Despite the prominence of laboratory rats in behavioral neuroscience and complex trait genetics, a significant gap exists in the functional rat genomics database that explains the regulation of gene expression at the genome level. To address this, we analyzed genome-wide Hi-C data from the frontal cortex of ten strains in the Hybrid Rat Diversity Panel. While originally generated to improve the rat genome assembly, these data provided a unique opportunity to characterize the regulatory landscape of the adult rat brain. We identified an average of 5,899 ± 1,997 (STD) loops per sample and integrated these with over 3 million curated CTCF binding sites, which serve as architectural anchors for chromatin loops. Our multi-stage filtering workflow identified 15,085 unique, high-confidence regulatory interactions throughout the genome. As a validation of our approach, we observed that genes with the highest loop counts, including Foxo1, Gja1, and Spry2, were significantly enriched for developmental processes, reflecting the role of 3D genome structure in maintaining adult neuronal identity. The resulting resource addresses a critical deficiency in rat functional genomics and provides a foundation for dissecting the genetic architecture of complex traits.

From molecular, cellular, to ecological systems, the modeling of biological processes often stands on the assumption that fast components immediately reach the equilibrium at each moment (quasi-steady state) and only slow components govern the relevant system dynamics. This quasi-steady state approximation (QSSA) simplifies the modeling but discards the effects of the relaxation towards each quasi-steady state. Unclear is the QSSA's suitability around the transition point, a specific condition where the system changes to a qualitatively different state. In this regard, we here derived a theoretical framework for the near-transition dynamics of biological systems, explicitly considering the relaxation processes overlooked by the QSSA. Numerical simulations verify our predictions for cellular decision-making, metabolic oscillations, and ecological cycles. Despite the extreme slowdown near the transition point, the QSSA alone misestimates the duration of the transition from one state to another. Moreover, the QSSA erroneously predicts the transition point itself for the onset of oscillations, while the relaxation dynamics facilitates or suppresses the oscillation onset with a counterintuitive time-delay effect. Common feedback interactions between biological components are pivotal to those relaxation effects. Our study provides an analytical foundation to understand the rich transient or rhythmic dynamics of interacting biological components near the transitions.

The three-dimensional conformation and packaging of chromosomes modulates the spatial organization of the nucleus, orchestrating DNA replication and repair, maintaining genome stability and integrity, and regulating gene expression. Hi-C methods provide high-resolution data on chromatin-to-chromatin interactions both within and among chromosomes at a genome-wide scale. Hi-C resolves topologically-associated domains (TADs) and chromatin loops that are linked to cell-specific transcriptional control. We present a comprehensive Hi-C dataset generated from the frontal cortex of laboratory rats, encompassing a diverse group of inbred strains (SHR/OlaIpcv, BN-Lx/Cub, BXH6/Cub, HXB2/Ipcv, HXB10/Ipcv, HXB23/Ipcv, HXB31/Ipcv, LE/Stm, F344/Stm) and an F1 hybrid (SHR/Olalpcv x BN/NHsdMcwi). This dataset serves as a valuable resource for studying the mechanisms by which three-dimensional chromatin architecture governs gene expression in the brain. Strain-specific variations in genome organization can illuminate the influence of chromatin structure on gene expression, neuronal functionality, and the predisposition to neurological and behavioral disorders.

The three-dimensional structure of chromosomes is integral to nuclear organization and influences processes such as DNA replication, repair, and gene expression. Hi-C methods generate high-resolution, genome-wide data that map physical interactions between different parts of the chromatin. These data have applications in de novo genome assembly, the detection of structural variants, and the analysis of gene regulatory mechanisms. This report describes a Hi-C dataset from the frontal cortex of laboratory rats. The dataset includes a diverse panel of inbred strains (SHR/OlaIpcv, BN-Lx/Cub, BXH6/Cub, HXB2/Ipcv, HXB10/Ipcv, HXB23/Ipcv, HXB31/Ipcv, LE/Stm, F344/Stm) and an F1 hybrid (SHR/Olalpcv × BN/NHsdMcwi). This data set provides a valuable resource for advancing rat genomics and offers opportunities for integration with other omic datasets generated using this widely used model organism.

