolutionary time. We further discuss the implications of these negative outcomes for microbiota manipulation and engineering in disease management.

An Overview of Current Knowledge of the Gut Microbiota and Low-Calorie Sweeteners

This review provides an overview of the interrelationships among the diet, gut microbiota and health status, and then focuses specifically on published research assessing the relationship of low/no-calorie sweeteners (LNCS) to selected aspects of the gut microbiota. Microbiome research is expanding as new data on its role in health and disease vulnerability emerge. The gut microbiome affects health, digestion, and susceptibility to disease. In the last 10 years, investigations of LNCS effects on the gut microbiota have proliferated, though results are conflicting and are often confounded by differences in study design such as study diet, the form of the test article, dosage, and study population. Staying current on microbiome research and the role of dietary inputs, like LNCS, will allow healthcare and nutrition practitioners to provide evidenced-based guidance to the individuals they serve.

Gut Microbiota: Influence on Carcinogenesis and Modulation Strategies by Drug Delivery Systems to Improve Cancer Therapy

Gut microbiota have close interactions with the host. It can affect cancer progression and the outcomes of cancer therapy, including chemotherapy, immunotherapy, and radiotherapy. Therefore, approaches toward the modulation of gut microbiota will enhance cancer prevention and treatment. Modern drug delivery systems (DDS) are emerging as rational and promising tools for microbiota intervention. These delivery systems have compensated for the obstacles associated with traditional treatments. In this review, the essential roles of gut microbiota in carcinogenesis, cancer progression, and various cancer therapies are first introduced. Next, advances in DDS that are aimed at enhancing the efficacy of cancer therapy by modulating or engineering gut microbiota are highlighted. Finally, the challenges and opportunities associated with the application of DDS targeting gut microbiota for cancer prevention and treatment are briefly discussed.

Brazilian Coffee Production and the Future Microbiome and Mycotoxin Profile Considering the Climate Change Scenario

Brazil holds a series of favorable climatic conditions for agricultural production including the hours and intensity of sunlight, the availability of agricultural land and water resources, as well as diverse climates, soils and biomes. Amidst such diversity, Brazilian coffee producers have obtained various standards of qualities and aromas, between the arabica and robusta species, which each present a wide variety of lineages. However, temperatures in coffee producing municipalities in Brazil have increased by about 0.25 °C per decade and annual precipitation has decreased. Therefore, the agricultural sector may face serious challenges in the upcoming decades due to crop sensitivity to water shortages and thermal stress. Furthermore, higher temperatures may reduce the quality of the culture and increase pressure from pests and diseases, reducing worldwide agricultural production. The impacts of climate change directly affect the coffee microbiota. Within the climate change scenario, aflatoxins, which are more toxic than OTA, may become dominant, promoting greater food insecurity surrounding coffee production. Thus, closer attention on the part of authorities is fundamental to stimulate replacement of areas that are apt for coffee production, in line with changes in climate zoning, in order to avoid scarcity of coffee in the world market.

Experimental systems biology approaches reveal interaction mechanisms in model multispecies communities

Interactions between microorganisms in multispecies communities are thought to have substantial consequences for the community. Identifying the molecules and genetic pathways that contribute to such interplay is thus crucial to understand as well as modulate community dynamics. Here I focus on recent studies that utilize experimental systems biology techniques to study these phenomena in simplified model microbial communities. These unbiased biochemical and genomic approaches have identified novel interactions and described the underlying genetic and molecular mechanisms. I discuss the insights provided by these studies, describe innovative strategies used to investigate less tractable organisms and environments, and highlight the utility of integrating these and more targeted methods to comprehensively characterize interactions between species in microbial communities.

