Project description:The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.
Project description:Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.
Project description:Nonalcoholic steatohepatitis (NASH) is a major cause of liver fibrosis with increasing prevalence worldwide. Currently there are no approved drugs available. The development of new therapies is difficult as diagnosis and staging requires biopsies. Consequently, predictive plasma biomarkers would be useful for drug development. Here we present a multi-omics approach to characterize the molecular pathophysiology and to identify new plasma biomarkers in a choline-deficient L-amino acid-defined diet rat NASH model. We analyzed liver samples by RNA-Seq and proteomics, revealing disease relevant signatures and a high correlation between mRNA and protein changes. Comparison to human data showed an overlap of inflammatory, metabolic, and developmental pathways. Using proteomics analysis of plasma we identified mainly secreted proteins that correlate with liver RNA and protein levels. We developed a multi-dimensional attribute ranking approach integrating multi-omics data with liver histology and prior knowledge uncovering known human markers, but also novel candidates. Using regression analysis, we show that the top-ranked markers were highly predictive for fibrosis in our model and hence can serve as preclinical plasma biomarkers. Our approach presented here illustrates the power of multi-omics analyses combined with plasma proteomics and is readily applicable to human biomarker discovery.
Project description:Three osteosarcoma (OS) cell lines (MG-63, Saos-2 and U-2 OS) and 1 osteoblastic cell line (hFOB1.19) were collected for this work. MG-63 was kindly provided by Dr. Agi Grigoriadis from University College London. Saos-2, U-2 OS and hFOB1.19 were purchased from ATCC. All cells used were kept in exponential phase of growth. Total RNA was extracted using the RNeasy Total RNA Isolation kit (QIAGEN). The quality and purity of the products were controlled by Agilent 2100. The final synthesized biotinylated cDNAs were hybridized to Affymetrix GeneChip® U133A 2.0 arrays following the protocol strictly. Arrays were scanned with the Affymetrix scanner 3000. Data analysis was performed by Microarray Suite 5.0 after pre-standard procedure. Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis platforms can accelerate the identification of tumor biomarkers. The results confirmed that CYC-1 with important biomedical function was an effective candidate biomarker for osteosarcoma early diagnosis.
Project description:Inflammatory bowel disease (IBD) represents a group of progressive disorders characterized by recurrent chronic inflammation of the gut. Ulcerative colitis and Crohn's disease are the major manifestations of IBD. While our understanding of IBD has progressed in recent years, its etiology is far from being fully understood, resulting in suboptimal treatment options. Complementing other biological endpoints, bioanalytical "omics" methods that quantify many biomolecules simultaneously have great potential in the dissection of the complex pathogenesis of IBD. In this review, we focus on the rapidly evolving proteomics and lipidomics technologies and their broad applicability to IBD studies; these range from investigations of immune-regulatory mechanisms and biomarker discovery to studies dissecting host⁻microbiome interactions and the role of intestinal epithelial cells. Future studies can leverage recent advances, including improved analytical methodologies, additional relevant sample types, and integrative multi-omics analyses. Proteomics and lipidomics could effectively accelerate the development of novel targeted treatments and the discovery of complementary biomarkers, enabling continuous monitoring of the treatment response of individual patients; this may allow further refinement of treatment and, ultimately, facilitate a personalized medicine approach to IBD.
Project description:The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD- (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.
Project description:Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.
Project description:Breath collection and analysis can be used to discover volatile biomarkers in a number of infectious and non-infectious diseases, such as malaria, tuberculosis, lung cancer, and liver disease. This protocol describes a reproducible method for sampling breath in children and then stabilizing breath samples for further analysis with gas chromatography-mass spectrometry (GC-MS). The goal of this method is to establish a standardized protocol for the acquisition of breath samples for further chemical analysis, from children aged 4-15 years. First, breath is sampled using a cardboard mouthpiece attached to a 2-way valve, which is connected to a 3 L bag. Breath analytes are then transferred to a thermal desorption tube and stored at 4-5 °C until analysis. This technique has been previously used to capture breath of children with malaria for successful breath biomarker identification. Subsequently, we have successfully applied this technique to additional pediatric cohorts. The advantage of this method is that it requires minimal cooperation on part of the patient (of particular value in pediatric populations), has a short collection period, does not require trained staff, and can be performed with portable equipment in resource-limited field settings.