Large scale human genome wide association studies (GWAS) have identified a growing pool of genes associated with cigarette smoking. One of the most prominent, phosphodiesterase-4B (PDE4B), has been associated with multiple smoking phenotypes. Although PDE4B modulates the half-life of neuronal cAMP, its precise role in smoking behaviors is unknown. To address this knowledge gap, we used a reverse translational approach. We inactivated PDE4B in bilateral medial nucleus accumbens shell (NAcs) neurons by injecting AAV containing a specific gRNA in female transgenic Cas9+ Long Evans rats. These rats then were given 23-h chronic access to nicotine intravenous self-administration (IVSA) under a schedule of increasing fixed ratios (FR). With the increased effort required at FR7, nicotine SA (i.e. active presses and drug infusions) declined significantly in controls, whereas it was maintained in the mutagenized group. A progressive ratio (PR) study also showed significantly greater cumulative nicotine infusions in the PDE4B-edited group. Hence, we hypothesized that enhanced PDE4B protein activity would reduce nicotine IVSA. A positive allosteric modulator, 2-(3-(4-chloro-3-fluorophenyl)-5-ethyl-1H-1,2,4-triazol-1-yl)-N-(3,5-dichlorobenzyl)acetamide (MR-L2), was microinfused into NAcs bilaterally at FR3 or FR5; in both cohorts, MR-L2 acutely reduced nicotine IVSA. In summary, these studies show that the activity of PDE4B regulates the capacity of NAcs to maintain nicotine IVSA in face of the cost of increasing work. This finding and the results of the PR study indicate that PDE4B affects the motivation to obtain nicotine. These reverse translational studies in rats provide insight into the motivational effects of NAcs PDE4B that advance our understanding of the smoking behaviors mapped in human GWAS.

The seventh iteration of the reference genome assembly for Rattus norvegicus—mRatBN7.2—corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

The century-long Michaelis-Menten rate law and its modifications in the modeling of biochemical rate processes stand on the assumption that the concentration of the complex of interacting molecules, at each moment, rapidly approaches an equilibrium (quasi-steady state) compared to the pace of molecular concentration changes. Yet, in the case of actively time-varying molecular concentrations with transient or oscillatory dynamics, the deviation of the complex profile from the quasi-steady state becomes relevant. A recent theoretical approach, known as the effective time-delay scheme (ETS), suggests that the delay from the relaxation time of molecular complex formation contributes to the substantial breakdown of the quasi-steady state assumption. Here, we systematically expand this ETS and inquire into the comprehensive roles of relaxation dynamics in complex formation. Through the modeling of rhythmic protein-protein and protein-DNA interactions and the mammalian circadian clock, our analysis reveals the effect of the relaxation dynamics beyond the time delay, which extends to the dampening of changes in the complex concentration with a reduction in the oscillation amplitude compared to the quasi-steady state. Interestingly, the combined effect of the time delay and amplitude reduction shapes both qualitative and quantitative oscillatory patterns such as the emergence and variability of the mammalian circadian rhythms. These findings highlight the downside of the routine assumption of quasi-steady states and enhance the mechanistic understanding of rich time-varying biomolecular processes.

The Michaelis–Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein–protein and protein–DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

Large scale human genome wide association studies (GWAS) have identified a growing pool of genes associated with cigarette smoking. One of the most prominent, phosphodiesterase-4B (PDE4B), has been associated with multiple smoking phenotypes. Although PDE4B modulates the half-life of neuronal cAMP, its precise role in smoking behaviors is unknown. To address this knowledge gap, we used a reverse translational approach. We inactivated PDE4B in bilateral medial nucleus accumbens shell (NAcs) neurons by injecting AAV containing a specific gRNA in female transgenic Cas9+ Long Evans rats. These rats then were given 23-hour chronic access to nicotine intravenous self-administration (IVSA) under a schedule of increasing fixed ratios (FR). With the increased effort required at FR7, nicotine SA (i.e. active presses and drug infusions) declined significantly in controls, whereas it was maintained in the mutagenized group. A progressive ratio (PR) study also showed significantly greater cumulative nicotine infusions in the mutant group. Hence, we hypothesized that enhanced PDE4B protein activity would reduce nicotine IVSA. A positive allosteric modulator,2-(3-(4-chloro-3-fluorophenyl)-5-ethyl-1H-1,2,4-triazol-1-yl)-N-(3,5-dichlorobenzyl)acetamide (MR-L2), was microinfused into NAcs bilaterally at FR3 or FR5; in both cohorts, MR-L2 acutely reduced nicotine IVSA. In summary, these studies show that the activity of PDE4B regulates the capacity of NAcs to maintain nicotine IVSA in face of the cost of increasing work. This finding and the results of the PR study indicate that PDE4B affects the motivation to obtain nicotine. These reverse translational studies in rats provide insight into the motivational effects of NAcs PDE4B that advance our understanding of the smoking behaviors mapped in human GWAS.

