Multi-Omics and Genome-Scale Modeling Reveal a Metabolic Shift During C. elegans Aging

A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

Multi-omics studies have emerged as powerful tools for tailoring individualized responses to various conditions, capitalizing on genome sequencing technologies' increasing affordability and efficiency. This paper delves into the potential of multi-omics in deepening our understanding of biological age, examining the techniques available in light of evolving technology and computational models. The primary objective is to review the relationship between ionizing radiation and biological age, exploring a wide array of functional, physiological, and psychological parameters. This comprehensive review draws upon an extensive range of sources, including peer-reviewed journal articles, government documents, and reputable websites. The literature review spans from fundamental insights into radiation effects to the latest developments in aging research. Ionizing radiation exerts its influence through direct mechanisms, notably single- and double-strand DNA breaks and cross links, along with other critical cellular events. The cumulative impact of DNA damage forms the foundation for the intricate process of natural aging, intersecting with numerous diseases and pivotal biomarkers. Furthermore, there is a resurgence of interest in ionizing radiation research from various organizations and countries, reinvigorating its importance as a key contributor to the study of biological age. Biological age serves as a vital reference point for the monitoring and mitigation of the effects of various stressors, including ionizing radiation. Ionizing radiation emerges as a potent candidate for modeling the separation of biological age from chronological age, offering a promising avenue for tailoring protocols across diverse fields, including the rigorous demands of space exploration.

In vivo neuroprotective capacity of a Dunaliella salina extract - comprehensive transcriptomics and metabolomics study

In this study, an exhaustive chemical characterization of a Dunaliella salina (DS) microalga extract obtained using supercritical fluids has been performed, and its neuroprotective capacity has been evaluated in vivo using an Alzheimer's disease (AD) transgenic model of Caenorhabditis elegans (strain CL4176). More than 350 compounds were annotated in the studied DS extract, with triacylglycerols, free fatty acids (FAs), carotenoids, apocarotenoids and glycerol being the most abundant. DS extract significantly protects C. elegans in a dose-dependent manner against Aβ-peptide paralysis toxicity, after 32 h, 53% of treated worms at 50 µg/mL were not paralyzed. This concentration was selected to further evaluate the transcriptomics and metabolomics changes after 26 h by using advanced analytical methodologies. The RNA-Seq data showed an alteration of 150 genes, mainly related to the stress and detoxification responses, and the retinol and lipid metabolism. The comprehensive metabolomics and lipidomics analyses allowed the identification of 793 intracellular metabolites, of which 69 were significantly altered compared to non-treated control animals. Among them, different unsaturated FAs, lysophosphatidylethanolamines, nucleosides, dipeptides and modified amino acids that have been previously reported as beneficial during AD progression, were assigned. These compounds could explain the neuroprotective capacity observed, thus, providing with new evidences of the protection mechanisms of this promising extract.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Human wild-type and D76N β2-microglobulin variants are significant proteotoxic and metabolic stressors for transgenic C. elegans

β2-microglobulin (β2-m) is a plasma protein derived from physiological shedding of the class I major histocompatibility complex (MHCI), causing human systemic amyloidosis either due to persistently high concentrations of the wild-type (WT) protein in hemodialyzed patients, or in presence of mutations, such as D76N β2-m, which favor protein deposition in the adulthood, despite normal plasma levels. Here we describe a new transgenic Caenorhabditis elegans (C. elegans) strain expressing human WT β2-m at high concentrations, mimicking the condition that underlies dialysis-related amyloidosis (DRA) and we compare it to a previously established strain expressing the highly amyloidogenic D76N β2-m at lower concentrations. Both strains exhibit behavioral defects, the severity of which correlates with β2-m levels rather than with the presence of mutations, being more pronounced in WT β2-m worms. β2-m expression also has a deep impact on the nematodes' proteomic and metabolic profiles. Most significantly affected processes include protein degradation and stress response, amino acids metabolism, and bioenergetics. Molecular alterations are more pronounced in worms expressing WT β2-m at high concentration compared to D76N β2-m worms. Altogether, these data show that β2-m is a proteotoxic protein in vivo also in its wild-type form, and that concentration plays a key role in modulating pathogenicity. Our transgenic nematodes recapitulate the distinctive features subtending DRA compared to hereditary β2-m amyloidosis (high levels of non-mutated β2-m vs. normal levels of variant β2-m) and provide important clues on the molecular bases of these human diseases.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Distinct global metabolomic profiles of the model organism Caenorhabditis elegans during interactions with Staphylococcus aureus and Salmonella enterica Serovar Typhi

