Fibroblast growth factor 21 enhances learning and memory performance in mice by regulating hippocampal L-lactate homeostasis.

Fibroblast growth factor 21 (FGF21) is one endogenous metabolic molecule that functions as a regulator in glucose and lipid homeostasis. However, the effect of FGF21 on L-lactate homeostasis and its mechanism remains unclear until now. Forty-five Six-week-old male C57BL/6 mice were divided into three groups: control, L-lactate, and FGF21 (1.5 mg/kg) groups. At the end of the treatment, nuclear magnetic resonance-based metabolomics, and key proteins related to L-lactate homeostasis were determined respectively to evaluate the efficacy of FGF21 and its mechanisms. The results showed that, compared to the vehicle group, the L-lactate-treated mice displayed learning and memory performance impairments, as well as reduced hippocampal ATP and NADH levels, but increased oxidative stress, mitochondrial dysfunction, and apoptosis, which suggesting inhibited L-lactate-pyruvate conversion in the brain. Conversely, FGF21 treatment ameliorated the L-lactate accumulation state, accompanied by restoration of the learning and memory defects, indicating enhanced L-lactate uptake and utilization in hippocampal neurons. We demonstrated that maintaining constant L-lactate-pyruvate flux is essential for preserving neuronal bioenergetic and redox levels. FGF21 contributed to preparing the brain for situations of high availability of L-lactate, thus preventing neuronal vulnerability in metabolic reprogramming.

Fibroblast growth factor 21 protects the liver from apoptosis in a type 1 diabetes mouse model via regulating L-lactate homeostasis.

AIMS/HYPOTHESIS: Fibroblast growth factor 21 (FGF21) is a hepatokine with pleiotropic effects on glucose and lipid metabolic homeostasis. Here, we aimed to elucidate the mechanisms underlying the protective effects of FGF21 on L-lactate homeostasis and liver lesions in a type 1 diabetes mellitus (T1DM) mice model. METHODS: Six-week-old male C57BL/6 mice were divided into control, T1DM, and FGF21 groups. We also examined hepatic apoptotic signaling and functional indices in wild-type and hydroxycarboxylic acid receptor 1 (HCA1) knockout mice with T1DM or long-term L-lactate exposure. After preincubation of high glucose- or L-lactate treated hepatic AML12 cells, L-lactate uptake, apoptosis, and monocarboxylic acid transporter 2 (MCT2) expression were investigated. RESULTS: In a mouse model of T1DM, hepatic FGF21 expression was downregulated by approximately 1.5-fold at 13 weeks after the hyperglycemic insult. In vivo administration of exogenous FGF21 (2 mg/kg) to diabetic or L-lactate-infused mice significantly prevented hepatic oxidative stress and apoptosis by activating extracellular signal-regulated kinase (ERK)1/2, p38 mitogen-activated protein kinase (MAPK) and AMP-activated protein kinase (AMPK) pathways. HCA1-KO mice were less susceptible to diabetes- and L-lactate-induced hepatic apoptosis and dysfunction. In addition, inhibition of PI3K-mTOR activity revealed that FGF21 prevented L-lactate-induced Cori cycle alterations and hepatic apoptosis by upregulating MCT2 protein translation. CONCLUSIONS/INTERPRETATION: These results demonstrate that L-lactate homeostasis may be a therapeutic target for T1DM-related hepatic dysfunction. The protective effects of FGF21 on hepatic damage were associated with its ability to ameliorate MCT2-dependent Cori cycle alterations and prevent HCA1-mediated inhibition of ERK1/2, p38 MAPK, and AMPK signaling.

C1632 suppresses the migration and proliferation of non-small-cell lung cancer cells involving LIN28 and FGFR1 pathway.

Chemoresistance and migration represent major obstacles in the therapy of non-small-cell lung cancer (NSCLC), which accounts for approximately 85% of lung cancer patients in clinic. In the present study, we report that the compound C1632 is preferentially distributed in the lung after oral administration in vivo with high bioavailability and limited inhibitory effects on CYP450 isoenzymes. We found that C1632 could simultaneously inhibit the expression of LIN28 and block FGFR1 signalling transduction in NSCLC A549 and A549R cells, resulting in significant decreases in the phosphorylation of focal adhesion kinase and the expression of matrix metalloproteinase-9. Consequently, C1632 effectively inhibited the migration and invasion of A549 and A549R cells. Meanwhile, C1632 significantly suppressed the cell viability and the colony formation of A549 and A549R cells by inhibiting DNA replication and inducing G0/G1 cell cycle arrest. Interestingly, compared with A549 cells, C1632 possesses the same or even better anti-migration and anti-proliferation effects on A549R cells, regardless of drug resistance. In addition, C1632 also displayed the capacity to inhibit the growth of A549R xenograft tumours in mice. Altogether, these findings reveal the potential of C1632 as a promising anti-NSCLC agent, especially for chemotherapy-resistant NSCLC treatment.

