The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Use of genomics & proteomics in studying lipase producing microorganisms & its application

In biotechnological applications, lipases are recognized as the most widely utilized and versatile enzymes, pivotal in biocatalytic processes, predominantly produced by various microbial species. Utilizing omics technology, natural sources can be meticulously screened to find microbial flora which are responsible for oil production. Lipases are versatile biocatalysts. They are used in a variety of bioconversion reactions and are receiving a lot of attention because of the quick development of enzyme technology and its usefulness in industrial operations. This article offers recent insights into microbial lipase sources, including fungi, bacteria, and yeast, alongside traditional and modern methods of purification such as precipitation, immunopurification and chromatographic separation. Additionally, it explores innovative methods like the reversed micellar system, aqueous two-phase system (ATPS), and aqueous two-phase flotation (ATPF). The article deals with the use of microbial lipases in a variety of sectors, including the food, textile, leather, cosmetics, paper, detergent, while also critically analyzing lipase-producing microbes. Moreover, it highlights the role of lipases in biosensors, biodiesel production, tea processing, bioremediation, and racemization. This review provides the concept of the use of omics technique in the mechanism of screening of microbial species those are capable of producing lipase and also find the potential applications.

MoNETA: MultiOmics Network Embedding for SubType Analysis

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

Transcriptomic analysis reveals differential gene expression patterns of Lacticaseibacillus casei ATCC 393 in response to ultrasound stress

In recent years, there has been a growing interest in modulating the performance of probiotic, mainly Lactic Acid Bacteria (LAB), in the field of probiotic food. Attenuation, induced by sub-lethal stresses, delays the probiotic metabolism, and induces a metabolic shift as survival strategy. In this paper, RNA sequencing was used to uncover the transcriptional regulation in Lacticaseibacillus casei ATCC 393 after ultrasound-induced attenuation. Six (T) and 8 (ST) min of sonication induced a significant differential expression of 742 and 409 genes, respectively. We identified 198 up-regulated and 321 down-regulated genes in T, and similarly 321 up-regulated and 249 down-regulated in ST. These results revealed a strong defensive response at 6 min, followed by adaptation at 8 min. Ultrasound attenuation modified the expression of genes related to a series of crucial biomolecular processes including membrane transport, carbohydrate and purine metabolism, phage-related genes, and translation. Specifically, genes encoding PTS transporters and genes involved in the glycolytic pathway and pyruvate metabolism were up-regulated, indicating an increased need for energy supply, as also suggested by an increase in the transcription of purine biosynthetic genes. Instead, protein translation, a high-energy process, was inhibited with the down-regulation of ribosomal protein biosynthetic genes. Moreover, phage-related genes were down-regulated suggesting a tight transcriptional control on DNA structure. The observed phenomena highlight the cell need of ATP to cope with the multiple ultrasound stresses and the activation of processes to stabilize and preserve the DNA structure. Our work demonstrates that ultrasound has remarkable effects on the tested strain and elucidates the involvement of different pathways in its defensive stress-response and in the modification of its phenotype.

A comprehensive review of genomics, transcriptomics, proteomics, and metabolomic insights into the differentiation of Pseudomonas aeruginosa from the planktonic to biofilm state: A multi-omics approach

Biofilm formation by Pseudomonas aeruginosa is primarily responsible for chronic wound and lung infections in humans. These infections are persistent owing to the biofilm's high tolerance to antimicrobials and constantly changing environmental factors. Understanding the mechanism governing biofilm formation can help to develop therapeutics explicitly directed against the molecular markers responsible for this process. After numerous years of research, many genes responsible for both in vitro and in vivo biofilm development remain unidentified. However, there is no "all in one" complete in vivo or in vitro biofilm model. Recent findings imply that the shift from planktonic bacteria to biofilms is a complicated and interrelated differentiation process. Research on the applications of omics technologies in P. aeruginosa biofilm development is ongoing, and these approaches hold great promise for expanding our knowledge of the mechanisms of biofilm formation. This review discusses the different factors that affect biofilm formation and compares P. aeruginosa biofilm formation using the omics approaches targeting essential biological macromolecules, such as DNA, RNA, Protein, and metabolome. Furthermore, we have outlined the application of currently available omics tools, such as genomics, proteomics, metabolomics, transcriptomics, and integrated multi-omics methodologies, to understand the differential gene expression (biofilm vs. planktonic bacteria) of P. aeruginosa biofilms.

A review of the quantitative real-time PCR and Omics approaches applied to study the effects of dietary selenium nanoparticles (nano-Se) on fish

•Selenium is an essential microelement required for the health of humans and animals. •Nano-Se have been applied in aquafeeds to enhance fish immunity and growth. •Omics are used to discover molecular mechanisms underlying biological processes. •This article reviews the omics platforms used to study the nano-Se effects in fish.

Selenium (Se) is an essential trace microelement required for the overall health of humans and animals. The importance of Se is mainly related to its participation in the structure of selenoproteins with diverse biological functions, including antioxidant defense, immunity, and thyroid hormone metabolism. The functionality of Se depends on its chemical form (inorganic and organic Se). Due to low toxicity and higher efficacy, Se nanoparticles (nano-Se) have been recently applied in aquafeeds to enhance fish performance. New technological advances have offered different Omics approaches, such as transcriptomics, proteomics, and metabolomics, to realize molecular mechanisms underlying biological processes. In recent years, Omics approaches have been employed to study nano-Se effects on fish. The present article summarizes the impacts of nano-Se supplementation on fish performance, then reviews the qRT-PCR assay and Omics-based approaches used to study the dietary nano-Se supplementation effects in fish.