Project description:The goals and objectives: To study Type 2 diabetes progression and the development of insulin resistance in two animal models with and without a high fat diet superimposed on these models. Background: Diabetes is a systemic metabolic imbalance involving multiple tissues/organs, and an early hallmark feature of this disease state is insulin resistance. Multifactorial interactions of genetics, prenatal environmental factors (fetal programming) and postnatal environmental factors (nutrition and activity) likely contribute to the diabetic phenotype.Animal models can serve as a valuable tool for studying diabetes disease progression and for identifying useful biomarkers of type 2 diabetes. Several inbred rodent models are available for diabetes related studies. The GK rat is an obvious choice among available inbred models as the genetic basis for this inheritable form of diabetes is polygenic (5), unlike most other inbred rodent models that exhibit single gene defects. Many of the characteristics of the GK rat mirror human diabetes (hyperglycemia, glucose intolerance, insulin resistance), although hyperlipidemia does not appear to be prominent in the GK rat. Due to its polygenic mode of inheritance and 100% penetrance, the GK rat may be a useful model for human diabetes. Induced animal models can also be useful in diabetes studies. One such model is metabolic syndrome resulting from experimentally induced fetal programming (produced by maternal malnutrition or by exposure to corticosteroids in the third trimester). Both in humans and animals, accumulating evidence suggests that alterations in the human fetal environment can result in permanent physiologic changes that manifest as increased incidence of adult onset pathology. Numerous epidemiological studies have forged a strong link between low birth weight and the development of metabolic syndrome in adulthood. From such observations has arisen the concept of “fetal programming” whereby exposure to some factor(s) during crucial stages in development can permanently alter or “reset” physiologic/metabolic functions. In the rat, exposure to corticosteroids during a “window” in third trimester gestation (CS programming) results in fetal growth retardation and insulin resistance in adult offspring. Genetic factors play a primarily role in the etiology of diabetes in the GK rat, whereas fetal environmental factors are causative in CS programming. (It should be noted that although altered fetal environmental effects, most likely stemming from maternal hyperglycemia, have been implicated to play some role in the decreased pancreatic B cell mass in GK rats, these effects occur earlier in gestation and therefore differ from programming by CS in late gestation.) A comparison of the development of insulin resistance in the GK rat with development in the CS programmed rat will provide insight into genetic and fetal environmental factors in disease development. Superimposing dietary alterations (i.e., high fat feeding) (11) on both animal models may aid in the dissection of multiple interacting factors (genetic, fetal environmental factors, postnatal environmental factors) on the development and progression of insulin resistance and type 2 diabetes. Such studies may also aid in the identification of useful biomarkers for insulin resistance and type 2 diabetes in humans. Proposed research: Experiments are designed to study disease progression and the development of insulin resistance in two animal models: the GK rat and the CS programmed rat, with and without a high fat diet superimposed on these models. Animals will be maintained in our facility from weaning (GK rats) or birth (CS programmed - WKY), and body weights taken weekly. Appropriate diets (normal or high fat) will be introduced at weaning. Groups of animals (N=6) will be sacrificed at 5 different ages: 4, 8, 12, 16, and 20 weeks. Plasma samples will be analyzed for markers of hyperglycemia, hyperinsulinemia, dyslipidimia, and for selected other hormonal factors which may contribute to disease etiology (adiponectin, leptin, corticosterone). At sacrifice, muscle is harvested, flash-frozen in liquid nitrogen, and warehoused in our tissue collection maintained at - 80 degrees C for this and possible future work. Study will initially focus on examination of selected molecular markers of insulin resistance at the mRNA level in rat liver (PEPCK, PDK4). Keywords: Type 2 diabetes, biomarker, rat, liver, time series