Omics-based Predictors of NAFLD/Potential NASH

The national treatment program intended to provide a cure for Egyptian HCV-infected patients was the sparking light toward an HCV-free and healthy liver among the Egyptian population. However, another rapidly evolving liver disease has been emerging with an increase in both mortality and morbidity, and an estimated prevalence of 25-35% across the globe; non-alcoholic fatty liver disease (NAFLD). The highest rate of NAFLD is reported from the Middle East (32%). The prevalence of NAFLD in the general population increases with age; from 3% in children, 5% in teenagers, 18% between 20 and 40 years, 39% in those aged 40 to 50 years, and to over 40% in those greater than 70 years.

NAFLD is a metabolic disorder, whose spectrum progresses from simple steatosis to non-alcoholic steatohepatitis (NASH) and liver fibrosis, potentially leading to cirrhosis, hepatocellular carcinoma, and liver failure. Accordingly, NASH is considered a severe form of NAFLD. Given the association between NAFLD and the growing global epidemics of obesity, type 2 diabetes, sedentary lifestyles, dyslipidemia, and unhealthy dietary patterns, the prevalence of NAFLD is expected to increase. Therefore, NAFLD is a major clinical and economic burden on the world's healthcare systems.

Although liver biopsy is the reference standard for the assessment of fibrosis associated with NASH, the inherent limitations of an invasive procedure, and the need for repeat sampling, have led to the development of several non-invasive tests (NITs) as alternatives to liver biopsy. The current NITs were used for the diagnosis of advanced fibrosis in patients with NAFLD (5), Such NITs mostly include biological (serum biomarker algorithms) or physical (imaging assessment of tissue stiffness) assessments. However, currently available NITs have several limitations, such as variability, inadequate accuracy, and risk factors for error. The current NITs were used not only to diagnose significant fibrosis in chronic hepatitis C but also the diagnosis of advanced fibrosis in patients with NAFLD/NASH.

In low-resource countries, despite the high prevalence of NAFLD and that its early stages are reversible with diet and lifestyle modifications, the availability of NITs is likely to be limited, especially the more expensive imaging-based tests. Blood-based biomarkers are therefore attractive, but those available to date have only moderate diagnostic accuracy. Furthermore, only a minority of NAFLD cases are diagnosed and correctly treated as detecting early stages is hindered by a lack of non-invasive reliable, and validated methods of early diagnosis. In addition, there are few options available for the management of NASH and no current FDA-approved therapies for NAFLD.

To date, the pathogenesis of NAFLD is not fully clarified. NAFLD is thought to be involved in complex interactions among diet, genetic susceptibility, and gut microbiota (6). At the same time, the role of gut microbiota and microbial metabolites in NAFLD has attracted more attention. Gut microbiota regulates the development and progression of NAFLD on the basis of the gut-liver axis. Future targeted treatment strategies based on the pathogenic pathways are accordingly needed to develop an effective treatment for patients with NASH.

Broadly speaking, the scientific fields associated with measuring the biological molecules in a high-throughput way are called "omics". Although, ongoing technological advances in omics technologies such as genomics, proteomics, and metabolomics hold great promise for the discovery of useful non-invasive biomarkers and increased pathophysiological understanding of NAFLD and NASH diagnosis, prognosis, and drug response. The majority applied one omics technique. Advances in human genetics present new opportunities to address the urgent need for NASH therapeutics, based on an improved understanding of the interaction between the genetic and environmental risk factors for the development of NASH. MicroRNAs (miRNAs were reported to be closely related to NAFLD by targeting genes involved in lipid metabolism and pro-inflammatory factors which are related to the pathogenesis of NAFLD. In addition, many glycoproteins have been linked with the diagnosis of liver disorders given that the majority of serum glycoproteins are synthesized in the liver. Unfortunately, a few pioneering studies have successfully applied multi-omics technologies to investigate NAFLD/NASH, none of which was done among the Egyptian population.

The investigators aimed at developing a multi-omics composite predictive biomarker panel to be used as an Egyptian scoring system to improve the predictive power of the diagnosis of NAFLD and limit its progression to NASH, compared with the traditional markers that usually focus on a single aspect of the disease. Such predictive biomarkers can also benefit the clinical management of NAFLD to limit its progression to NASH.

