Ting-Li Han

The metabolic basis of Candida albicans morphogenesis and quorum sensing

Candida albicans is a polymorphic fungus that has the ability to rapidly switch between yeast and filamentous forms. The morphological transition appears to be a critical virulence factor of this fungus. Recent studies have elucidated the signal transduction pathways and quorum sensing molecules that affect the morphological transition of C. albicans. The metabolic mechanisms that recognize, and respond to, such signaling molecules and promote the morphological changes at a system level, however, remain unknown. Here we review the metabolic basis of C. albicans morphogenesis and we discuss the role of primary metabolic pathways and quorum sensing molecules in the morphogenetic process. We have reconstructed, in silico, the central carbon metabolism and sterol biosynthesis of C. albicans based on its genome sequence, highlighting the metabolic pathways associated with the dimorphic transition and virulence as well as pathways involved in the biosynthesis of important quorum sensing molecules.

Hair Metabolomics: Identification of Fetal Compromise Provides Proof of Concept for Biomarker Discovery

Analysis of the human metabolome has yielded valuable insights into health, disease and toxicity. However, the metabolic profile of complex biological fluids such as blood is highly dynamic and this has limited the discovery of robust biomarkers. Hair grows relatively slowly, and both endogenous compounds and environmental exposures are incorporated from blood into hair during growth, which reflects the average chemical composition over several months. We used hair samples to study the metabolite profiles of women with pregnancies complicated by fetal growth restriction (FGR) and healthy matched controls. We report the use of GC-MS metabolite profiling of hair samples for biomarker discovery. Unsupervised statistical analysis showed complete discrimination of FGR from controls based on hair composition alone. A predictive model combining 5 metabolites produced an area under the receiver-operating curve of 0.998. This is the first study of the metabolome of human hair and demonstrates that this biological material contains robust biomarkers, which may lead to the development of a sensitive diagnostic tool for FGR, and perhaps more importantly, to stable biomarkers for a range of other diseases.

Mass Spectrometry-Based Proteomics for Pre-Eclampsia and Preterm Birth

Pregnancy-related complications such as pre-eclampsia and preterm birth now represent a notable burden of adverse health. Pre-eclampsia is a hypertensive disorder unique to pregnancy. It is an important cause of maternal death worldwide and a leading cause of fetal growth restriction and iatrogenic prematurity. Fifteen million infants are born preterm each year globally, but more than one million of those do not survive their first month of life. Currently there are no predictive tests available for diagnosis of these pregnancy-related complications and the biological mechanisms of the diseases have not been fully elucidated. Mass spectrometry-based proteomics have all the necessary attributes to provide the needed breakthrough in understanding the pathophysiology of complex human diseases thorough the discovery of biomarkers. The mass spectrometry methodologies employed in the studies for pregnancy-related complications are evaluated in this article. Top-down proteomic and peptidomic profiling by laser mass spectrometry, liquid chromatography or capillary electrophoresis coupled to mass spectrometry, and bottom-up quantitative proteomics and targeted proteomics by liquid chromatography mass spectrometry have been applied to elucidate protein biomarkers and biological mechanism of pregnancy-related complications. The proteomes of serum, urine, amniotic fluid, cervical-vaginal fluid, placental tissue, and cytotrophoblastic cells have all been investigated. Numerous biomarkers or biomarker candidates that could distinguish complicated pregnancies from healthy controls have been proposed. Nevertheless, questions as to the clinically utility and the capacity to elucidate the pathogenesis of the pre-eclampsia and preterm birth remain to be answered.

Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is defined as an abnormal glucose tolerance that develops, or is first recognized during pregnancy; the development of GDM markedly increases risks of adverse obstetric and perinatal outcome. The immediate consequences include an increased likelihood of a Caesarean section, hypoglycaemia of the newborn, respiratory distress syndrome, and macrosomia. Longer-term implications of a pregnancy affected by GDM include a substantially increased risk of the mother developing type 2 diabetes postnatally, as well as the offspring having an increased susceptibility to obesity and related metabolic complications in adulthood. Within the Asia–Pacific region there are an estimated 76 million women at risk of having a pregnancy complicated by diabetes, with recent estimates suggesting up to 18 % of pregnancies in China may be complicated by GDM [1]. Metabolomic profiling is a strategy for investigating the low weight molecules that represent the metabolome of a cell, tissue, or organism. The metabolome’s position as a downstream product of gene expression enables the provision of a high-resolution multifactorial phenotypic signature of disease aetiology, manifestation, or pathophysiology, and has led to the search for metabolite biomarkers. Previous studies have explored the GDMspecific metabolomic profile of blood samples, with promising results [2]. However, the dynamic nature of biofluids can be influenced by many transient factors such as recent dietary intake, and hormonal changes. Analysis of blood requires invasive sampling, immediate processing and curation of samples under controlled circumstances if analysis is not immediate. Hair, in contrast, is a highly stable structure retaining endogenous compounds and reflecting environmental exposures for many months; moreover, hair sampling is non-invasive and the storage and processing of hair is much simpler, making it a particularly useful source of biomarkers in low resource settings. We have previously demonstrated the potential of the hair metabolome by identifying a metabolomic signature able to predict the subsequent development of fetal growth restriction [3]. The current pilot study aimed to investigate the maternal hair metabolome in relation to GDM outcome, to determine if maternal hair could be a source of metabolic information underlying the development of GDM.