Mining Gut Microbiota From Bariatric Surgery for MAFLD

The progression of metabolic dysfunction associated fatty liver disease (MAFLD) leads to steatohepatitis, liver fibrosis and hepatocellular carcinoma. Thus far, there have been no FDA-approved medications for MAFLD. Bariatric surgery (BS) has been found to improve insulin resistance, steatohepatitis and liver fibrosis but is not recommended for treating MAFLD due to its invasiveness. Recent studies suggest the improved glucose metabolism after BS is a result of, at least partly, alterations to the gut microbiota and its associated metabolites, including short chain fatty acids and bile acids. It makes sense the improved steatohepatitis and fibrosis after BS are also induced by the gut microbiota that involves in host metabolic modulation, for example, through altering bile acids composition. Given that the gut-liver axis is a path that may harbor unexplored mechanisms behind MAFLD, we review current literatures about disentangling the metabolic benefits of MAFLD after BS, with a focus on gut microbiota. Some useful research tools including the rodent BS model, the multiomics approach, and the human microbiota associated (HMA) mice are presented and discussed. We believe, by taking advantage of these modern translational tools, researchers will uncover microbiota related pathways to serve as potential therapeutic targets for treating MAFLD.

The need to study human milk as a biological system

Critical advancement is needed in the study of human milk as a biological system that intersects and interacts with myriad internal (maternal biology) and external (diet, environment, infections) factors and its plethora of influences on the developing infant. Human-milk composition and its resulting biological function is more than the sum of its parts. Our failure to fully understand this biology in a large part contributes to why the duration of exclusive breastfeeding remains an unsettled science (if not policy). Our current understanding of human-milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation. The overly simplistic, but common, approach to analyzing single, mostly nutritive components of human milk is insufficient to understand the contribution of either individual components or the matrix within which they exist to both maternal and child health. There is a need for a shift in the conceptual approach to studying human milk to improve strategies and interventions to support better lactation, breastfeeding, and the full range of infant feeding practices, particularly for women and infants living in undernourished and infectious environments. Recent technological advances have led to a rising movement towards advancing the science of human-milk biology. Herein, we describe the rationale and critical need for unveiling the multifunctionality of the various nutritional, nonnutritional, immune, and biological signaling pathways of the components in human milk that drive system development and maturation, growth, and development in the very early postnatal period of life. We provide a vision and conceptual framework for a research strategy and agenda to change the field of human-milk biology with implications for global policy, innovation, and interventions.

Microbiome and colorectal cancer: A review of the past, present, and future

The gastrointestinal tract is home to diverse and abundant microorganisms, collectively referred to as the microbiome. This ecosystem typically contains trillions of microbial cells that play an important role in regulation of human health. The microbiome has been implicated in host immunity, nutrient absorption, digestion, and metabolism. In recent years, researchers have shown that alteration of the microbiome is associated with disease development, such as obesity, inflammatory bowel disease, and cancer. This review discusses the five decades of research into the human microbiome and the development of colorectal cancer - the historical context including experiments that sparked interest, the explosion of research that has occurred in the last decade, and finally the future of testing and treatment.

Enhancing Microbial Pollutant Degradation by Integrating Eco-Evolutionary Principles with Environmental Biotechnology

Environmental accumulation of anthropogenic pollutants is a pressing global issue. The biodegradation of these pollutants by microbes is an emerging field but is hampered by inefficient degradation rates and a limited knowledge of potential enzymes and pathways. Here, we advocate the view that significant progress can be achieved by harnessing artificial community selection for a desired biological process, an approach that makes use of eco-evolutionary principles. The selected communities can either be directly used in bioremediation applications or further be analyzed and modified, for instance through a combination of systems biology, synthetic biology, and genetic engineering. This knowledge can then inform machine learning and enhance the discovery of novel biodegradation pathways.

Microbial signatures in the lower airways of mechanically ventilated COVID19 patients associated with poor clinical outcome

Mortality among patients with COVID-19 and respiratory failure is high and there are no known lower airway biomarkers that predict clinical outcome. We investigated whether bacterial respiratory infections and viral load were associated with poor clinical outcome and host immune tone. We obtained bacterial and fungal culture data from 589 critically ill subjects with COVID-19 requiring mechanical ventilation. On a subset of the subjects that underwent bronchoscopy, we also quantified SARS-CoV-2 viral load, analyzed the microbiome of the lower airways by metagenome and metatranscriptome analyses and profiled the host immune response. We found that isolation of a hospital-acquired respiratory pathogen was not associated with fatal outcome. However, poor clinical outcome was associated with enrichment of the lower airway microbiota with an oral commensal ( Mycoplasma salivarium ), while high SARS-CoV-2 viral burden, poor anti-SARS-CoV-2 antibody response, together with a unique host transcriptome profile of the lower airways were most predictive of mortality. Collectively, these data support the hypothesis that 1) the extent of viral infectivity drives mortality in severe COVID-19, and therefore 2) clinical management strategies targeting viral replication and host responses to SARS-CoV-2 should be prioritized.