The interactive network of hosts with pathogenic microbes is still questionable. It has been hypothesized and reported that the host shows altered regulatory mechanisms for different pathogens. Several studies using transcriptomics and proteomics revealed the altered pathways and sequential regulations displayed by the host during bacterial interactions. Still, there is a gap in understanding the triggering molecule at transcriptomic and proteomic levels due to the lack of the knowledge of the interactive metabolites produced during their interactions. In this study, the global metabolomic approach was performed in the nematode model organism Caenorhabditis elegans upon exposure to a Gram-negative bacteria, Salmonella enterica Serovar Typhi, and a Gram-positive bacteria, Staphylococcus aureus, and the whole metabolome was categorized as endo-metabolome (internally produced) and exo-metabolome (externally releasing). The extracted metabolites were subjected to liquid chromatography mass spectrometry (ESI-LC/qToF-MS/MS). In total 5578, 4554 and 4046 endo-metabolites and 4451, 3625 and 1281 exo-metabolites were identified in C. elegans when exposed to E. coli OP50, S. Typhi and S. aureus, respectively. Both the multivariate and univariate analyses were performed. The variation in endo- and exo-metabolome during candidate bacterial interactions was observed. The results indicated that, during S. aureus interaction, the exclusively enriched metabolites were significantly involved in alpha-linoleic acid metabolism. Similarly, the exclusively enriched metabolites during the interaction of S. Typhi were significantly involved in the phosphatidylinositol signalling system. The whole metabolomic profile presented here will build the scope to understand the role of metabolites and the respective pathways in host response during the early period of bacterial infections.

Caenorhabditis elegans in microgravity: An omics perspective

The application of omics to study Caenorhabditis elegans (C. elegans) in the context of spaceflight is increasing, illuminating the wide-ranging biological impacts of spaceflight on physiology. In this review, we highlight the application of omics, including transcriptomics, genomics, proteomics, multi-omics, and integrated omics in the study of spaceflown C. elegans, and discuss the impact, use, and future direction of this branch of research. We highlight the variety of molecular alterations that occur in response to spaceflight, most notably changes in metabolic and neuromuscular gene regulation. These transcriptional features are reproducible and evident across many spaceflown species (e.g., mice and astronauts), supporting the use of C. elegans as a model organism to study spaceflight physiology with translational capital. Integrating tissue-specific, spatial, and multi-omics approaches, which quantitatively link molecular responses to phenotypic adaptations, will facilitate the identification of candidate regulatory molecules for therapeutic intervention and thus represents the next frontiers in C. elegans space omics research.

Mulberry leaves attenuate D-galactose-induced aging in vivo and in vitro

ETHNOPHARMACOLOGICAL RELEVANCE

Mulberry leaves contain many bioactive compounds and have been widely used in traditional medicines and functional foods for prevention and treatment of age-related diseases, such as diabetes, cognitive impairment and obesity-mediated liver cancer. Aging has an irreversible negative impact on human health for many years, even decades, before death, which is a social and economic burden on society.

AIM OF THE STUDY

The objective of this study was to investigate the antioxidant and anti-aging effects of mulberry leaf extract (MLE) in vivo and in vitro.