Our backward simulation (BS) is an approach to infer the dynamics of individual components in ordinary differential equation (ODE) models, given the information on relatively downstream components or their sums. Here, we demonstrate the use of BS to infer protein synthesis rates with a given profile of protein concentrations over time in a circadian system. This protocol can also be applied to a wide range of problems with undetermined dynamics at the upstream levels. For complete details on the use and execution of this protocol, please refer to Lim et al. (2021).

The WMI and WLI inbred rats were generated from the stress-prone, and not yet fully inbred, Wistar Kyoto (WKY) strain. These were selected using bi-directional selection for immobility in the forced swim test and were then sib-mated for over 38 generations. Despite the low level of genetic diversity among WKY progenitors, the WMI substrain is significantly more vulnerable to stress relative to the counter-selected WLI strain. Here we quantify numbers and classes of genomic sequence variants distinguishing these substrains with the long term goal of uncovering functional and behavioral polymorphism that modulate sensitivity to stress and depression-like phenotypes. DNA from WLI and WMI was sequenced using Illumina xTen, IonTorrent, and 10X Chromium linked-read platforms to obtain a combined coverage of ~ 100X for each strain. We identified 4,296 high quality homozygous SNPs and indels between the WMI and WLI. We detected high impact variants in genes previously implicated in depression (e.g. Gnat2 ), depression-like behavior (e.g. Prlr , Nlrp1a ), other psychiatric disease (e.g. Pou6f2 , Kdm5a , Reep3 , Wdfy3 ), and responses to psychological stressors (e.g. Pigr ). High coverage sequencing data confirm that the two substrains are nearly coisogenic. Nonetheless, the small number of sequence variants contributes to numerous well characterized differences including depression-like behavior, stress reactivity, and addiction related phenotypes. These selected substrains are an ideal resource for forward and reverse genetic studies using a reduced complexity cross.

Circadian protein oscillations are maintained by the lifelong repetition of protein production and degradation in daily balance. It comes at the cost of ever-replayed, futile protein synthesis each day. This biosynthetic cost with a given oscillatory protein profile is relievable by a rhythmic, not constant, degradation rate that selectively peaks at the right time of day but remains low elsewhere, saving much of the gross protein loss and of the replenishing protein synthesis. Here, our mathematical modeling reveals that the rhythmic degradation rate of proteins with circadian production spontaneously emerges under steady and limited activity of proteolytic mediators and does not necessarily require rhythmic post-translational regulation of previous focus. Additional (yet steady) post-translational modifications in a proteolytic pathway can further facilitate the degradation's rhythmicity in favor of the biosynthetic cost saving. Our work is supported by animal and plant circadian data, offering a generic mechanism for potentially widespread, time-dependent protein turnover.

The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.

Circadian clocks play a pivotal role in orchestrating numerous physiological and developmental events. Waveform shapes of the oscillations of protein abundances can be informative about the underlying biochemical processes of circadian clocks. We derive a mathematical framework where waveforms do reveal hidden biochemical mechanisms of circadian timekeeping. We find that the cost of synthesizing proteins with particular waveforms can be substantially reduced by rhythmic protein half-lives over time, as supported by previous plant and mammalian data, as well as our own seedling experiment. We also find that previously enigmatic, cyclic expression of positive arm components within the mammalian and insect clocks allows both a broad range of peak time differences between protein waveforms and the symmetries of the waveforms about the peak times. Such various peak-time differences may facilitate tissue-specific or developmental stage-specific multicellular processes. Our waveform-guided approach can be extended to various biological oscillators, including cell-cycle and synthetic genetic oscillators.

Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods' nutrient compositions, the recent concept of nutritional fitness helps quantify a nutrient balance within each food with regard to satisfying daily nutritional requirements. Nutritional fitness offers prioritisation of recommended foods using the foods' occurrence in nutritionally adequate food combinations. Here, we systematically identify nutritionally recommendable foods for semi- to strict vegetarian diets through the computation of nutritional fitness. Along with commonly recommendable foods across different diets, our analysis reveals favourable foods specific to each diet, such as immature lima beans for a vegan diet as an amino acid and choline source, and mushrooms for ovo-lacto vegetarian and vegan diets as a vitamin D source. Furthermore, we find that selenium and other essential micronutrients can be subject to deficiency in plant-based diets, and suggest nutritionally-desirable dietary patterns. We extend our analysis to two hypothetical scenarios of highly personalised, plant-based methionine-restricted diets. Our nutrient-profiling approach may provide a useful guide for designing different types of personalised vegetarian diets.

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

A wide range of organisms features molecular machines, circadian clocks, which generate endogenous oscillations with ~24 h periodicity and thereby synchronize biological processes to diurnal environmental fluctuations. Recently, it has become clear that plants harbor more complex gene regulatory circuits within the core circadian clocks than other organisms, inspiring a fundamental question: are all these regulatory interactions between clock genes equally crucial for the establishment and maintenance of circadian rhythms? Our mechanistic simulation for Arabidopsis thaliana demonstrates that at least half of the total regulatory interactions must be present to express the circadian molecular profiles observed in wild-type plants. A set of those essential interactions is called herein a kernel of the circadian system. The kernel structure unbiasedly reveals four interlocked negative feedback loops contributing to circadian rhythms, and three feedback loops among them drive the autonomous oscillation itself. Strikingly, the kernel structure, as well as the whole clock circuitry, is overwhelmingly composed of inhibitory, rather than activating, interactions between genes. We found that this tendency underlies plant circadian molecular profiles which often exhibit sharply-shaped, cuspidate waveforms. Through the generation of these cuspidate profiles, inhibitory interactions may facilitate the global coordination of temporally-distant clock events that are markedly peaked at very specific times of day. Our systematic approach resulting in experimentally-testable predictions provides insights into a design principle of biological clockwork, with implications for synthetic biology.

Genetic studies using model organisms have shown that many long-lived mutants display impaired fitness, such as reduced fecundity and delayed development. However, in several wild animals, the association between longevity and fitness does not seem to be inevitable. Thus, the relationship between longevity and fitness in wild organisms remains inconclusive. Here, we determined the correlation between lifespan and fitness, developmental rate and brood size, by using 16 wild-derived C. elegans strains originated from various geographic areas. We found a negative correlation between lifespan and developmental rate. In contrast, we did not find such negative correlation between longevity and developmental rate among the individuals of C. elegans strains. These data imply that polymorphic genetic variants among wild isolates determine resource allocation to longevity and developmental rate.

ABSTRACT Bacterial persisters are a small fraction of quiescent cells that survive in the presence of lethal concentrations of antibiotics. They can regrow to give rise to a new population that has the same vulnerability to the antibiotics as did the parental population. Although formation of bacterial persisters in the presence of various antibiotics has been documented, the molecular mechanisms by which these persisters tolerate the antibiotics are still controversial. We found that amplification of the fumarate reductase operon ( FRD ) in Escherichia coli led to a higher frequency of persister formation. The persister frequency of E. coli was increased when the cells contained elevated levels of intracellular fumarate. Genetic perturbations of the electron transport chain (ETC), a metabolite supplementation assay, and even the toxin-antitoxin-related hipA7 mutation indicated that surplus fumarate markedly elevated the E. coli persister frequency. An E. coli strain lacking succinate dehydrogenase ( SDH ), thereby showing a lower intracellular fumarate concentration, was killed ∼1,000-fold more effectively than the wild-type strain in the stationary phase. It appears that SDH and FRD represent a paired system that gives rise to and maintains E. coli persisters by producing and utilizing fumarate, respectively.

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework – or metabolic model – on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.