Cerebral Organoids Derived from a Parkinson's Patient Exhibit Unique Pathogenesis from Chikungunya Virus Infection When Compared to a Non-Parkinson's Patient.

(1) Background: Arboviruses of medical and veterinary significance have been identified on all seven continents, with every human and animal population at risk for exposure. Like arboviruses, chronic neurodegenerative diseases, like Alzheimer's and Parkinson's disease, are found wherever there are humans. Significant differences in baseline gene and protein expression have been determined between human-induced pluripotent stem cell lines derived from non-Parkinson's disease individuals and from individuals with Parkinson's disease. It was hypothesized that these inherent differences could impact cerebral organoid responses to viral infection. (2) Methods: In this study, cerebral organoids from a non-Parkinson's and Parkinson's patient were infected with Chikungunya virus and observed for two weeks. (3) Results: Parkinson's organoids lost mass and exhibited a differential antiviral response different from non-Parkinson's organoids. Neurotransmission data from both infected non-Parkinson's and Parkinson's organoids had dysregulation of IL-1, IL-10, and IL-6. These cytokines are associated with mood and could be contributing to persistent depression seen in patients following CHIKV infection. Both organoid types had increased expression of CXCL10, which is linked to demyelination. (4) Conclusions: The differential antiviral response of Parkinson's organoids compared with non-Parkinson's organoids highlights the need for more research in neurotropic infections in a neurologically compromised host.

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility.

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators, and role in key physiological response.

The ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation.

OxyR Is a Convergent Target for Mutations Acquired during Adaptation to Oxidative Stress-Prone Metabolic States.

Oxidative stress is concomitant with aerobic metabolism. Thus, bacterial genomes encode elaborate mechanisms to achieve redox homeostasis. Here we report that the peroxide-sensing transcription factor, oxyR, is a common mutational target using bacterial species belonging to two genera, Escherichia coli and Vibrio natriegens, in separate growth conditions implemented during laboratory evolution. The mutations clustered in the redox active site, dimer interface, and flexible redox loop of the protein. These mutations favor the oxidized conformation of OxyR that results in constitutive expression of the genes it regulates. Independent component analysis of the transcriptome revealed that the constitutive activity of OxyR reduces DNA damage from reactive oxygen species, as inferred from the activity of the SOS response regulator LexA. This adaptation to peroxide stress came at a cost of lower growth, as revealed by calculations of proteome allocation using genome-scale models of metabolism and macromolecular expression. Further, identification of similar sequence changes in natural isolates of E. coli indicates that adaptation to oxidative stress through genetic changes in oxyR can be a common occurrence.

Adaptive laboratory evolution of Escherichia coli under acid stress.

The ability of Escherichia coli to tolerate acid stress is important for its survival and colonization in the human digestive tract. Here, we performed adaptive laboratory evolution of the laboratory strain E. coli K-12 MG1655 at pH 5.5 in glucose minimal medium. After 800 generations, six independent populations under evolution had reached 18.0 % higher growth rates than their starting strain at pH 5.5, while maintaining comparable growth rates to the starting strain at pH 7. We characterized the evolved strains and found that: (1) whole genome sequencing of isolated clones from each evolved population revealed mutations in rpoC appearing in five of six sequenced clones; and (2) gene expression profiles revealed different strategies to mitigate acid stress, which are related to amino acid metabolism and energy production and conversion. Thus, a combination of adaptive laboratory evolution, genome resequencing and expression profiling revealed, on a genome scale, the strategies that E. coli uses to mitigate acid stress.

The Escherichia coli transcriptome mostly consists of independently regulated modules.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Adaptive evolution reveals a tradeoff between growth rate and oxidative stress during naphthoquinone-based aerobic respiration.