Crosstalk between Gut and Brain in Alzheimer's Disease: The Role of Gut Microbiota Modulation Strategies

The gut microbiota (GM) represents a diverse and dynamic population of microorganisms and about 100 trillion symbiotic microbial cells that dwell in the gastrointestinal tract. Studies suggest that the GM can influence the health of the host, and several factors can modify the GM composition, such as diet, drug intake, lifestyle, and geographical locations. Gut dysbiosis can affect brain immune homeostasis through the microbiota-gut-brain axis and can play a key role in the pathogenesis of neurodegenerative diseases, including dementia and Alzheimer's disease (AD). The relationship between gut dysbiosis and AD is still elusive, but emerging evidence suggests that it can enhance the secretion of lipopolysaccharides and amyloids that may disturb intestinal permeability and the blood-brain barrier. In addition, it can promote the hallmarks of AD, such as oxidative stress, neuroinflammation, amyloid-beta formation, insulin resistance, and ultimately the causation of neural death. Poor dietary habits and aging, along with inflammatory responses due to dysbiosis, may contribute to the pathogenesis of AD. Thus, GM modulation through diet, probiotics, or fecal microbiota transplantation could represent potential therapeutics in AD. In this review, we discuss the role of GM dysbiosis in AD and potential therapeutic strategies to modulate GM in AD.

Linkage between the intestinal microbiota and residual feed intake in broiler chickens

Background

Intestinal microbiota plays a key role in nutrient digestion and utilization with a profound impact on feed efficiency of livestock animals. However, the intestinal microbes that are critically involved in feed efficiency remain elusive.

Methods

To identify intestinal bacteria associated with residual feed intake (RFI) in chickens, male Cobb broiler chicks were individually housed from day 14 to day 35. Individual RFI values were calculated for 56 chickens. Luminal contents were collected from the ileum, cecum, and cloaca of each animal on day 35. Bacterial DNA was isolated and subjected to 16S rRNA gene sequencing. Intestinal microbiota was classified to the feature level using Deblur and QIIME 2. High and low RFI groups were formed by selecting 15 and 17 chickens with the most extreme RFI values for subsequent LEfSe comparison of the difference in the microbiota. Spearman correlation analysis was further performed to identify correlations between the intestinal microbiota composition and RFI.

Results

No significant difference in evenness, richness, and overall diversity of the microbiota in the ileum, cecum, or cloaca was observed between high and low RFI chickens. However, LEfSe analysis revealed a number of bacterial features being differentially enriched in either high or low RFI chickens. Spearman correlation analysis further identified many differentially enriched bacterial features to be significantly correlated with RFI (P < 0.05). Importantly, not all short-chain fatty acid (SCFA) producers showed a positive association with RFI. While two novel members of Oscillibacter and Butyricicoccus were more abundant in low-RFI, high-efficiency chickens, several other SCFA producers such as Subdoligranulum variabile and two related Peptostreptococcaceae members were negatively associated with feed efficiency. Moreover, a few closely-related Lachnospiraceae family members showed a positive correlation with feed efficiency, while others of the same family displayed an opposite relationship.

Conclusions

Our results highlight the complexity of the intestinal microbiota and a need to differentiate the bacteria to the species, subspecies, and even strain levels in order to reveal their true association with feed efficiency. Identification of RFI-associated bacteria provides important leads to manipulate the intestinal microbiota for improving production efficiency, profitability, and sustainability of poultry production.

