Tryptophan metabolism is a physiological integrator regulating circadian rhythms.

OBJECTIVE: The circadian clock aligns physiology with the 24-hour rotation of Earth. Light and food are the main environmental cues (zeitgebers) regulating circadian rhythms in mammals. Yet, little is known about the interaction between specific dietary components and light in coordinating circadian homeostasis. Herein, we focused on the role of essential amino acids. METHODS: Mice were fed diets depleted of specific essential amino acids and their behavioral rhythms were monitored and tryptophan was selected for downstream analyses. The role of tryptophan metabolism in modulating circadian homeostasis was studied using isotope tracing as well as transcriptomic- and metabolomic- analyses. RESULTS: Dietary tryptophan depletion alters behavioral rhythms in mice. Furthermore, tryptophan metabolism was shown to be regulated in a time- and light- dependent manner. A multi-omics approach and combinatory diet/light interventions demonstrated that tryptophan metabolism modulates temporal regulation of metabolism and transcription programs by buffering photic cues. Specifically, tryptophan metabolites regulate central circadian functions of the suprachiasmatic nucleus and the core clock machinery in the liver. CONCLUSIONS: Tryptophan metabolism is a modulator of circadian homeostasis by integrating environmental cues. Our findings propose tryptophan metabolism as a potential point for pharmacologic intervention to modulate phenotypes associated with disrupted circadian rhythms.

Correlation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks.

Summary: Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions. Availability and implementation: The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism.

OBJECTIVE: Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce. METHODS: We present here a fully unsupervised and versatile correlation-based method - termed Correlation guided Network Integration (CoNI) - to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways. RESULTS: By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). CONCLUSION: Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.

Disentangling the aging gene expression network of termite queens.

BACKGROUND: Most insects are relatively short-lived, with a maximum lifespan of a few weeks, like the aging model organism, the fruit-fly Drosophila melanogaster. By contrast, the queens of many social insects (termites, ants and some bees) can live from a few years to decades. This makes social insects promising models in aging research providing insights into how a long reproductive life can be achieved. Yet, aging studies on social insect reproductives are hampered by a lack of quantitative data on age-dependent survival and time series analyses that cover the whole lifespan of such long-lived individuals. We studied aging in queens of the drywood termite Cryptotermes secundus by determining survival probabilities over a period of 15 years and performed transcriptome analyses for queens of known age that covered their whole lifespan. RESULTS: The maximum lifespan of C. secundus queens was 13 years, with a median maximum longevity of 11.0 years. Time course and co-expression network analyses of gene expression patterns over time indicated a non-gradual aging pattern. It was characterized by networks of genes that became differentially expressed only late in life, namely after ten years, which associates well with the median maximum lifespan for queens. These old-age gene networks reflect processes of physiological upheaval. We detected strong signs of stress, decline, defense and repair at the transcriptional level of epigenetic control as well as at the post-transcriptional level with changes in transposable element activity and the proteostasis network. The latter depicts an upregulation of protein degradation, together with protein synthesis and protein folding, processes which are often down-regulated in old animals. The simultaneous upregulation of protein synthesis and autophagy is indicative of a stress-response mediated by the transcription factor cnc, a homolog of human nrf genes. CONCLUSIONS: Our results show non-linear senescence with a rather sudden physiological upheaval at old-age. Most importantly, they point to a re-wiring in the proteostasis network and stress as part of the aging process of social insect queens, shortly before queens die.

Combination therapies induce cancer cell death through the integrated stress response and disturbed pyrimidine metabolism.

By accentuating drug efficacy and impeding resistance mechanisms, combinatorial, multi-agent therapies have emerged as key approaches in the treatment of complex diseases, most notably cancer. Using high-throughput drug screens, we uncovered distinct metabolic vulnerabilities and thereby identified drug combinations synergistically causing a starvation-like lethal catabolic response in tumor cells from different cancer entities. Domperidone, a dopamine receptor antagonist, as well as several tricyclic antidepressants (TCAs), including imipramine, induced cancer cell death in combination with the mitochondrial uncoupler niclosamide ethanolamine (NEN) through activation of the integrated stress response pathway and the catabolic CLEAR network. Using transcriptome and metabolome analyses, we characterized a combinatorial response, mainly driven by the transcription factors CHOP and TFE3, which resulted in cell death through enhanced pyrimidine catabolism as well as reduced pyrimidine synthesis. Remarkably, the drug combinations sensitized human organoid cultures to the standard-of-care chemotherapy paclitaxel. Thus, our combinatorial approach could be clinically implemented into established treatment regimen, which would be further facilitated by the advantages of drug repurposing.