Evolution fine-tunes biological pathways to achieve a robust cellular physiology. Two and a half billion years ago, rapidly rising levels of oxygen as a byproduct of blooming cyanobacterial photosynthesis resulted in a redox upshift in microbial energetics. The appearance of higher-redox-potential respiratory quinone, ubiquinone (UQ), is believed to be an adaptive response to this environmental transition. However, the majority of bacterial species are still dependent on the ancient respiratory quinone, naphthoquinone (NQ). Gammaproteobacteria can biosynthesize both of these respiratory quinones, where UQ has been associated with aerobic lifestyle and NQ with anaerobic lifestyle. We engineered an obligate NQ-dependent γ-proteobacterium, Escherichia coli ΔubiC, and performed adaptive laboratory evolution to understand the selection against the use of NQ in an oxic environment and also the adaptation required to support the NQ-driven aerobic electron transport chain. A comparative systems-level analysis of pre- and postevolved NQ-dependent strains revealed a clear shift from fermentative to oxidative metabolism enabled by higher periplasmic superoxide defense. This metabolic shift was driven by the concerted activity of 3 transcriptional regulators (PdhR, RpoS, and Fur). Analysis of these findings using a genome-scale model suggested that resource allocation to reactive oxygen species (ROS) mitigation results in lower growth rates. These results provide a direct elucidation of a resource allocation tradeoff between growth rate and ROS mitigation costs associated with NQ usage under oxygen-replete condition.

Pseudogene repair driven by selection pressure applied in experimental evolution.

Pseudogenes represent open reading frames that have been damaged by mutations, rendering the gene product non-functional. Pseudogenes are found in many genomes and are not always eliminated, even if they are potentially 'wasteful'. This raises a fundamental question about their prevalence. Here we report pseudogene efeU repair that restores the iron uptake system of Escherichia coli under a designed selection pressure during adaptive laboratory evolution.

Evolution of gene knockout strains of E. coli reveal regulatory architectures governed by metabolism.

Biological regulatory network architectures are multi-scale in their function and can adaptively acquire new functions. Gene knockout (KO) experiments provide an established experimental approach not just for studying gene function, but also for unraveling regulatory networks in which a gene and its gene product are involved. Here we study the regulatory architecture of Escherichia coli K-12 MG1655 by applying adaptive laboratory evolution (ALE) to metabolic gene KO strains. Multi-omic analysis reveal a common overall schema describing the process of adaptation whereby perturbations in metabolite concentrations lead regulatory networks to produce suboptimal states, whose function is subsequently altered and re-optimized through acquisition of mutations during ALE. These results indicate that metabolite levels, through metabolite-transcription factor interactions, have a dominant role in determining the function of a multi-scale regulatory architecture that has been molded by evolution.

Growth Adaptation of gnd and sdhCB Escherichia coli Deletion Strains Diverges From a Similar Initial Perturbation of the Transcriptome.

Adaptive laboratory evolution (ALE) has emerged as a new approach with which to pursue fundamental biological inquiries and, in particular, new insights into the systemic function of a gene product. Two E. coli knockout strains were constructed: one that blocked the Pentose Phosphate Pathway (gnd KO) and one that decoupled the TCA cycle from electron transport (sdhCDAB KO). Despite major perturbations in central metabolism, minimal growth rate changes were found in the two knockout strains. More surprisingly, many similarities were found in their initial transcriptomic states that could be traced to similarly perturbed metabolites despite the differences in the network location of the gene perturbations and concomitant re-routing of pathway fluxes around these perturbations. However, following ALE, distinct metabolomic and transcriptomic states were realized. These included divergent flux and gene expression profiles in the gnd and sdhCDAB KOs to overcome imbalances in NADPH production and nitrogen/sulfur assimilation, respectively, that were not obvious limitations of growth in the unevolved knockouts. Therefore, this work demonstrates that ALE provides a productive approach to reveal novel insights of gene function at a systems level that cannot be found by observing the fresh knockout alone.

Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances in Escherichia coli pgi Knockout Evolutions.

A mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory functions of the lost gene. The pgi gene, whose product catalyzes the second step in glycolysis, was deleted in a growth-optimized Escherichia coli K-12 MG1655 strain. The initial knockout (KO) strain exhibited an 80% drop in growth rate that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi-omic data sets showed that the loss of pgi substantially shifted pathway usage, leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.IMPORTANCE A mechanistic understanding of how microbes are able to overcome the loss of a gene through regulatory and metabolic changes is not well understood. Eight independent adaptive laboratory evolution (ALE) experiments with pgi knockout strains resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after the loss of a major gene product were revealed.

Adaptive laboratory evolution resolves energy depletion to maintain high aromatic metabolite phenotypes in Escherichia coli strains lacking the Phosphotransferase System.