High-resolution temporal profiling of E. coli transcriptional response

Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress (fast, intermediate, and steady). Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress.

The impact of milk storage temperatures on cheese quality and microbial communities at dairy processing plant scale

Cheese production is an applied biotechnology whose proper outcome relies strictly on the complex interactive dynamics which unfold within defined microbial groups. These may start being active from the collection of milk and continue up to its final stages of maturation. One of the critical parameters playing a major role is the milk refrigeration temperature before pasteurization as it can affect the proportion of psychrotrophic taxa abundance in the total milk bacterial population. While a standard temperature of 4 °C is the common choice, due to its general growth control effect, it does have a potential drawback. This is due to the fact that some cold-tolerant genera present a proteolytic activity with uncompleted proliferation, which could negatively affect curd clotting and regular cheese maturation. Moreover, accidental thermal variations of milk before cheese-making, in a plus or minus direction, can occur both at farm collection sites and during transfer to dairy plant. This present research, directly commissioned by a major fresh cheese-producing company, includes an in-factory trial. In this trial, a gradient of temperatures from 4 °C to 13 °C, which were subsequently reversed, was purposely adopted to: (a) verify sensory alterations in the resulting product at different maturation stages, and, (b) analyze, in parallel, using DNA extraction and 16S-metabarcoding sequencing from the same samples, the presence, abundance and corresponding taxonomical identity of all the bacteria featured in communities found in milk and cheese samples. Overall, 1,714 different variants were detected and sorted into 394 identified taxa. Significant bacterial community shifts in cheese were observed in response to milk refrigeration temperature and subsequently associated with samples having altered scores in sensory panel tests. In particular, proteolytic psychrotrophes were outcompeted by Enterobacteriales and by other taxa at the peak temperature of 13 °C, but aggressively increased in the descent phases, upon the cooling down of milk to values of 7 °C. Relevant clues have been collected for better anticipation of thermal abuse effects or parameter variations allowing for improved handling of technical processing conditions by the cheese manufacturing industry.

Genetic Engineering of Resident Bacteria in the Gut Microbiome

Techniques by which to genetically manipulate members of the microbiota enable both the evaluation of host-microbe interactions and an avenue by which to monitor and modulate human physiology. Genetic engineering applications have traditionally focused on model gut residents, such as Escherichia coli and lactic acid bacteria. However, emerging efforts by which to develop synthetic biology toolsets for "nonmodel" resident gut microbes could provide an improved foundation for microbiome engineering. As genome engineering tools come online, so too have novel applications for engineered gut microbes. Engineered resident gut bacteria facilitate investigations of the roles of microbes and their metabolites on host health and allow for potential live microbial biotherapeutics. Due to the rapid pace of discovery in this burgeoning field, this minireview highlights advancements in the genetic engineering of all resident gut microbes.

Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.

Rapid Detection of the Activity of Lacticaseibacillus Casei Zhang by Flow Cytometry

Food processing, e.g., freeze-drying, exerts strong pressure on bacteria in the food matrix, decreasing their viability/activity and even forcing them to become viable but unculturable (VBNC), which are often underestimated by traditional plate count. The strict standards of bacterial viability in probiotic products require accurate cell viability/activity enumeration. We developed a staining (5(6)-carboxyfluorescein diacetate succinimide ester, propidium iodide)-based flow cytometry rapid method for detecting the viability/activity of Lacticaseibacillus (Lb.) casei Zhang, a widely used probiotic in the dairy industry in China. We optimized the procedural and instrumental parameters for generating results comparable to that of standard plate counts. This method was also applied to freeze-dried Lb. casei Zhang, yielding 7.7 × 1011 CFU/g, which was non-significantly higher than the results obtained by plate count (6.4 × 1011 CFU/g), possibly due to the detection of VBNC cells in the freeze-dried powder. We anticipated that this method can be used for detecting lactic acid bacteria in other probiotic food/beverages.

A novel approach to Lactiplantibacillus plantarum: From probiotic properties to the omics insights

Lactiplantibacillus plantarum (previously known as Lactobacillus plantarum) strains are one of the lactic acid bacteria (LAB) commonly used in fermentation and their probiotic and functional properties along with their health-promoting roles come to the fore. Food-derived L. plantarum strains have shown good resistance and adhesion in the gastrointestinal tract (GI) and excellent antioxidant and antimicrobial properties. Furthermore, many strains of L. plantarum can produce bacteriocins with interesting antimicrobial activity. This probiotic properties of L. plantarum and existing in different niches give a great potential to have beneficial effects on health. It is also has been shown that L. plantarum can regulate the intestinal microbiota composition in a good way. Recently, omics approaches such as metabolomics, secretomics, proteomics, transcriptomics and genomics try to understand the roles and mechanisms of L. plantarum that are related to its functional characteristics. This review provides an overview of the probiotic properties, including the specific interactions between microbiota and host, and omics insights of L. plantarum.