MATERIALS AND METHODS

The Caenorhabditis elegans (C. elegans) was used as a model organism to observe the effects of different concentrations of MLE (1, 2, 4, 8 mg/mL) on nematodes' healthy lifespan, reproductive capacity, locomotion, stress resistance, and antioxidation. In addition, D-galactose (D-gal) induced liver aging in mice and L-02 cells were established. The antioxidant and anti-aging effects of MLE were evaluated by body weight, organ indexes, malondialdehyde (MDA), total superoxide dismutase (T-SOD), total antioxidant capacity (T-AOC), aspartate and alanine aminotransferases (AST and ALT), reactive oxygen species (ROS), mitochondrial membrane potential (MMP), hematoxylin and eosin (H&E), senescence-associated β-galactosidase (SA-β-Gal). Besides, the expressions of AMPK/SIRT1/PGC-1α and Nrf2-Keap1 were detected by Western blotting.

RESULTS

MLE could significantly prolonged nematodes' average life span and improved most physiological indicators related to aging of C. elegans. Moreover, Treatment with MLE ameliorated the decreased body weight and organ index (weight of organ/body weight) in model mice, and protected against oxidative stress in mice and liver cells, in a dose-dependent manner, up-regulating T-SOD and T-AOC, while reducing ROS and MDA levels. MLE decreased both liver and cell levels of AST and ALT, and enhanced the mitochondrial membrane potential. MLE activated the AMPK/SIRT1/PGC-1α pathways, participated in mitochondrial biosynthesis and oxidative metabolism and delayed D-gal-induced aging. MLE promoted the accumulation of Nrf2 in the nucleus, indicating that the improved oxidative stress response was mediated by the Nrf2-Keap1 pathway in vivo and in vitro.

CONCLUSION

MLE appeared to have great potential for stimulating the oxidative stress response and attenuating the aging process of in vivo and in vitro, and provide a novel health-promoting resource against aging and aging-related diseases.

Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes

The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.

Alpiniae oxyphylla fructus extract promotes longevity and stress resistance of C. elegans via DAF-16 and SKN-1

Background: Alpiniae Oxyphylla Fructus (AOF) is Traditional Chinese medicine and a dietary supplements for centuries, which posseses cardiotonic, neuroprotective, antioxidant, warming the kidney and nourish the spleen, these biological fuction is related to potential anti-aging properties. However, little is known about their effects on aging. This work aimed to investigate the effects of extracts of AOF on longevity and stress resistance in Caenorhabditis elegans (C. elegans) and the mechanisms that underlie its effects. Methods: Wild-type (WT) strand of C.elegans (N2)worms were cultured in growth medium with or without AOF. First, we examined the effects of AOF on lifespan, reproduction and healthspan assay, stress resistance and oxidative analysis, lipofuscin levels. Second, The levels of ROS and MDA, the antioxidant enzyme activities were examined to explore the underlying mechanism of AOF. Finally, the expression of the longevity-related genes were investigated to further understand the AOF's underlying mechanism. Results: The lifespan of C. elegans was prolonged by 23.44% after treatment with high-dose AOF (100 ug/ml). AOF alleviated aging-related declines in C. elegans health and enhanced resistance to heat shock. Furthermore, AOF decreased reactive oxygen species and malondialdehyde, increased the activities of superoxide dismutase and catalase, and reduced accumulation of fat. AOF upregulated the expression of sod-3, gst-4, daf-16, and skn-1 but downregulated the expression of daf-2 and age-1 and accelerated the translocation of DAF-16 into the nucleus. The extended lifespan induced by AOF was reversed in daf-16(mu86) and skn-1(zu135) mutants, indicating that this gene is involved in AOF-regulated longevity. Conclusion: Our findings demonstrated that AOF extends lifespan and healthspan and enhances stress via boosting the activity of the antioxidant enzyme and controlling the expression of genes associated with insulin/IGF signaling and SKN-1 pathways. As a result, this work suggested AOF as a possible candidate to reduce the signs of aging by activating and inhibiting target genes.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Multi-omics analysis reveals neuroinflammation, activated glial signaling, and dysregulated synaptic signaling and metabolism in the hippocampus of aged mice