Gut Microbiome and Nonalcoholic Fatty Liver Disease (NAFLD): Implications for Nursing Care of Individuals Living With Chronic Metabolic Diseases

At present, the incidence of nonalcoholic fatty liver disease (NAFLD) in adults is increasing year by year and at a younger age. Evidence-based healthcare has confirmed that NAFLD is closely related to obesity, cardiovascular disease, type 2 diabetes, metabolic syndrome, and other chronic metabolic diseases. Despite the growing prevalence of NAFLD, little is known about symptoms for patients at risk of NAFLD progression, thus preventing healthcare providers from intervening at an early stage. In addition, these symptoms usually cause problems for patients to cope with other chronic metabolic diseases. Symptoms may have a biological basis; especially as the changes of gut microbes may affect the symptoms of metabolic diseases. This article aims to describe the new role of gut microbes in the development of NAFLD, focusing on the potential relationship between gut microbes and symptoms of NAFLD, as well as the mechanism of action of the "gut-liver-brain" axis. This information can be useful in developing precise nursing interventions for NAFLD patients, restoring the "health" of gut microbes, and alleviating the symptom burden of chronic metabolic disease in NAFLD.

Information Theoretic Metagenome Assembly Allows the Discovery of Disease Biomarkers in Human Microbiome

Quantitative metagenomics is an important field that has delivered successful microbiome biomarkers associated with host phenotypes. The current convention mainly depends on unsupervised assembly of metagenomic contigs with a possibility of leaving interesting genetic material unassembled. Additionally, biomarkers are commonly defined on the differential relative abundance of compositional or functional units. Accumulating evidence supports that microbial genetic variations are as important as the differential abundance content, implying the need for novel methods accounting for the genetic variations in metagenomics studies. We propose an information theoretic metagenome assembly algorithm, discovering genomic fragments with maximal self-information, defined by the empirical distributions of nucleotides across the phenotypes and quantified with the help of statistical tests. Our algorithm infers fragments populating the most informative genetic variants in a single contig, named supervariant fragments. Experiments on simulated metagenomes, as well as on a colorectal cancer and an atherosclerotic cardiovascular disease dataset consistently discovered sequences strongly associated with the disease phenotypes. Moreover, the discriminatory power of these putative biomarkers was mainly attributed to the genetic variations rather than relative abundance. Our results support that a focus on metagenomics methods considering microbiome population genetics might be useful in discovering disease biomarkers with a great potential of translating to molecular diagnostics and biotherapeutics applications.

Raw Cow Milk Bacterial Consortium as Bioindicator of Circulating Anti-Microbial Resistance (AMR)

The environment, including animals and animal products, is colonized by bacterial species that are typical and specific of every different ecological niche. Natural and human-related ecological pressure promotes the selection and expression of genes related to antimicrobial resistance (AMR). These genes might be present in a bacterial consortium but might not necessarily be expressed. Their expression could be induced by the presence of antimicrobial compounds that could originate from a given ecological niche or from human activity. In this work, we applied (meta)proteomics analysis of bacterial compartment of raw milk in order to obtain a method that provides a measurement of circulating AMR involved proteins and gathers information about the whole bacterial composition. Results from milk analysis revealed the presence of 29 proteins/proteoforms linked to AMR. The detection of mainly β-lactamases suggests the possibility of using the milk microbiome as a bioindicator for the investigation of AMR. Moreover, it was possible to achieve a culture-free qualitative and functional analysis of raw milk bacterial consortia.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

A priori estimation of sequencing effort in complex microbial metatranscriptomes

Metatranscriptome analysis or the analysis of the expression profiles of whole microbial communities has the additional challenge of dealing with a complex system with dozens of different organisms expressing genes simultaneously. An underlying issue for virtually all metatranscriptomic sequencing experiments is how to allocate the limited sequencing budget while guaranteeing that the libraries have sufficient depth to cover the breadth of expression of the community. Estimating the required sequencing depth to effectively sample the target metatranscriptome using RNA-seq is an essential first step to obtain robust results in subsequent analysis and to avoid overexpansion, once the information contained in the library reaches saturation. Here, we present a method to calculate the sequencing effort using a simulated series of metatranscriptomic/metagenomic matrices. This method is based on an extrapolation rarefaction curve using a Weibull growth model to estimate the maximum number of observed genes as a function of sequencing depth. This approach allowed us to compute the effort at different confidence intervals and to obtain an approximate a priori effort based on an initial fraction of sequences. The analytical pipeline presented here may be successfully used for the in-depth and time-effective characterization of complex microbial communities, representing a useful tool for the microbiome research community.