Metabolome analysis during the morphological transition of Candida albicans

Candida albicans is an opportunistic pathogen of humans with significant mortality in severely immunocompromised patients. The ability to switch from yeast to hyphal morphology and vice versa, in response to various environmental cues, is believed to be a critical virulence factor of this fungus. However, the mechanisms that recognize such environmental signals and trigger the morphological change at a system level are still not clearly understood. Therefore, we have compared the metabolite profiles of C. albicans cells growing under different hyphae-inducing conditions to the metabolite profiles of growing yeast cells. Surprisingly our results suggest an overall downregulation of cellular metabolism during the yeast to hyphal morphological transition. Among the metabolic pathways involved in the central carbon metabolism, we have found seventeen that were significantly downregulated in all three hyphae-inducing conditions. This indicates that these central carbon metabolic pathways are likely to be intrinsically involved in the downstream effects of the morphogenetic process.

Mitochondrial mutations and metabolic adaptation in pancreatic cancer

BackgroundPancreatic cancer has a five-year survival rate of ~8%, with characteristic molecular heterogeneity and restricted treatment options. Targeting metabolism has emerged as a potentially effective therapeutic strategy for cancers such as pancreatic cancer, which are driven by genetic alterations that are not tractable drug targets. Although somatic mitochondrial genome (mtDNA) mutations have been observed in various tumors types, understanding of metabolic genotype-phenotype relationships is limited.MethodsWe deployed an integrated approach combining genomics, metabolomics, and phenotypic analysis on a unique cohort of patient-derived pancreatic cancer cell lines (PDCLs). Genome analysis was performed via targeted sequencing of the mitochondrial genome (mtDNA) and nuclear genes encoding mitochondrial components and metabolic genes. Phenotypic characterization of PDCLs included measurement of cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) using a Seahorse XF extracellular flux analyser, targeted metabolomics and pathway profiling, and radiolabelled glutamine tracing.ResultsWe identified 24 somatic mutations in the mtDNA of 12 patient-derived pancreatic cancer cell lines (PDCLs). A further 18 mutations were identified in a targeted study of ~1000 nuclear genes important for mitochondrial function and metabolism. Comparison with reference datasets indicated a strong selection bias for non-synonymous mutants with predicted functional effects. Phenotypic analysis showed metabolic changes consistent with mitochondrial dysfunction, including reduced oxygen consumption and increased glycolysis. Metabolomics and radiolabeled substrate tracing indicated the initiation of reductive glutamine metabolism and lipid synthesis in tumours.ConclusionsThe heterogeneous genomic landscape of pancreatic tumours may converge on a common metabolic phenotype, with individual tumours adapting to increased anabolic demands via different genetic mechanisms. Targeting resulting metabolic phenotypes may be a productive therapeutic strategy.

Metabolic response of Candida albicans to phenylethyl alcohol under hyphae-inducing conditions.

Phenylethyl alcohol was one of the first quorum sensing molecules (QSMs) identified in C. albicans. This extracellular signalling molecule inhibits the hyphal formation of C. albicans at high cell density. Little is known, however, about the underlying mechanisms by which this QSM regulates the morphological switches of C. albicans. Therefore, we have applied metabolomics and isotope labelling experiments to investigate the metabolic changes that occur in C. albicans in response to phenylethyl alcohol under defined hyphae-inducing conditions. Our results showed a global upregulation of central carbon metabolism when hyphal development was suppressed by phenylethyl alcohol. By comparing the metabolic changes in response to phenylethyl alcohol to our previous metabolomic studies, we were able to short-list 7 metabolic pathways from central carbon metabolism that appear to be associated with C. albicans morphogenesis. Furthermore, isotope-labelling data showed that phenylethyl alcohol is indeed taken up and catabolised by yeast cells. Isotope-labelled carbon atoms were found in the majority of amino acids as well as in lactate and glyoxylate. However, isotope-labelled carbon atoms from phenylethyl alcohol accumulated mainly in the pyridine ring of NAD+/NADH and NADP−/NADPH molecules, showing that these nucleotides were the main products of phenylethyl alcohol catabolism. Interestingly, two metabolic pathways where these nucleotides play an important role, nitrogen metabolism and nicotinate/nicotinamide metabolism, were also short-listed through our previous metabolomics works as metabolic pathways likely to be closely associated with C. albicans morphogenesis.