Aging is an intricate biological event that occurs in both vertebrates and invertebrates. During the aging process, the brain, a vulnerable organ, undergoes structural and functional alterations, resulting in behavioral changes. The hippocampus has long been known to be critically associated with cognitive impairment, dementia, and Alzheimer’s disease during aging; however, the underlying mechanisms remain largely unknown. In this study, we hypothesized that altered metabolic and gene expression profiles promote the aging process in the hippocampus. Behavioral tests showed that exploration, locomotion, learning, and memory activities were reduced in aged mice. Metabolomics analysis identified 69 differentially abundant metabolites and showed that the abundance of amino acids, lipids, and microbiota-derived metabolites (MDMs) was significantly altered in hippocampal tissue of aged animals. Furthermore, transcriptomic analysis identified 376 differentially expressed genes in the aged hippocampus. A total of 35 differentially abundant metabolites and 119 differentially expressed genes, constituting the top 200 correlations, were employed for the co-expression network. The multi-omics analysis showed that pathways related to inflammation, microglial activation, synapse, cell death, cellular/tissue homeostasis, and metabolism were dysregulated in the aging hippocampus. Our data revealed that metabolic perturbations and gene expression alterations in the aged hippocampus were possibly linked to their behavioral changes in aged mice; we also provide evidence that altered MDMs might mediate the interaction between gut and brain during the aging process.

Structured Long-Chain Omega-3 Fatty Acids for Improvement of Cognitive Function during Aging

Although the human lifespan has increased in the past century owing to advances in medicine and lifestyle, the human healthspan has not kept up the same pace, especially in brain aging. Consequently, the role of preventive health interventions has become a crucial strategy, in particular, the identification of nutritional compounds that could alleviate the deleterious effects of aging. Among nutrients to cope with aging in special cognitive decline, the long-chain omega-3 polyunsaturated fatty acids (ω-3 LCPUFAs) docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), have emerged as very promising ones. Due to their neuroinflammatory resolving effects, an increased status of DHA and EPA in the elderly has been linked to better cognitive function and a lower risk of dementia. However, the results from clinical studies do not show consistent evidence and intake recommendations for old adults are lacking. Recently, supplementation with structured forms of EPA and DHA, which can be derived natural forms or targeted structures, have proven enhanced bioavailability and powerful benefits. This review summarizes present and future perspectives of new structures of ω-3 LCPUFAs and the role of "omic" technologies combined with the use of high-throughput in vivo models to shed light on the relationships and underlying mechanisms between ω-3 LCPUFAs and healthy aging.

Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes

We design a “wisdom-of-the-crowds” GRN inference pipeline and couple it to complex network analysis to understand the organizational principles governing gene regulation in long-lived glp-1/Notch Caenorhabditis elegans. The GRN has three layers (input, core, and output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess the functional importance of structural layers, we screened 80% of regulators and discovered 50 new aging genes, 86% with human orthologues. Genes essential for longevity—including ones involved in insulin-like signaling (ILS)—are at the core, indicating that GRN's structure is predictive of functionality. We used in vivo reporters and a novel functional network covering 5,497 genetic interactions to make mechanistic predictions. We used genetic epistasis to test some of these predictions, uncovering a novel transcriptional regulator, sup-37, that works alongside DAF-16/FOXO. We present a framework with predictive power that can accelerate discovery in C. elegans and potentially humans.