The need for an integrated multi-OMICs approach in microbiome science in the food system

Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop "Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research" gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety.

Deep longitudinal multi-omics analysis of Bordetella pertussis cultivated in bioreactors highlights medium starvations and transitory metabolisms, associated to vaccine antigen biosynthesis variations and global virulence regulation

Bordetella pertussis is the bacterial causative agent of whooping cough, a serious respiratory illness. An extensive knowledge on its virulence regulation and metabolism is a key factor to ensure pertussis vaccine manufacturing process robustness. The aim of this study was to refine our comprehension of B. pertussis physiology during in vitro cultures in bioreactors. A longitudinal multi-omics analysis was carried out over 26 h small-scale cultures of B. pertussis. Cultures were performed in batch mode and under culture conditions intending to mimic industrial processes. Putative cysteine and proline starvations were, respectively, observed at the beginning of the exponential phase (from 4 to 8 h) and during the exponential phase (18 h 45 min). As revealed by multi-omics analyses, the proline starvation induced major molecular changes, including a transient metabolism with internal stock consumption. In the meantime, growth and specific total PT, PRN, and Fim2 antigen productions were negatively affected. Interestingly, the master virulence-regulating two-component system of B. pertussis (BvgASR) was not evidenced as the sole virulence regulator in this in vitro growth condition. Indeed, novel intermediate regulators were identified as putatively involved in the expression of some virulence-activated genes (vags). Such longitudinal multi-omics analysis applied to B. pertussis culture process emerges as a powerful tool for characterization and incremental optimization of vaccine antigen production.

Proteomic, Metabolomic, and Lipidomic Analyses of Lung Tissue Exposed to Mustard Gas

Sulfur mustard (HD) poses a serious threat due to its relatively simple production process. Exposure to HD in the short-term causes an inflammatory response, while long-term exposure results in DNA and RNA damage. Respiratory tract tissue models were exposed to relatively low concentrations of HD and collected at 3 and 24 h post exposure. Histology, cytokine ELISAs, and mass spectrometric-based analyses were performed. Histology and ELISA data confirmed previously seen lung damage and inflammatory markers from HD exposure. The multi-omic mass spectrometry data showed variation in proteins and metabolites associated with increased inflammation, as well as DNA and RNA damage. HD exposure causes DNA and RNA damage that results in variation of proteins and metabolites that are associated with transcription, translation and cellular energy.

Advances in multi-omics based quantitative microbial risk assessment in the dairy sector: A semi-systematic review

With the increasing consumption of packaged and ready-to-eat food products, the risk of foodborne illness has drastically increased and so has the dire need for proper management. The conventional Microbial Risk Assessment (MRA) investigations require prior knowledge of process flow, exposure, and hazard assessment throughout the supply chain. These data are often generated using conventional microbiological approaches based either on shelf-life studies or specific spoilage organisms (SSOs), frequently overlooking crucial information such as antimicrobial resistance (AMR), biofilm formation, virulence factors and other physiological variations coupled with bio-chemical characteristics of food matrix. Additionally, the microbial risks in food are diverse and heterogenous, that might be an outcome of growth and activity of multiple microbial populations rather than a single species contamination. The uncertainty on the microbial source, time as well as point of entry into the food supply chain poses a constraint to the efficiency of preventive approaches and conventional MRA. In the last few decades, significant breakthroughs in molecular methods and continuously progressing bioinformatics tools have opened up a new horizon for risk analysis-based approaches in food safety. Real time polymerase chain reaction (qPCR) and kit-based assays provide better accuracy and precision with shorter processing time. Despite these improvements, the effect of complex food matrix on growth environment and recovery of pathogen is a persistent problem for risk assessors. The dairy industry is highly impacted by spoilage and pathogenic microorganisms. Therefore, this review discusses the evolution and recent advances in MRAmethodologies equipped with predictive interventions and "multi-omics" approach for robust MRA specifically targeting dairy products. It also highlights the limiting gap area and the opportunity for improvement in this field to ensure precision food safety.

Untargeted Metabolomics Approach of Cross-Adaptation in Salmonella Enterica Induced by Major Compounds of Essential Oils

Cross-adaptation phenomena in bacterial populations, induced by sublethal doses of antibacterial solutions, are a major problem in the field of food safety. In this regard, essential oils and their major compounds appear as an effective alternative to common sanitizers in food industry environments. The present study aimed to evaluate the untargeted metabolomics perturbations of Salmonella enterica serovar Enteritidis that has been previously exposed to the sublethal doses of the major components of essential oils: cinnamaldehyde, citral, and linalool (CIN, CIT, and LIN, respectively). Cinnamaldehyde appeared to be the most efficient compound in the assays evaluating the inhibitory effects [0.06% (v/v) as MBC]. Also, preliminary tests exhibited a phenotype of adaptation in planktonic and sessile cells of S. Enteritidis when exposed to sublethal doses of linalool, resulting in tolerance to previously lethal concentrations of citral. A metabolomics approach on S. Enteritidis provided an important insight into the phenomenon of cross-adaptation induced by sublethal doses of major compounds of some essential oils. In addition, according to the results obtained, when single molecules were used, many pathways may be involved in bacterial tolerance, which could be different from the findings revealed in previous studies regarding the use of phytocomplex of essential oils. Orthogonal projection to latent structures (OPLS) proved to be an interesting predictive model to demonstrate the adaptation events in pathogenic bacteria because of the global engagement to prevent and control foodborne outbreaks.