Molecular Mechanisms of Microbiota-Mediated Pathology in Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is one of the most prevalent functional gastrointestinal disorders, and accumulating evidence gained in both preclinical and clinical studies indicate the involvement of enteric microbiota in its pathogenesis. Gut resident microbiota appear to influence brain activity through the enteric nervous system, while their composition and function are affected by the central nervous system. Based on these results, the term “brain–gut–microbiome axis” has been proposed and enteric microbiota have become a potential therapeutic target in IBS cases. However, details regarding the microbe-related pathophysiology of IBS remain elusive. This review summarizes the existing knowledge of molecular mechanisms in the pathogenesis of IBS as well as recent progress related to microbiome-derived neurotransmitters, compounds, metabolites, neuroendocrine factors, and enzymes.

Intestinal Organoids: A Tool for Modelling Diet-Microbiome-Host Interactions

Dietary patterns, microbiome dysbiosis, and gut microbial metabolites (GMMs) have a pivotal role in the homeostasis of intestinal epithelial cells and in disease progression, such as that of colorectal cancer (CRC). Although GMMs and microorganisms have crucial roles in many biological activities, models for deciphering diet-microbiome-host relationships are largely limited to animal models. Thus, intestinal organoids (IOs) have provided unprecedented opportunities for the generation of in vitro platforms with the sufficient level of complexity to model physiological and pathological diet-microbiome-host conditions. Overall, IO responses to GMM metabolites and microorganisms can provide new insights into the mechanisms by which those agents may prevent or trigger diseases, significantly extending our knowledge of diet-microbiome-host interactions.

M2IA: a web server for microbiome and metabolome integrative analysis

MOTIVATION

Microbiome-metabolome association studies have experienced exponential growth for an in-depth understanding of the impact of microbiota on human health over the last decade. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge due to the lack of a comprehensive computational tool that can facilitate data integration and interpretation. In this study, an automated microbiome and metabolome integrative analysis pipeline (M2IA) has been developed to meet the urgent needs for tools that can effectively integrate microbiome and metabolome data to derive biological insights.

RESULTS

M2IA streamlines the integrative data analysis between metabolome and microbiome, from data preprocessing, univariate and multivariate statistical analyses, advanced functional analysis for biological interpretation, to a summary report. The functionality of M2IA was demonstrated using TwinsUK cohort datasets consisting of 1116 fecal metabolites and 16s rRNA microbiome from 786 individuals. Moreover, two important metabolic pathways, i.e. benzoate degradation and phosphotransferase system, were identified to be closely associated with obesity.

AVAILABILITY AND IMPLEMENTATION

M2IA is public available at http://m2ia.met-bioinformatics.cn.

CONTACT

yanni617@zju.edu.cn or fjf68@zju.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impact of the Human Microbiome in Forensic Sciences: a Systematic Review

Numerous studies relate differences in microbial communities to human health and disease; however, little is known about microbial changes that occur postmortem or the possible applications of microbiome analysis in the field of forensic science. The aim of this review was to study the microbiome and its applications in forensic sciences and to determine the main lines of investigation that are emerging, as well as its possible contributions to the forensic field. A systematic review of the human microbiome in relation to forensic science was carried out by following PRISMA guidelines. This study sheds light on the role of microbiome research in the postmortem interval during the process of decomposition, identifying death caused by drowning or sudden death, locating the geographical location of death, establishing a connection between the human microbiome and personal items, sexual contact, and the identification of individuals. Actinomycetaceae, Bacteroidaceae, Alcaligenaceae, and Bacilli play an important role in determining the postmortem interval. Aeromonas can be used to determine the cause of death, and Corynebacterium or Helicobacter pylori can be used to ascertain personal identity or geographical location. Several studies point to a promising future for microbiome analysis in the different fields of forensic science, opening up an important new area of research.