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Gene-regulatory inference provides global network of long-lived animals

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The large-scale topology of the network has an hourglass structure

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Membership to the core of the hourglass is a good predictor of functionality

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Discovered 50 novel aging genes, including sup-37, a DAF-16 dependent gene

Combinatorial Assembly of Modular Glucosides via Carboxylesterases Regulates C. elegans Starvation Survival

The recently discovered modular glucosides (MOGLs) form a large metabolite library derived from combinatorial assembly of moieties from amino acid, neurotransmitter, and lipid metabolism in the model organism C. elegans. Combining CRISPR-Cas9 genome editing, comparative metabolomics, and synthesis, we show that the carboxylesterase homologue Cel-CEST-1.2 is responsible for specific 2-O-acylation of diverse glucose scaffolds with a wide variety of building blocks, resulting in more than 150 different MOGLs. We further show that this biosynthetic role is conserved for the closest homologue of Cel-CEST-1.2 in the related nematode species C. briggsae, Cbr-CEST-2. Expression of Cel-cest-1.2 and MOGL biosynthesis are strongly induced by starvation conditions in C. elegans, one of the premier model systems for mechanisms connecting nutrition and physiology. Cel-cest-1.2-deletion results in early death of adult animals under starvation conditions, providing first insights into the biological functions of MOGLs.

Quantification of Age-Related Decline in Transcriptional Homeostasis

Age-dependent dysregulation of transcription regulatory machinery triggers modulations in the gene expression levels leading to the decline in cellular fitness. Tracking of these transcripts along the temporal axis in multiple species revealed a spectrum of evolutionarily conserved pathways, such as electron transport chain, translation regulation, DNA repair, etc. Recent shreds of evidence suggest that aging deteriorates the transcription machinery itself, indicating the hidden complexity of the aging transcriptomes. This reinforces the need for devising novel computational methods to view aging through the lens of transcriptomics. Here, we present Homeostatic Divergence Score (HDS) to quantify the extent of messenger RNA (mRNA) homeostasis by assessing the balance between spliced and unspliced mRNA repertoire in single cells. We validated its utility in two independent aging datasets, and identified sets of genes undergoing age-related breakdown of transcriptional homeostasis. Moreover, testing of our method on a subpopulation of human embryonic stem cells revealed a set of differentially processed transcripts segregating these subpopulations. Our preliminary analyses in this direction suggest that mRNA processing level information offered by single-cell RNA sequencing (scRNA-seq) data is a superior determinant of chronological age as compared to transcriptional noise.

Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

Background

Aging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration.

Recent Advances

In this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today.

Critical Issues

Although the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types.

Future Directions

We critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.

Regulation of the one carbon folate cycle as a shared metabolic signature of longevity

The metabolome represents a complex network of biological events that reflects the physiologic state of the organism in health and disease. Additionally, specific metabolites and metabolic signaling pathways have been shown to modulate animal ageing, but whether there are convergent mechanisms uniting these processes remains elusive. Here, we used high resolution mass spectrometry to obtain the metabolomic profiles of canonical longevity pathways in C. elegans to identify metabolites regulating life span. By leveraging the metabolomic profiles across pathways, we found that one carbon metabolism and the folate cycle are pervasively regulated in common. We observed similar changes in long-lived mouse models of reduced insulin/IGF signaling. Genetic manipulation of pathway enzymes and supplementation with one carbon metabolites in C. elegans reveal that regulation of the folate cycle represents a shared causal mechanism of longevity and proteoprotection. Such interventions impact the methionine cycle, and reveal methionine restriction as an underlying mechanism. This comparative approach reveals key metabolic nodes to enhance healthy ageing.