The Impacts of Lactiplantibacillus plantarum on the Functional Properties of Fermented Foods: A Review of Current Knowledge

One of the most varied species of lactic acid bacteria is Lactiplantibacillus plantarum (Lb. plantarum), formerly known as Lactobacillus plantarum. It is one of the most common species of bacteria found in foods, probiotics, dairy products, and beverages. Studies related to genomic mapping and gene locations of Lb. plantarum have shown the novel findings of its new strains along with their non-pathogenic or non-antibiotic resistance genes. Safe strains obtained with new technologies are a pioneer in the development of new probiotics and starter cultures for the food industry. However, the safety of Lb. plantarum strains and their bacteriocins should also be confirmed with in vivo studies before being employed as food additives. Many of the Lb. plantarum strains and their bacteriocins are generally safe in terms of antibiotic resistance genes. Thus, they provide a great opportunity for improving the nutritional composition, shelf life, antioxidant activity, flavour properties and antimicrobial activities in the food industry. Moreover, since some Lb. plantarum strains have the ability to reduce undesirable compounds such as aflatoxins, they have potential use in maintaining food safety and preventing food spoilage. This review emphasizes the impacts of Lb. plantarum strains on fermented foods, along with novel approaches to their genomic mapping and safety aspects.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.

Advantages and disadvantages of non-starter lactic acid bacteria from traditional fermented foods: Potential use as starters or probiotics

Traditional fermented foods are a significant source of starter and/or non-starter lactic acid bacteria (nsLAB). Moreover, these microorganisms are also known for their role as probiotics. The potential of nsLAB is huge; however, there are still challenges to be overcome with respect to characterization and application. In the present review, the most important steps that autochthonous lactic acid bacteria isolated from fermented foods need to overcome, to qualify as novel starter cultures, or as probiotics, in food technology and biotechnology, are considered. These different characterization steps include precise identification, detection of health-promoting properties, and safety evaluation. Each of these features is strain specific and needs to be accurately determined. This review highlights the advantages and disadvantages of nsLAB, isolated from traditional fermented foods, discussing safety aspects and sensory impact.

Production of polyunsaturated fatty acids by Schizochytrium (Aurantiochytrium) spp

Diverse health benefits are associated with dietary consumption of omega-3 long-chain polyunsaturated fatty acids (ω-3 LC-PUFA), particularly docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). Traditionally, these fatty acids have been obtained from fish oil, but limited supply, variably quality, and an inability to sustainably increase production for a rapidly growing market, are driving the quest for alternative sources. DHA derived from certain marine protists (heterotrophic thraustochytrids) already has an established history of commercial production for high-value dietary use, but is too expensive for use in aquaculture feeds, a much larger potential market for ω-3 LC-PUFA. Sustainable expansion of aquaculture is prevented by its current dependence on wild-caught fish oil as the source of ω-3 LC-PUFA nutrients required in the diet of aquacultured animals. Although several thraustochytrids have been shown to produce DHA and EPA, there is a particular interest in Schizochytrium spp. (now Aurantiochytrium spp.), as some of the better producers. The need for larger scale production has resulted in development of many strategies for improving productivity and production economics of ω-3 PUFA in Schizochytrium spp. Developments in fermentation technology and metabolic engineering for enhancing LC-PUFA production in Schizochytrium spp. are reviewed.

Proteomic and metabolomic profiling of acute and chronic stress events associated with military exercises

By characterizing physiological changes that occur in warfighters during simulated combat, we can start to unravel the key biomolecular components that are linked to physical and cognitive performance. Viable field-based sensors for the warfighter must be rapid and noninvasive. In an effort to facilitate this, we applied a multiomics pipeline to characterize the stress response in the saliva of warfighters to correlate biomolecular changes with overall performance and health. In this study, two different stress models were observed - one of chronic stress and one of acute stress. In both models, significant perturbations in the immune, metabolic, and protein manufacturing/processing systems were observed. However, when differentiating between stress models, specific metabolites associated with the "fight or flight" response and protein folding were seen to be discriminate of the acute stress model.

Cadmium stress triggers significant metabolic reprogramming in Enterococcus faecium CX 2-6

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A cadmium resistant strain of Enterococcus faecium CX 2–6 is sequenced.

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Differential expression analysis found 47% of CX 2–6 genes are significantly affected by Cd treatment.

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Differentially expressed genes (DEGs) form physically linked gene clusters in the CX 2–6 genome.

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A prophage is unique to CX 2–6 and is strongly activated by high Cd concentration.

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A majority of DEGs responding to Cd treatment are present in the core genome.