The metabolic response of Candida albicans to farnesol under hyphae-inducing conditions

Farnesol is a quorum-sensing molecule (QSM) produced, and sensed, by the polymorphic fungus, Candida albicans. This cell-to-cell communication molecule is known to suppress the hyphal formation of C. albicans at high cell density. Despite many studies investigating the signalling mechanisms by which QSMs influence the morphogenesis of C. albicans, the downstream metabolic effect of these signalling pathways in response to farnesol-mediated morphogenesis remains obscure. Here, we have used metabolomics to investigate the metabolic response of C. albicans upon exposure to farnesol under hyphae-inducing conditions. We have found a general up-regulation of central carbon metabolic pathways when hyphal formation was suppressed by farnesol evidenced by a considerably larger number of central carbon metabolic intermediates detected under this condition at an overall lower intracellular level. By combining the metabolic profiles from farnesol-exposed cells with previous metabolomics data for C. albicans undergoing morphogenesis, we have identified several metabolic pathways that are likely to be associated with the morphogenetic process of C. albicans, as well as metabolic pathways such as those involved in lipid metabolism that appeared to be specifically affected by farnesol. Therefore, our results provide important new insights into the metabolic role of farnesol in C. albicans metabolism.

Impaired mitochondrial fusion, autophagy, biogenesis and dysregulated lipid metabolism is associated with preeclampsia

&NA; Preeclampsia(PE) is a pregnancy complication that is diagnosed by the new onset of hypertension and proteinuria. The etiology of PE remains unclear; however, growing evidence indicates that mitochondrial impairment contributes to the pathogenesis. Therefore, we aim to investigate the function of mitochondria in the development of PE. The mitochondrial metabolome in preeclamptic (n = 11) and normal (n = 11) placentas were analyzed using Gas chromatography‐mass spectrometry (GC‐MS). Students t‐tests and receiver operating characteristic (ROC) curves were conducted to determine which mitochondrial metabolites differed significantly between the two groups. The Pathway Activity Profiling (PAPi) R package was used to predict which metabolic pathways were affected by PE. Western blot analysis was performed to identify the candidate proteins which were associated with mitochondrial repair regulation. GC‐MS analysis demonstrated that higher levels of 38 metabolites and lower levels of 2 metabolites were observed in the placenta of patients with severe PE (sPE). Five fatty acids had an area under the ROC curve above 90%. Furthermore, we revealed abnormal regulation of mitochondrial dynamics, autophagy, and biogenesis in sPE. Our discoveries indicate that the compromised lipid metabolism in sPE may result from dysfunctional mitochondria, thus revealing new insights into the etiology of the disease.

Metabolite Profile of Cervicovaginal Fluids from Early Pregnancy Is Not Predictive of Spontaneous Preterm Birth

In our study, we used a mass spectrometry-based metabolomic approach to search for biomarkers that may act as early indicators of spontaneous preterm birth (sPTB). Samples were selected as a nested case-control study from the Screening for Pregnancy Endpoints (SCOPE) biobank in Auckland, New Zealand. Cervicovaginal swabs were collected at 20 weeks from women who were originally assessed as being at low risk of sPTB. Samples were analysed using gas chromatography-mass spectrometry (GC-MS). Despite the low amount of biomass (16–23 mg), 112 compounds were detected. Statistical analysis showed no significant correlations with sPTB. Comparison of reported infection and plasma inflammatory markers from early pregnancy showed two inflammatory markers were correlated with reported infection, but no correlation with any compounds in the metabolite profile was observed. We hypothesise that the lack of biomarkers of sPTB in the cervicovaginal fluid metabolome is simply because it lacks such markers in early pregnancy. We propose alternative biofluids be investigated for markers of sPTB. Our results lead us to call for greater scrutiny of previously published metabolomic data relating to biomarkers of sPTB in cervicovaginal fluids, as the use of small, high risk, or late pregnancy cohorts may identify metabolite biomarkers that are irrelevant for predicting risk in normal populations.