On the origins and conceptual frameworks of natural plasticity-Lessons from single-cell models in C. elegans

How flexible are cell identities? This problem has fascinated developmental biologists for several centuries and can be traced back to Abraham Trembley's pioneering manipulations of Hydra to test its regeneration abilities in the 1700s. Since the cell theory in the mid-19th century, developmental biology has been dominated by a single framework in which embryonic cells are committed to specific cell fates, progressively and irreversibly acquiring their differentiated identities. This hierarchical, unidirectional and irreversible view of cell identity has been challenged in the past decades through accumulative evidence that many cell types are more plastic than previously thought, even in intact organisms. The paradigm shift introduced by such plasticity calls into question several other key traditional concepts, such as how to define a differentiated cell or more generally cellular identity, and has brought new concepts, such as distinct cellular states. In this review, we want to contribute to this representation by attempting to clarify the conceptual and theoretical frameworks of cell plasticity and identity. In the context of these new frameworks we describe here an atlas of natural plasticity of cell identity in C. elegans, including our current understanding of the cellular and molecular mechanisms at play. The worm further provides interesting cases at the borderlines of cellular plasticity that highlight the conceptual challenges still ahead. We then discuss a set of future questions and perspectives arising from the studies of natural plasticity in the worm that are shared with other reprogramming and plasticity events across phyla.

Quo Vadis Caenorhabditis elegans Metabolomics-A Review of Current Methods and Applications to Explore Metabolism in the Nematode

Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.

Metabolomics and lipidomics in Caenorhabditis elegans using a single-sample preparation

Comprehensive metabolomic and lipidomic mass spectrometry methods are in increasing demand; for instance, in research related to nutrition and aging. The nematode Caenorhabditis elegans is a key model organism in these fields, owing to the large repository of available C. elegans mutants and their convenient natural lifespan. Here, we describe a robust and sensitive analytical method for the semi-quantitative analysis of >100 polar (metabolomics) and >1000 apolar (lipidomics) metabolites in C. elegans, using a single-sample preparation. Our method is capable of reliably detecting a wide variety of biologically relevant metabolic aberrations in, for example, glycolysis and the tricarboxylic acid cycle, pyrimidine metabolism and complex lipid biosynthesis. In conclusion, we provide a powerful analytical tool that maximizes metabolic data yield from a single sample. This article has an associated First Person interview with the joint first authors of the paper.

BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy

Aging clocks dissociate biological from chronological age. The estimation of biological age is important for identifying gerontogenes and assessing environmental, nutritional, or therapeutic impacts on the aging process. Recently, methylation markers were shown to allow estimation of biological age based on age‐dependent somatic epigenetic alterations. However, DNA methylation is absent in some species such as Caenorhabditis elegans and it remains unclear whether and how the epigenetic clocks affect gene expression. Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. Here, we devised an approach that uses temporal scaling and binarization of C. elegans transcriptomes to define a gene set that predicts biological age with an accuracy that is close to the theoretical limit. Our model accurately predicts the longevity effects of diverse strains, treatments, and conditions. The involved genes support a role of specific transcription factors as well as innate immunity and neuronal signaling in the regulation of the aging process. We show that this binarized transcriptomic aging (BiT age) clock can also be applied to human age prediction with high accuracy. The BiT age clock could therefore find wide application in genetic, nutritional, environmental, and therapeutic interventions in the aging process.

Enhanced Healthspan in Caenorhabditis elegans Treated With Extracts From the Traditional Chinese Medicine Plants Cuscuta chinensis Lam. and Eucommia ulmoides Oliv

To uncover potential anti-aging capacities of Traditional Chinese Medicine (TCM), the nematode Caenorhabditis elegans was used to investigate the effects of Eucommia ulmoides and Cuscuta chinensis extracts, selected by screening seven TCM extracts, on different healthspan parameters. Nematodes exposed to E. ulmoides and C. chinensis extracts, starting at the young adult stage, exhibited prolonged lifespan and increased survival after heat stress as well as upon exposure to the pathogenic bacterium Photorhabdus luminescens, whereby the survival benefits were monitored after stress initiation at different adult stages. However, only C. chinensis had the ability to enhance physical fitness: the swimming behavior and the pharyngeal pumping rate of C. elegans were improved at day 7 and especially at day 12 of adulthood. Finally, monitoring the red fluorescence of aged worms revealed that only C. chinensis extracts caused suppression of intestinal autofluorescence, a known marker of aging. The results underline the different modes of action of the tested plants extracts. E. ulmoides improved specifically the physiological fitness by increasing the survival probability of C. elegans after stress, while C. chinensis seems to be an overall healthspan enhancer, reflected in the suppressed autofluorescence, with beneficial effects on physical as well as physiological fitness. The C. chinensis effects may be hormetic: this is supported by increased gene expression of hsp-16.1 and by trend, also of hsp-12.6.