Heavy metal pollutions in the soils are increasingly threatening the global crop and food production. Using plant associated bacteria to remediate heavy metal contamination is a promising approach. We have isolated a cadmium (Cd) resistant Enterococcus faecium strain CX 2–6 from a heavy metal contaminated farmland. We have shown that: (i) CX 2–6 can tolerate cadmium (Cd) with a slower growth rate; (ii) The CX 2–6 complete genome is fully assembled using PacBio long reads; (iii) Differential expression analysis found 47% of CX 2–6 genes are significantly affected by Cd treatment and form three gene groups with distinct expression profiles; (iv) Differentially expressed genes (DEGs) form physically linked gene clusters in the CX 2–6 genome, and one of the gene clusters corresponds to a prophage that is unique to CX 2–6 and is strongly activated when Cd concentration is higher; (v) A majority of DEGs responding to Cd treatment are present in the core genome; and (vi) 55 noncoding RNA genes are identified and 49 of them are DEGs responding to cadmium stress. Our pan-genome analysis and comparative RNA-seq data analysis has significantly improved our understanding of the metabolic reprogramming of E. faecium CX 2–6 under Cd stress.

Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement

"Omics" techniques (including genomics, transcriptomics, metabolomics, proteomics, and metagenomics) have been employed with huge success in the improvement of agricultural crops. As marine aquaculture of macroalgae expands globally, biologists are working to domesticate species of macroalgae by applying these techniques tested in agriculture to wild macroalgae species. Metabolomics has revealed metabolites and pathways that influence agriculturally relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in metagenomics have improved understanding of host-symbiont interactions and the potential for microbial organisms to improve crop outcomes. There is much room in the field of macroalgal biology for further research toward improvement of macroalgae cultivars in aquaculture using metabolomic and metagenomic analyses. To this end, this review discusses the application and necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance seaweed farming worldwide.

Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single- and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes.

Investigating dairy microbiome: an opportunity to ensure quality, safety and typicity

A detailed understanding of the microbiome of cheese and dairy products is key to the optimization of flavour, appearance, overall quality and safety. Microorganisms (including bacteria, yeasts, moulds and viruses, especially bacteriophages) from the environment can enter the dairy supply chain at multiple stages with several implications. The ability to track these microorganisms and to understand their function and interaction can be greatly enhanced by the use of high-throughput sequencing. Depending on the specific production technology, dairy products can harbor several strains and antibiotic-resistance genes that can potentially interact with the gut microbiome, once the product is ingested. Milk-associated or cheese-associated microbial communities with their interaction, function and diversity are a key factor for the dairy industry. Multi-omics approaches have been seldom utilized in literature and they need to be further considered. Studying the role, origin, diversity and function of the microbial species involved in the complex system of dairy production can help improve processes in several fields of application. Integrating an extensive sampling procedure with an extensive culture based methodology is necessary. To this end, local producers, and in general stakeholders, should be guided to discover and maintain their microbial diversity. A better management of microbial resources through precision fermentation processes will in turn reduce overall food losses and increase the possibility to use the microbiome in order to increase the local producers' income.

Mass spectrometry based metabolomics approach on the elucidation of volatile metabolites formation in fermented foods: A mini review

Metabolomics can be applied for comparative and quantitative analyses of the metabolic changes induced by microorganisms during fermentation. In particular, mass spectrometry (MS) is a powerful tool for metabolomics that is widely used for elucidating biomarkers and patterns of metabolic changes. Fermentation involves the production of volatile metabolites via diverse and complex metabolic pathways by the activities of microbial enzymes. These metabolites can greatly affect the organoleptic properties of fermented foods. This review provides an overview of the MS-based metabolomics techniques applied in studies of fermented foods, and the major metabolic pathways and metabolites (e.g., sugars, amino acids, and fatty acids) derived from their metabolism. In addition, we suggest an efficient tool for understanding the metabolic patterns and for identifying novel markers in fermented foods.

Genetic Diversity of Leuconostoc mesenteroides Isolates from Traditional Montenegrin Brine Cheese

In many dairy products, Leuconostoc spp. is a natural part of non-starter lactic acid bacteria (NSLAB) accounting for flavor development. However, data on the genomic diversity of Leuconostoc spp. isolates obtained from cheese are still scarce. The focus of this study was the genomic characterization of Leuconostoc spp. obtained from different traditional Montenegrin brine cheeses with the aim to explore their diversity and provide genetic information as a basis for the selection of strains for future cheese production. In 2019, sixteen Leuconostoc spp. isolates were obtained from white brine cheeses from nine different producers located in three municipalities in the northern region of Montenegro. All isolates were identified as Ln. mesenteroides. Classical multilocus sequence tying (MLST) and core genome (cg) MLST revealed a high diversity of the Montenegrin Ln. mesenteroides cheese isolates. All isolates carried genes of the bacteriocin biosynthetic gene clusters, eight out of 16 strains carried the citCDEFG operon, 14 carried butA, and all 16 isolates carried alsS and ilv, genes involved in forming important aromas and flavor compounds. Safety evaluation indicated that isolates carried no pathogenic factors and no virulence factors. In conclusion, Ln. mesenteroides isolates from Montenegrin traditional cheeses displayed a high genetic diversity and were unrelated to strains deposited in GenBank.

SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data

The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient's survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

Multiomics Imaging Using High-Energy Water Gas Cluster Ion Beam Secondary Ion Mass Spectrometry [(H2O)n-GCIB-SIMS] of Frozen-Hydrated Cells and Tissue

Integration of multiomics at the single-cell level allows the unambiguous dissecting of phenotypic heterogeneity at different states such as health, disease, and biomedical response. Imaging mass spectrometry holds the promise of being able to measure multiple types of biomolecules in parallel in the same cell. We have explored the possibility of using water gas cluster ion beam secondary ion mass spectrometry [(H2O)n-GCIB-SIMS] as an analytical tool for multiomics assay. (H2O)n-GCIB has been hailed as an ideal ionization source for biological sampling owing to the enhanced chemical sensitivity and reduced matrix effect. Taking advantage of 1 μm spatial resolution by using a high-energy beam system, we have clearly shown the enhancement of multiple intact biomolecules up to a few hundredfold in single cells. Coupled with the cryogenic sample preparation/measurement, the lipids and metabolites were imaged simultaneously within the cellular region, uncovering the pristine chemistry for integrated omics in the same sample. We have demonstrated that double-charged myelin protein fragments and single-charged multiple lipids and metabolites can be localized in the same cells/tissue with a single acquisition. Our exploration has also been extended to the capability of (H2O)n-GCIB in the generation of multiple charged peptides on protein standards. Frozen hydration combined with (H2O)n-GCIB provides the possibility of universal enhancement for the ionization of multiple bio-molecules, including peptides/proteins which has allowed "omics" to become feasible in the same sample using SIMS.

Recent advances and health implications of dietary fasting regimens on the gut microbiome

Calorie restriction is a primary dietary intervention demonstrated over many decades in cellular and animal models to modulate aging pathways, positively affect age-associated diseases and, in clinical studies, to promote beneficial health outcomes. Because long-term compliance with daily calorie restriction has proven problematic in humans several intermittent fasting regimens, including alternate day fasting and time-restricted feeding, have evolved revealing similar clinical benefits as calorie restriction. Despite significant research on the cellular and physiological mechanisms contributing to, and responsible for, these observed benefits, relatively little research has investigated the impact of these various fasting protocols on the gut microbiome (GM). Reduced external nutrient supply to the gut may beneficially alter the composition and function of a "fed" gut microflora. Indeed, the prevalent, obesogenic Western diet can promote deleterious changes in the GM, signaling intermediates involved in lipid and glucose metabolism, and immune responses in the gastrointestinal tract. This review describes recent preclinical and clinical effects of varying fasting regimens on GM composition and associated physiology. Although the number of preclinical and clinical interventions are limited, significant data thus far suggest fasting interventions impact GM composition and physiology. However, there are considerable heterogeneities of study design, methodological considerations, and practical implications. Ongoing research on the health impact of fasting regimens on GM modulation is warranted.

An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents

Bacterial biofilms are complex and highly antibiotic-resistant aggregates of microbes that form on surfaces in the environment and body including medical devices. They are key contributors to the growing antibiotic resistance crisis and account for two-thirds of all infections. Thus, there is a critical need to develop anti-biofilm specific therapeutics. Here we discuss mechanisms of biofilm formation, current anti-biofilm agents, and strategies for developing, discovering, and testing new anti-biofilm agents. Biofilm formation involves many factors and is broadly regulated by the stringent response, quorum sensing, and c-di-GMP signaling, processes that have been targeted by anti-biofilm agents. Developing new anti-biofilm agents requires a comprehensive systems-level understanding of these mechanisms, as well as the discovery of new mechanisms. This can be accomplished through omics approaches such as transcriptomics, metabolomics, and proteomics, which can also be integrated to better understand biofilm biology. Guided by mechanistic understanding, in silico techniques such as virtual screening and machine learning can discover small molecules that can inhibit key biofilm regulators. To increase the likelihood that these candidate agents selected from in silico approaches are efficacious in humans, they must be tested in biologically relevant biofilm models. We discuss the benefits and drawbacks of in vitro and in vivo biofilm models and highlight organoids as a new biofilm model. This review offers a comprehensive guide of current and future biological and computational approaches of anti-biofilm therapeutic discovery for investigators to utilize to combat the antibiotic resistance crisis.

Microbial Interactions within the Cheese Ecosystem and Their Application to Improve Quality and Safety

The cheese microbiota comprises a consortium of prokaryotic, eukaryotic and viral populations, among which lactic acid bacteria (LAB) are majority components with a prominent role during manufacturing and ripening. The assortment, numbers and proportions of LAB and other microbial biotypes making up the microbiota of cheese are affected by a range of biotic and abiotic factors. Cooperative and competitive interactions between distinct members of the microbiota may occur, with rheological, organoleptic and safety implications for ripened cheese. However, the mechanistic details of these interactions, and their functional consequences, are largely unknown. Acquiring such knowledge is important if we are to predict when fermentations will be successful and understand the causes of technological failures. The experimental use of "synthetic" microbial communities might help throw light on the dynamics of different cheese microbiota components and the interplay between them. Although synthetic communities cannot reproduce entirely the natural microbial diversity in cheese, they could help reveal basic principles governing the interactions between microbial types and perhaps allow multi-species microbial communities to be developed as functional starters. By occupying the whole ecosystem taxonomically and functionally, microbiota-based cultures might be expected to be more resilient and efficient than conventional starters in the development of unique sensorial properties.