Comparative transcriptomic analysis of the mechanisms underpinning ageing and fecundity in social insects.

The exceptional longevity of social insect queens despite their lifelong high fecundity remains poorly understood in ageing biology. To gain insights into the mechanisms that might underlie ageing in social insects, we compared gene expression patterns between young and old castes (both queens and workers) across different lineages of social insects (two termite, two bee and two ant species). After global analyses, we paid particular attention to genes of the insulin/insulin-like growth factor 1 signalling (IIS)/target of rapamycin (TOR)/juvenile hormone (JH) network, which is well known to regulate lifespan and the trade-off between reproduction and somatic maintenance in solitary insects. Our results reveal a major role of the downstream components and target genes of this network (e.g. JH signalling, vitellogenins, major royal jelly proteins and immune genes) in affecting ageing and the caste-specific physiology of social insects, but an apparently lesser role of the upstream IIS/TOR signalling components. Together with a growing appreciation of the importance of such downstream targets, this leads us to propose the TI-J-LiFe (TOR/IIS-JH-Lifespan and Fecundity) network as a conceptual framework for understanding the mechanisms of ageing and fecundity in social insects and beyond. This article is part of the theme issue 'Ageing and sociality: why, when and how does sociality change ageing patterns?'

Long live the queen, the king and the commoner? Transcript expression differences between old and young in the termite Cryptotermes secundus.

Social insects provide promising new avenues for aging research. Within a colony, individuals that share the same genetic background can differ in lifespan by up to two orders of magnitude. Reproducing queens (and in termites also kings) can live for more than 20 years, extraordinary lifespans for insects. We studied aging in a termite species, Cryptotermes secundus, which lives in less socially complex societies with a few hundred colony members. Reproductives develop from workers which are totipotent immatures. Comparing transcriptomes of young and old individuals, we found evidence for aging in reproductives that was especially associated with DNA and protein damage and the activity of transposable elements. By contrast, workers seemed to be better protected against aging. Thus our results differed from those obtained for social insects that live in more complex societies. Yet, they are in agreement with lifespan estimates for the study species. Our data are also in line with expectations from evolutionary theory. For individuals that are able to reproduce, it predicts that aging should only start after reaching maturity. As C. secundus workers are immatures with full reproductive options we expect them to invest into anti-aging processes. Our study illustrates that the degree of aging can differ between social insects and that it may be associated with caste-specific opportunities for reproduction.

Estimating genetic kin relationships in prehistoric populations.

Archaeogenomic research has proven to be a valuable tool to trace migrations of historic and prehistoric individuals and groups, whereas relationships within a group or burial site have not been investigated to a large extent. Knowing the genetic kinship of historic and prehistoric individuals would give important insights into social structures of ancient and historic cultures. Most archaeogenetic research concerning kinship has been restricted to uniparental markers, while studies using genome-wide information were mainly focused on comparisons between populations. Applications which infer the degree of relationship based on modern-day DNA information typically require diploid genotype data. Low concentration of endogenous DNA, fragmentation and other post-mortem damage to ancient DNA (aDNA) makes the application of such tools unfeasible for most archaeological samples. To infer family relationships for degraded samples, we developed the software READ (Relationship Estimation from Ancient DNA). We show that our heuristic approach can successfully infer up to second degree relationships with as little as 0.1x shotgun coverage per genome for pairs of individuals. We uncover previously unknown relationships among prehistoric individuals by applying READ to published aDNA data from several human remains excavated from different cultural contexts. In particular, we find a group of five closely related males from the same Corded Ware culture site in modern-day Germany, suggesting patrilocality, which highlights the possibility to uncover social structures of ancient populations by applying READ to genome-wide aDNA data. READ is publicly available from https://bitbucket.org/tguenther/read.