Catabolic Machinery of the Human Gut Microbes Bestow Resilience Against Vanillin Antimicrobial Nature

Vanillin is a phenolic food additive commonly used for flavor, antimicrobial, and antioxidant properties. Though it is one of the widely used food additives, strategies of the human gut microbes to evade its antimicrobial activity await extensive elucidation. The current study explores the human gut microbiome with a multi-omics approach to elucidate its composition and metabolic machinery to counter vanillin bioactivity. A combination of SSU rRNA gene diversity, metagenomic RNA features diversity, phylogenetic affiliation of metagenome encoded proteins, uniformly (R = 0.99) indicates the abundance of Bacteroidetes followed by Firmicutes and Proteobacteria. Manual curation of metagenomic dataset identified gene clusters specific for the vanillin metabolism (ligV, ligK, and vanK) and intermediary metabolic pathways (pca and cat operon). Metagenomic dataset comparison identified the omnipresence of vanillin catabolic features across diverse populations. The metabolomic analysis brings forth the functionality of the vanillin catabolic pathway through the Protocatechuate branch of the beta-ketoadipate pathway. These results highlight the human gut microbial features and metabolic bioprocess involved in vanillin catabolism to overcome its antimicrobial activity. The current study advances our understanding of the human gut microbiome adaption toward changing dietary habits.

Prevotella in Pigs: The Positive and Negative Associations with Production and Health

A diverse and dynamic microbial community (known as microbiota) resides within the pig gastrointestinal tract (GIT). The microbiota contributes to host health and performance by mediating nutrient metabolism, stimulating the immune system, and providing colonization resistance against pathogens. Manipulation of gut microbiota to enhance growth performance and disease resilience in pigs has recently become an active area of research in an era defined by increasing scrutiny of antimicrobial use in swine production. In order to develop microbiota-targeted strategies, or to identify potential next-generation probiotic strains originating from the endogenous members of GIT microbiota in pigs, it is necessary to understand the role of key commensal members in host health. Many, though not all, correlative studies have associated members of the genus Prevotella with positive outcomes in pig production, including growth performance and immune response; therefore, a comprehensive review of the genus in the context of pig production is needed. In the present review, we summarize the current state of knowledge about the genus Prevotella in the intestinal microbial community of pigs, including relevant information from other animal species that provide mechanistic insights, and identify gaps in knowledge that must be addressed before development of Prevotella species as next-generation probiotics can be supported.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Bovine Lactoferrin Supplementation Does Not Disrupt Microbiota Development in Preterm Infants Receiving Probiotics

OBJECTIVE

The aim of the study was to assess whether bovine lactoferrin (bLf) supplementation disrupts intestinal microbiota development in preterm infants less than 31 weeks gestational age receiving prophylactic probiotic administration.

METHODS

Subjects were recruited from the LACUNA trial (ISRCTN66482337), designed to assess bLf safety. These subjects were randomized to daily receive either probiotic supplements or probiotics supplemented with 100 mg bLf mixed with their feeds (human milk or formula). Stools were collected weekly from enrolled infants for 1 month and the microbiota characterized using V6-16S rRNA gene amplicon profiling.

RESULTS

Infants' microbiomes did not increase in alpha diversity over time in both feeding interventions. Infants receiving bLf supplementation had overall higher species richness as compared with those not receiving these supplements and lactoferrin supplementation had differing effects on infant microbiota species richness depending on the infant's gestational age. Principal co-ordinate analysis revealed that the infant microbiotas did not separate by intervention group, gestational age bracket at birth or sampling time and the main factor dictating sample clustering was infant identity. There were very few detectable differences in taxa relative abundance or functional gene content between the microbiotas in the 2 study groups.

CONCLUSIONS

Bovine lactoferrin supplementation has minimal impact on microbiota composition/function in preterm infants receiving probiotics, and therefore, is unlikely to disrupt microbiota development.

MetaLab 2.0 Enables Accurate Post-Translational Modifications Profiling in Metaproteomics

Studying the structure and function of microbiomes is an emerging research field. Metaproteomic approaches focusing on the characterization of expressed proteins and post-translational modifications (PTMs) provide a deeper understanding of microbial communities. Previous research has highlighted the value of examining microbiome-wide protein expression in studying the roles of the microbiome in human diseases. Nevertheless, the regulation of protein functions in complex microbiomes remains underexplored. This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome. We have developed comprehensive software termed MetaLab for the data analysis of metaproteomic data sets. Here, we build an open search workflow within MetaLab for unbiased identification and quantification of unmodified peptides as well as peptides with various PTMs from microbiome samples. This bioinformatics platform provides information about proteins, PTMs, taxa, functions, and pathways of microbial communities. The performance of the workflow was evaluated using conventional proteomics, metaproteomics from mouse and human gut microbiomes, and modification-specific enriched data sets. Superior accuracy and sensitivity were obtained simultaneously by using our method compared with the traditional closed search strategy.