Clavulanic Acid Production by Streptomyces clavuligerus: Insights from Systems Biology, Strain Engineering, and Downstream Processing

Clavulanic acid (CA) is an irreversible β-lactamase enzyme inhibitor with a weak antibacterial activity produced by Streptomyces clavuligerus (S. clavuligerus). CA is typically co-formulated with broad-spectrum β‑lactam antibiotics such as amoxicillin, conferring them high potential to treat diseases caused by bacteria that possess β‑lactam resistance. The clinical importance of CA and the complexity of the production process motivate improvements from an interdisciplinary standpoint by integrating metabolic engineering strategies and knowledge on metabolic and regulatory events through systems biology and multi-omics approaches. In the large-scale bioprocessing, optimization of culture conditions, bioreactor design, agitation regime, as well as advances in CA separation and purification are required to improve the cost structure associated to CA production. This review presents the recent insights in CA production by S. clavuligerus, emphasizing on systems biology approaches, strain engineering, and downstream processing.

Beyond Proteostasis: Lipid Metabolism as a New Player in ER Homeostasis

Biological membranes are not only essential barriers that separate cellular and subcellular structures, but also perform other critical functions such as the initiation and propagation of intra- and intercellular signals. Each membrane-delineated organelle has a tightly regulated and custom-made membrane lipid composition that is critical for its normal function. The endoplasmic reticulum (ER) consists of a dynamic membrane network that is required for the synthesis and modification of proteins and lipids. The accumulation of unfolded proteins in the ER lumen activates an adaptive stress response known as the unfolded protein response (UPR-ER). Interestingly, recent findings show that lipid perturbation is also a direct activator of the UPR-ER, independent of protein misfolding. Here, we review proteostasis-independent UPR-ER activation in the genetically tractable model organism Caenorhabditis elegans. We review the current knowledge on the membrane lipid composition of the ER, its impact on organelle function and UPR-ER activation, and its potential role in human metabolic diseases. Further, we summarize the bi-directional interplay between lipid metabolism and the UPR-ER. We discuss recent progress identifying the different respective mechanisms by which disturbed proteostasis and lipid bilayer stress activate the UPR-ER. Finally, we consider how genetic and metabolic disturbances may disrupt ER homeostasis and activate the UPR and discuss how using -omics-type analyses will lead to more comprehensive insights into these processes.

Using Co-Culture to Functionalize Clostridium Fermentation

Clostridium fermentations have been developed for producing butanol and other value-added chemicals, but their development is constrained by some limitations, such as relatively high substrate cost and the need to maintain an anaerobic condition. Recently, co-culture is emerging as a popular way to address these limitations by introducing a partner strain with Clostridium. Generally speaking, the co-culture strategy enables the use of a cheaper substrate, maintains the growth of Clostridium without any anaerobic treatment, improves product yields, and/or widens the product spectrum. Herein, we review recent developments of co-culture strategies involving Clostridium species according to their partner stains' functions with representative examples. We also discuss research challenges that need to be addressed for the future development of Clostridium co-cultures.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Network Simulations Reveal Molecular Signatures of Vulnerability to Age-Dependent Stress and Tau Accumulation