Aromatic metabolites provide the backbone for numerous industrial and pharmaceutical compounds of high value. The Phosphotransferase System (PTS) is common to many bacteria, and is the primary mechanism for glucose uptake by Escherichia coli. The PTS was removed to conserve phosphoenolpyruvate (pep), which is a precursor for aromatic metabolites and consumed by the PTS, for aromatic metabolite production. Replicate adaptive laboratory evolution (ALE) of PTS and detailed omics data sets collected revealed that the PTS bridged the gap between respiration and fermentation, leading to distinct high fermentative and high respiratory rate phenotypes. It was also found that while all strains retained high levels of aromatic amino acid (AAA) biosynthetic precursors, only one replicate from the high glycolytic clade retained high levels of intracellular AAAs. The fast growth and high AAA precursor phenotypes could provide a starting host for cell factories targeting the overproduction aromatic metabolites.

Adaptation to the coupling of glycolysis to toxic methylglyoxal production in tpiA deletion strains of Escherichia coli requires synchronized and counterintuitive genetic changes.

Methylglyoxal is a highly toxic metabolite that can be produced in all living organisms. Methylglyoxal was artificially elevated by removal of the tpiA gene from a growth optimized Escherichia coli strain. The initial response to elevated methylglyoxal and its toxicity was characterized, and detoxification mechanisms were studied using adaptive laboratory evolution. We found that: 1) Multi-omics analysis revealed biological consequences of methylglyoxal toxicity, which included attack on macromolecules including DNA and RNA and perturbation of nucleotide levels; 2) Counter-intuitive cross-talk between carbon starvation and inorganic phosphate signalling was revealed in the tpiA deletion strain that required mutations in inorganic phosphate signalling mechanisms to alleviate; and 3) The split flux through lower glycolysis depleted glycolytic intermediates requiring a host of synchronized and coordinated mutations in non-intuitive network locations in order to re-adjust the metabolic flux map to achieve optimal growth. Such mutations included a systematic inactivation of the Phosphotransferase System (PTS) and alterations in cell wall biosynthesis enzyme activity. This study demonstrated that deletion of major metabolic genes followed by ALE was a productive approach to gain novel insight into the systems biology underlying optimal phenotypic states.

Modeling Method for Increased Precision and Scope of Directly Measurable Fluxes at a Genome-Scale.

Metabolic flux analysis (MFA) is considered to be the gold standard for determining the intracellular flux distribution of biological systems. The majority of work using MFA has been limited to core models of metabolism due to challenges in implementing genome-scale MFA and the undesirable trade-off between increased scope and decreased precision in flux estimations. This work presents a tunable workflow for expanding the scope of MFA to the genome-scale without trade-offs in flux precision. The genome-scale MFA model presented here, iDM2014, accounts for 537 net reactions, which includes the core pathways of traditional MFA models and also covers the additional pathways of purine, pyrimidine, isoprenoid, methionine, riboflavin, coenzyme A, and folate, as well as other biosynthetic pathways. When evaluating the iDM2014 using a set of measured intracellular intermediate and cofactor mass isotopomer distributions (MIDs),1 it was found that a total of 232 net fluxes of central and peripheral metabolism could be resolved in the E. coli network. The increase in scope was shown to cover the full biosynthetic route to an expanded set of bioproduction pathways, which should facilitate applications such as the design of more complex bioprocessing strains and aid in identifying new antimicrobials. Importantly, it was found that there was no loss in precision of core fluxes when compared to a traditional core model, and additionally there was an overall increase in precision when considering all observable reactions.

MID Max: LC-MS/MS Method for Measuring the Precursor and Product Mass Isotopomer Distributions of Metabolic Intermediates and Cofactors for Metabolic Flux Analysis Applications.

The analytical challenges to acquire accurate isotopic data of intracellular metabolic intermediates for stationary, nonstationary, and dynamic metabolic flux analysis (MFA) are numerous. This work presents MID Max, a novel LC-MS/MS workflow, acquisition, and isotopomer deconvolution method for MFA that takes advantage of additional scan types that maximizes the number of mass isotopomer distributions (MIDs) that can be acquired in a given experiment. The analytical method was found to measure the MIDs of 97 metabolites, corresponding to 74 unique metabolite-fragment pairs (32 precursor spectra and 42 product spectra) with accuracy and precision. The compounds measured included metabolic intermediates in central carbohydrate metabolism and cofactors of peripheral metabolism (e.g., ATP). Using only a subset of the acquired MIDs, the method was found to improve the precision of flux estimations and number of resolved exchange fluxes for wild-type E. coli compared to traditional methods and previously published data sets.