Molecular Determinants of Antibiotic Resistance in the Costa Rican Pseudomonas aeruginosa AG1 by a Multi-omics Approach: A Review of 10 Years of Study

Pseudomonas aeruginosa AG1 (PaeAG1) is a Costa Rican strain that was isolated in 2010 in a major Hospital. This strain has resistance to multiple antibiotics such as β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. PaeAG1 is considered critical (Priority 1) due to its resistance to carbapenems, and it was the first report of a P. aeruginosa isolate carrying both VIM-2 and IMP-18 genes encoding for metallo-β-lactamases (MBL) enzymes (both with carbapenemase activity). Owing to these traits, we have studied this model for 10 years using diverse approaches including multi-omics. In this review, we summarize the main points of the different steps that we have studied in PaeAG1: preliminary analyses of this strain at the genomic and phenomic levels revealed that this microorganism has particular features of antibiotic resistance. In the multi-omics approach, the genome assembly was the initial step to identify the genomic determinants of this strain, including virulence factors, antibiotic resistance genes, as well as a complex accessory genome. Second, a comparative genomic approach was implemented to define and update the phylogenetic relationship among complete P. aeruginosa genomes, the genomic island content in other strains, and the architecture of the two MBL-carrying integrons. Third, the proteomic profile of PaeAG1 was studied after exposure to antibiotics using 2-dimensional gel electrophoresis (2D-GE). Fourth, to study the central response to multiple perturbations in P. aeruginosa, i.e., the core perturbome, a machine learning approach was used. The analysis revealed biological functions and determinants that are shared by different disturbances. Finally, to evaluate the effects of ciprofloxacin (CIP) on PaeAG1, a growth curve comparison, differential expression analysis (RNA-Seq), and network analysis were performed. Using the results of the core perturbome (pathways that also were found in this perturbation with CIP), it was possible to identify the “exclusive” response and determinants of PaeAG1 after exposure to CIP. Altogether, after a decade of study using a multi-omics approach (at genomics, comparative genomics, perturbomics, transcriptomics, proteomics, and phenomics levels), we have provided new insights about the genomic and transcriptomic determinants associated with antibiotic resistance in PaeAG1. These results not only partially explain the high-risk condition of this strain that enables it to conquer nosocomial environments and its multi-resistance profile, but also this information may eventually be used as part of the strategies to fight this pathogen.

Genomic and in vitro properties of the dairy Streptococcus thermophilus SMQ-301 strain against selected pathogens

Cumulative studies have suggested that probiotic bacterial strains could be an effective alternative in inhibiting conditions caused by foodborne and vaginal pathogens. The use of genomic techniques is becoming highly useful in understanding the potential of these beneficial microorganisms. This study presents some genomic and in vitro properties of the Streptococcus thermophilus SMQ-301 strain against foodborne and vaginal pathogens (Staphylococcus aureus, Escherichia coli, and Gardnerella vaginalis) to validate its use in dairy food formulations. Genomic analyses include bacteriocin production, stress response systems, antioxidant capability, and RAST-based functional annotation. In vitro investigations focused on the antimicrobial effects of the S. thermophilus SMQ-301 cell-free solution (CFS) against the selected pathogens after enzymatic actions and pH treatments, assessment of cytotoxic effects using murine RAW264.7 cells, and assessment of organic acid production levels using supplementary carbon sources. The results show that the S. thermophilus SMQ-301 genome possesses essential pathways for stress management, antioxidant activities, and bacteriocin production. For the first time, the bacteriocin-producing peptides of S. thermophilus SMQ-301 are reported, which gives an insight into its inhibitory potential. In vitro, the CFS of S. thermophilus SMQ-301 had significant (P < 0.05) antimicrobial effects on the selected pathogens, with S. aureus ATCC25923 being the most resistant. All antimicrobial activities of the CFS against the selected pathogens were eliminated at pH 6.5 and 7.0. S. thermophilus SMQ-301 CFS yielded the highest lactic (25.58 ± 0.24 mg mL-1) and acetic (5.53 ± 0.12 mg mL-1) acid production levels, with 1% fructooligosaccharide (P < 0.05). The S. thermophilus SMQ-301 strain also lowered murine RAW264.7 cell activities from 101.77% (control) to 80.16% (T5 - RAW264.7 cells + 1 × 109 CFU mL-1 cells) (P < 0.05). This study showed that although the S. thermophilus SMQ-301 strain had excellent genomic characteristics, the in vitro effects varied markedly against all three pathogens. In all, the S. thermophilus SMQ-301 strain has promising applications as a potential probiotic in the food and allied industries.

Fermented food products in the era of globalization: tradition meets biotechnology innovations

Omics tools offer the opportunity to characterize and trace traditional and industrial fermented foods. Bioinformatics, through machine learning, and other advanced statistical approaches, are able to disentangle fermentation processes and to predict the evolution and metabolic outcomes of a food microbial ecosystem. By assembling microbial artificial consortia, the biotechnological advances will also be able to enhance the nutritional value and organoleptics characteristics of fermented food, preserving, at the same time, the potential of autochthonous microbial consortia and metabolic pathways, which are difficult to reproduce. Preserving the traditional methods contributes to protecting the hidden value of local biodiversity, and exploits its potential in industrial processes with the final aim of guaranteeing food security and safety, even in developing countries.