Alzheimer’s disease (AD) is the leading cause of dementia and one of the most common causes of death worldwide. As an age-dependent multifactorial disease, the causative triggers of AD are rooted in spontaneous declines in cellular function and metabolic capacity with increases in protein stressors such as the tau protein. This multitude of age-related processes that cause neurons to change from healthy states to ones vulnerable to the damage seen in AD are difficult to simultaneously investigate and even more difficult to quantify. Here we aimed to diminish these gaps in our understanding of neuronal vulnerability in AD development by using simulation methods to theoretically quantify an array of cellular stress responses and signaling molecules. This temporally-descriptive molecular signature was produced using a novel multimethod simulation approach pioneered by our laboratory for biological research; this methodology combines hierarchical agent-based processes and continuous equation-based modeling in the same interface, all while maintaining intrinsic distributions that emulate natural biological stochasticity. The molecular signature was validated for a normal organismal aging trajectory using experimental longitudinal data from Caenorhabditis elegans and rodent studies. In addition, we have further predicted this aging molecular signature for cells impacted by the pathogenic tau protein, giving rise to distinct stress response conditions needed for cytoprotective aging. Interestingly, our simulation experiments showed that oxidative stress signaling (via daf-16 and skn-1 activities) does not substantially protect cells from all the early stressors of aging, but that it is essential in preventing a late-life degenerative cellular phenotype. Together, our simulation experiments aid in elucidating neurodegenerative triggers in the onset of AD for different genetic conditions. The long-term goal of this work is to provide more detailed diagnostic and prognostic tools for AD development and progression, and to provide more comprehensive preventative measures for this disease.

Predicted functional interactome of Caenorhabditis elegans and a web tool for the functional interpretation of differentially expressed genes

BACKGROUND

The nematode worm, Caenorhabditis elegans, is a saprophytic species that has been emerging as a standard model organism since the early 1960s. This species is useful in numerous fields, including developmental biology, neurobiology, and ageing. A high-quality comprehensive molecular interaction network is needed to facilitate molecular mechanism studies in C. elegans.

RESULTS

We present the predicted functional interactome of Caenorhabditis elegans (FIC), which integrates functional association data from 10 public databases to infer functional gene interactions on diverse functional perspectives. In this work, FIC includes 108,550 putative functional associations with balanced sensitivity and specificity, which are expected to cover 21.42% of all C. elegans protein interactions, and 29.25% of these associations may represent protein interactions. Based on FIC, we developed a gene set linkage analysis (GSLA) web tool to interpret potential functional impacts from a set of differentially expressed genes observed in transcriptome analyses.

CONCLUSION

We present the predicted C. elegans interactome database FIC, which is a high-quality database of predicted functional interactions among genes. The functional interactions in FIC serve as a good reference interactome for GSLA to annotate differentially expressed genes for their potential functional impacts. In a case study, the FIC/GSLA system shows more comprehensive and concise annotations compared to other widely used gene set annotation tools, including PANTHER and DAVID. FIC and its associated GSLA are available at the website http://worm.biomedtzc.cn .

Changes in Metabolism and Proteostasis Drive Aging Phenotype in Aplysia californica Sensory Neurons

Aging is associated with cognitive declines that originate in impairments of function in the neurons that make up the nervous system. The marine mollusk Aplysia californica (Aplysia) is a premier model for the nervous system uniquely suited to investigation of neuronal aging due to uniquely identifiable neurons and molecular techniques available in this model. This study describes the molecular processes associated with aging in two populations of sensory neurons in Aplysia by applying RNA sequencing technology across the aging process (age 6-12 months). Differentially expressed genes clustered into four to five coherent expression patterns across the aging time series in the two neuron populations. Enrichment analysis of functional annotations in these neuron clusters revealed decreased expression of pathways involved in energy metabolism and neuronal signaling, suggesting that metabolic and signaling pathways are intertwined. Furthermore, increased expression of pathways involved in protein processing and translation suggests that proteostatic stress also occurs in aging. Temporal overlap of enrichment for energy metabolism, proteostasis, and neuronal function suggests that cognitive impairments observed in advanced age result from the ramifications of broad declines in energy metabolism.

Using Genome-Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data

Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.