Gut-Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization

Recent advances in the field of meta-omics sciences and related bioinformatics tools have allowed a comprehensive investigation of human-associated microbiota and its contribution to achieving and maintaining the homeostatic balance. Bioactive compounds from the microbial community harboring the human gut are involved in a finely tuned network of interconnections with the host, orchestrating a wide variety of physiological processes. These includes the bi-directional crosstalk between the central nervous system, the enteric nervous system, and the gastrointestinal tract (i.e., gut-brain axis). The increasing accumulation of evidence suggest a pivotal role of the composition and activity of the gut microbiota in neurodegeneration. In the present review we aim to provide an overview of the state-of-the-art of meta-omics sciences including metagenomics for the study of microbial genomes and taxa strains, metatranscriptomics for gene expression, metaproteomics and metabolomics to identify and/or quantify microbial proteins and metabolites, respectively. The potential and limitations of each discipline were highlighted, as well as the advantages of an integrated approach (multi-omics) to predict microbial functions and molecular mechanisms related to human diseases. Particular emphasis is given to the latest results obtained with these approaches in an attempt to elucidate the link between the gut microbiota and the most common neurodegenerative diseases, such as multiple sclerosis (MS), Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics

Introduction: Metaproteomics is an established method to obtain a comprehensive taxonomic and functional view of microbial communities. After more than a decade, we are now able to describe the promise, reality, and perspectives of metaproteomics and provide useful information about the choice of method, applications, and potential improvement strategies.Areas covered: In this article, we will discuss current challenges of species and proteome coverage, and also highlight functional aspects of metaproteomics analysis of microbial communities with different levels of complexity. To do this, we re-analyzed data from microbial communities with low to high complexity (8, 72, 200 and >300 species). High species diversity leads to a reduced number of protein group identifications in a complex community, and thus the number of species resolved is underestimated. Ultimately, low abundance species remain undiscovered in complex communities. However, we observed that the main functional categories were better represented within complex microbiomes when compared to species coverage.Expert opinion: Our findings showed that even with low species coverage, metaproteomics has the potential to reveal habitat-specific functional features. Finally, we exploit this information to highlight future research avenues that are urgently needed to enhance our understanding of taxonomic composition and functions of complex microbiomes.

Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections

In this study, we evaluate the association between vaginal and cervical human papillomavirus infections high-risk types (HPV+H), negative controls (HPV-), the bacterial biota, and urinary metabolites via integration of metagenomics, metabolomics, and bioinformatics analysis. We recently proposed that testing urine as a biofluid could be a non-invasive method for the detection of cervical HPV+H infections by evaluating the association between cervical HPV types and a total of 24 urinary metabolites identified in the samples. As a follow-up study, we expanded the analysis by pairing the urine metabolome data with vaginal and cervical microbiota in selected samples from 19 Puerto Rican women diagnosed with HPV+H infections and HPV- controls, using a novel comprehensive framework, Model-based Integration of Metabolite Observations and Species Abundances 2 (MIMOSA2). This approach enabled us to estimate the functional activities of the cervicovaginal microbiome associated with HPV+H infections. Our results suggest that HPV+H infections could induce changes in physicochemical properties of the genital tract through which niche partitioning may occur. As a result, Lactobacillus sp. enrichment coincided with the depletion of L. iners and Shuttleworthia, which dominate under normal physiological conditions. Changes in the diversity of microbial species in HPV+H groups influence the capacity of new community members to produce or consume metabolites. In particular, the functionalities of four metabolic enzymes were predicted to be associated with the microbiota, including acylphosphatase, prolyl aminopeptidase, prolyl-tRNA synthetase, and threonyl-tRNA synthetase. Such metabolic changes may influence systemic health effects in women at risk of developing cervical cancer. Overall, even assuming the limitation of the power due to the small sample number, our study adds to current knowledge by suggesting how microbial taxonomic and metabolic shifts induced by HPV infections may influence the maintenance of microbial homeostasis and indicate that HPV+H infections may alter the ecological balance of the cervicovaginal microbiota, resulting in higher bacterial diversity.