Multiplex Genome Engineering Methods for Yeast Cell Factory Development

As biotechnological applications of synthetic biology tools including multiplex genome engineering are expanding rapidly, the construction of strategically designed yeast cell factories becomes increasingly possible. This is largely due to recent advancements in genome editing methods like CRISPR/Cas tech and high-throughput omics tools. The model organism, baker's yeast (Saccharomyces cerevisiae) is an important synthetic biology chassis for high-value metabolite production. Multiplex genome engineering approaches can expedite the construction and fine tuning of effective heterologous pathways in yeast cell factories. Numerous multiplex genome editing techniques have emerged to capitalize on this recently. This review focuses on recent advancements in such tools, such as delta integration and rDNA cluster integration coupled with CRISPR-Cas tools to greatly enhance multi-integration efficiency. Examples of pre-placed gate systems which are an innovative alternative approach for multi-copy gene integration were also reviewed. In addition to multiple integration studies, multiplexing of alternative genome editing methods are also discussed. Finally, multiplex genome editing studies involving non-conventional yeasts and the importance of automation for efficient cell factory design and construction are considered. Coupling the CRISPR/Cas system with traditional yeast multiplex genome integration or donor DNA delivery methods expedites strain development through increased efficiency and accuracy. Novel approaches such as pre-placing synthetic sequences in the genome along with improved bioinformatics tools and automation technologies have the potential to further streamline the strain development process. In addition, the techniques discussed to engineer S. cerevisiae, can be adapted for use in other industrially important yeast species for cell factory development.

Proteomics and Lipidomics Investigations to Decipher the Behavior of Willaertia magna C2c Maky According to Different Culture Modes

Willaertia magna C2c Maky is a free-living amoeba that has demonstrated its ability to inhibit the intracellular multiplication of some Legionella pneumophila strains, which are pathogenic bacteria inhabiting the aquatic environment. The Amoeba, an industry involved in the treatment of microbiological risk in the water and plant protection sectors, has developed a natural biocide based on the property of W. magna to manage the proliferation of the pathogen in cooling towers. In axenic liquid medium, amoebas are usually cultivated in adhesion on culture flask. However, we implemented a liquid culture in suspension using bioreactors in order to produce large quantities of W. magna. In order to investigate the culture condition effects on W. magna, we conducted a study based on microscopic, proteomics and lipidomics analyzes. According to the culture condition, amoeba exhibited two different phenotypes. The differential proteomics study showed that amoebas seemed to promote the lipid metabolism pathway in suspension culture, whereas we observed an upregulation of the carbohydrate pathway in adherent culture. Furthermore, we observed an over-regulation of proteins related to the cytoskeleton for W. magna cells grown in adhesion. Regarding the lipid analysis, suspension and adhesion cell growth showed comparable lipid class compositions. However, the differential lipid analysis revealed differences that confirmed cell phenotype differences observed by microscopy and predicted by proteomics. Overall, this study provides us with a better insight into the biology and molecular processes of W. magna in different culture lifestyles.

In Vitro and In Vivo Models for Evaluating the Oral Toxicity of Nanomedicines

Toxicity studies for conventional oral drug formulations are standardized and well documented, as required by the guidelines of administrative agencies such as the US Food & Drug Administration (FDA), the European Medicines Agency (EMA) or European Medicines Evaluation Agency (EMEA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA). Researchers tend to extrapolate these standardized protocols to evaluate nanoformulations (NFs) because standard nanotoxicity protocols are still lacking in nonclinical studies for testing orally delivered NFs. However, such strategies have generated many inconsistent results because they do not account for the specific physicochemical properties of nanomedicines. Due to their tiny size, accumulated surface charge and tension, sizeable surface-area-to-volume ratio, and high chemical/structural complexity, orally delivered NFs may generate severe topical toxicities to the gastrointestinal tract and metabolic organs, including the liver and kidney. Such toxicities involve immune responses that reflect different mechanisms than those triggered by conventional formulations. Herein, we briefly analyze the potential oral toxicity mechanisms of NFs and describe recently reported in vitro and in vivo models that attempt to address the specific oral toxicity of nanomedicines. We also discuss approaches that may be used to develop nontoxic NFs for oral drug delivery.

Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes.

FoodOmics as a new frontier to reveal microbial community and metabolic processes occurring on table olives fermentation

Table olives are considered the most widespread fermented food in the Mediterranean area and their consumption is expanding all over the world. This fermented vegetable can be considered as a natural functional food thanks to their high nutritional value and high content of bioactive compounds that contribute to the health and well-being of consumers. The presence of bioactive compounds is strongly influenced by a complex microbial consortium, traditionally exploited through culture-dependent approaches. Recently, the rapid spread of omics technologies has represented an important challenge to better understand the function, the adaptation and the exploitation of microbial diversity in different complex ecosystems, such as table olives. This review provides an overview of the potentiality of omics technologies to in depth investigate the microbial composition and the metabolic processes that drive the table olives fermentation, affecting both sensorial profile and safety properties of the final product. Finally, the review points out the role of omics approaches to raise at higher sophisticated level the investigations on microbial, gene, protein, and metabolite, with huge potential for the integration of table olives composition with functional assessments.