Specific objectives:

1. To identify the functional variants causing dyslipidemia and its causative genes mutation on a subsample of participants (30 patients) using next-generation sequencing (NGS).
2. To identify and verify the most significant 10 genes (previously identified to be associated with NAFLD/NASH in genome-wide analyses) among the Egyptian population: PNPLA3 rs738409, PNPLA3 rs6006460, FDFT1 rs2645424, COL13A1 rs1227756, NCAN rs2228603, LYPLAL1 rs12137855, GCKR rs780094, PPP1R3B rs4240624, PPAR rs1800234 and MTTP rs1800591 using TaqMan SNP Genotyping Assay
3. To outline the possible association of genetic variations with the currently used treatment for NAFLD with and without fibrosis (e.g vitamin E, vitamin c, vitamin D; metformin for children and pioglitazone for adults…)
4. To identify the expression level of plasma miRNAs (the top 5 of 168-panel genes of plasma miRNAs) by PCR array as per the studied groups
5. To assess the potential of altered miRNAs expression profile associated with currently used drug for the treatment of NAFLD without or with fibrosis.
6. To identify the glycosylation profile of both N- and O-glycoproteins proved to be linked with NAFLD/NASH (transferrin, apolipoprotein C III (apoC III), haptoglobin, Mac2 binding protein, IgG) -) among Egyptian patients with NAFLD without or with fibrosis
7. To assess the relationship between the glycosylation pattern of the studied glycoproteins in response to currently used drug for NAFLD WITH AND WITHOUT FIBROSIS treatment.
8. To identify the bacterial isolates among the Egyptian populations that are linked to NAFLD patients without and with fibrosis and controls (out of 10 bacterial isolates) using -Rapid RT-PCR test for 16S rRNA gene amplicon library preparation and sequencing
9. To identify the top five salivary detected microbiome-related metabolites by comparing their concentrations for NAFLD without and with fibrosis and control using Gas Chromatography-Mass Spectrometer (GC-MS) Analysis
10. To assess the association of some known meta-genomic functions using Commercial ELISA kits. (salivary concentrations of the lactoferrin, lipopolysaccharide, and immunoglobulin A for detecting NAFLD without and with fibrosis.
11. To determine whether combinations of the detected salivary metabolites could be used as a biomarker signature for detecting NAFLD without and with fibrosis.
12. To identify the interaction and influence of the epidemiological, dietary, lifestyle on the studied multi-omics biomarkers and on the clinical presentation of NAFLD without and with fibrosis.

Research Methodology and tools

This study is a cross-sectional exploratory study conducted along 24 months tools:

1. Baseline Assessment Questionnaire: 1.1 Assessment of the sociodemographic characteristics, detailed history taking, and other known risk factors (oral hygiene….), family pedigree construction up to three generations to diagnose the risk of genetic and familial causes for NAFLD and NASH 1.2 Nutritional and dietary behavioral assessment with anthropometric measurements using Diet Quality index assessment questionnaire, beverages intake
2. Multi-omics biomarkers in blood and alive will be investigated in this study. The approach is based on detecting new molecular pathogeneses and new genes for discovering Egyptian-related biomarkers of the studied multi-omics markers. 2.1 Blood samples: Genomics 2.1.1.1 Detection of genes involved in Dyslipidemia: Identification of functional variant(s) that is responsible for Dyslipidemia using next-generation sequencing (NGS) panels of Dyslipidemia main genes; (LDLR), (APOB), (PCSK9) and (LDLRAP). This technique can help us to identify novel dyslipidemia-related variants and those related commonly to the Egyptian population. This will be applied to 30 participants only diagnosed to have dyslipidemia. 2.1.1.2 Detection of gene polymorphism for NAFLD and NASH by "TaqMan SNP Genotyping Assay" for the whole genome previously identified in genome-wide analyses to be linked to NAFLD/NASH (to be verified among the Egyptian population) PNPLA3 rs738409, PNPLA3 rs6006460, FDFT1 rs2645424, COL13A1 rs1227756, NCAN rs2228603, LYPLAL1 rs12137855, GCKR rs780094, PPP1R3B rs4240624, PPAR rs1800234 and MTTP rs1800591: from all participants

• DNA will be extracted from the peripheral blood using a DNA extraction kit supplied by Qiagen, the USA using Nanodropper 2000 (ThermoScientific).
• SNP Genotyping will be performed using the Roche real-time PCR system (light cycler 480) with TaqMan allelic discrimination assay (Applied Biosystems, USA). 2.1.2 Epi-genomics Expression profiling of plasma microRNAs
• Expression analysis of top 5 miRNAs: The top 5 altered miRNAs will be selected to be analyzed in all patients and controls by real-time PCR using mercury LNA SYBR Green PCR kit (Qiagen). Fold change of miRNA will be calculated using 2-∆Ct method. 2.1.3 Glycoproteomics: identifying the glycosylation pattern transferrin, apolipoprotein C III (apoC III), haptoglobin, Mac2 binding protein, IgG (Santa Cruz, USA). T 2.2 Saliva samples: 2.2.1 Salivary Metabolomics 2.2.2 Metabolites Identification using Gas Chromatography-Mass Spectrometer (GC-MS) Analysis: assessing the concentration of the top five identified microbial-related metabolites (which will be found in at least 85% of samples) as metabolomics biomarkers among all participants will be investigated using GC-MS analysis. 2.2.3 Predictive analysis of some known meta-genomic functions. The salivary concentrations of the lactoferrin, lipopolysaccharide (LPS), and immunoglobulin A (IgA) will be determined for all participants.

1. Behavioral modification through individual counseling