Integrative analysis of Multiple Sclerosis using a systems biology approach

Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammatory-demyelinating events in the central nervous system. Despite more than 40 years of MS research its aetiology remains unknown. This study aims to identify the most frequently reported and consistently regulated molecules in MS in order to generate molecular interaction networks and thereby leading to the identification of deregulated processes and pathways which could give an insight of the underlying molecular mechanisms of MS. Driven by an integrative systems biology approach, gene-expression profiling datasets were combined and stratified into "Non-treated" and "Treated" groups and additionally compared to other disease patterns. Molecular identifiers from dataset comparisons were matched to our Multiple Sclerosis database (MuScle; www.padb.org/muscle ). From 5079 statistically significant molecules, correlation analysis within groups identified a panel of 16 high-confidence genes unique to the naïve MS phenotype, whereas the "Treated" group reflected a common pattern associated with autoimmune disease. Pathway and gene-ontology clustering identified the Interferon gamma signalling pathway as the most relevant amongst all significant molecules, and viral infections as the most likely cause of all down-stream events observed. This hypothesis-free approach revealed the most significant molecular events amongst different MS phenotypes which can be used for further detailed studies

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology's potential, a set of transcriptomic datasets are meta-analyzed as an example

CVD and oxidative stress

Nowadays, it is known that oxidative stress plays at least two roles within the cell, the generation of cellular damage and the involvement in several signaling pathways in its balanced normal state. So far, a substantial amount of time and effort has been expended in the search for a clear link between cardiovascular disease (CVD) and the effects of oxidative stress. Here, we present an overview of the different sources and types of reactive oxygen species in CVD, highlight the relationship between CVD and oxidative stress and discuss the most prominent molecules that play an important role in CVD pathophysiology. Details are given regarding common pharmacological treatments used for cardiovascular distress and how some of them are acting upon ROS-related pathways and molecules. Novel therapies, recently proposed ROS biomarkers, as well as future challenges in the field are addressed. It is apparent that the search for a better understanding of how ROS are contributing to the pathophysiology of CVD is far from over, and new approaches and more suitable biomarkers are needed for the latter to be accomplished

Modelling liver cancer microenvironment using a novel 3D culture system

The tumor microenvironment and its contribution to tumorigenesis has been a focal highlight in recent years. A two-way communication between the tumor and the surrounding microenvironment sustains and contributes to the growth and metastasis of tumors. Progression and metastasis of hepatocellular carcinoma (HCC) have been reported to be exceedingly influenced by diverse microenvironmental cues. In this study, we present a 3D-culture model of liver cancer to better mimic in vivo tumor settings. By creating novel 3D co-culture model that combines free-floating and scaffold-based 3D-culture techniques of liver cancer cells and fibroblasts, we aimed to establish a simple albeit reproducible ex vivo cancer microenvironment model that captures tumor-stroma interactions. The model presented herein exhibited unique gene expression and protein expression profiles when compared to 2D and 3D mono-cultures of liver cancer cells. Our results showed that in vivo like conditions cannot be mimicked by simply growing cancer cells as spheroids, but by co-culturing them with 3D fibroblast with which they were able to crosstalk. This was evident by the upregulation of several pathways involved in HCC, and the increase in secreted factors by co-cultured cancer cells, many of which are also involved in tumor-stroma interactions. Compared to the conventional 2D culture, the proposed model exhibits an increase in the expression of genes associated with development, progression, and poor prognosis of HCC. Our results correlated with an aggressive outcome that better mirrors in vivo HCC, and therefore, a more reliable platform for molecular understanding of HCC

Modelling liver cancer microenvironment: novel 3D culture system as a potential anti-cancer drug screening tool

The tumor microenvironment and its contribution to tumorigenesis has been a focal highlight in recent years. A two-way communication between the tumor and the surrounding microenvironment sustains and contributes to the growth and metastasis of tumors. Progression and metastasis of hepatocellular carcinoma have been reported to be exceedingly influenced by diverse microenvironmental cues. In this study, we present a 3D-culture model of liver cancer to better mimic in vivo tumor settings. By creating novel 3D co-culture model that combines free-floating and scaffold based 3D-culture techniques of liver cancer cells and fibroblasts, we aimed to establish a simple albeit reproducible ex vivo cancer microenvironment model that captures tumor-stroma interactions. The model presented herein exhibited unique gene expression and protein expression profiles when compared to 2D and 3D mono-cultures of liver cancer cells. Our results showed that in vivo like conditions cannot be mimicked by simply growing cancer cells as spheroids, but by co-culturing them with 3D fibroblast with which they were able to cross-talk. This was evident by the upregulation of several pathways involved in HCC, and the increase in secreted factors by co-cultured cancer cells, many of which are also involved in tumor-stroma interactions. Compared to the conventional 2D culture, the proposed model exhibits an increase in the expression of genes associated with development, progression, and poor prognosis of HCC. Our results correlated with an aggressive outcome that better mirrors in vivo HCC, and therefore, a more reliable platform for molecular understanding of HCC and possibly better anti-cancer drug screening

Maternal iron kinetics and maternal–fetal iron transfer in normal-weight and overweight pregnancy

Background Inflammation during pregnancy may aggravate iron deficiency (ID) by increasing serum hepcidin and reducing iron absorption. This could restrict iron transfer to the fetus, increasing risk of infant ID and its adverse effects. Objectives We aimed to assess whether iron bioavailability and/or iron transfer to the fetus is impaired in overweight/obese (OW) pregnant women with adiposity-related inflammation, compared with normal-weight (NW) pregnant women. Methods In this prospective study, we followed NW (n = 43) and OW (n = 40) pregnant women who were receiving iron supplements from the 14th week of gestation to term and followed their infants to age 6 mo. We administered 57Fe and 58Fe in test meals mid-second and mid-third trimester, and measured tracer kinetics throughout pregnancy and infancy. Results In total, 38 NW and 36 OW women completed the study to pregnancy week 36, whereas 30 NW and 27 OW mother–infant pairs completed the study to 6 mo postpartum. Both groups had comparable iron status, hemoglobin, and serum hepcidin throughout pregnancy. Compared with the NW, the OW pregnant women had 1) 43% lower fractional iron absorption (FIA) in the third trimester (P = 0.033) with median [IQR] FIA of 23.9% [11.4%–35.7%] and 13.5% [10.8%–19.5%], respectively; and 2) 17% lower maternal–fetal iron transfer from the first tracer (P = 0.051) with median [IQR] maternal–fetal iron transfer of 4.8% [4.2%–5.4%] and 4.0% [3.6%–4.6%], respectively. Compared with the infants born to NW women, infants born to OW women had lower body iron stores (BIS) with median [IQR] 7.7 [6.3–8.8] and 6.6 [4.6–9.2] mg/kg body weight at age 6 mo, respectively (P = 0.024). Prepregnancy BMI was a negative predictor of maternal–fetal iron transfer (β = −0.339, SE = 0.144, P = 0.025) and infant BIS (β = −0.237, SE = 0.026, P = 0.001). Conclusions Compared with NW, OW pregnant women failed to upregulate iron absorption in late pregnancy, transferred less iron to their fetus, and their infants had lower BIS. These impairments were associated with inflammation independently of serum hepcidin

CVD and Oxidative Stress

Nowadays, it is known that oxidative stress plays at least two roles within the cell, the generation of cellular damage and the involvement in several signaling pathways in its balanced normal state. So far, a substantial amount of time and effort has been expended in the search for a clear link between cardiovascular disease (CVD) and the effects of oxidative stress. Here, we present an overview of the different sources and types of reactive oxygen species in CVD, highlight the relationship between CVD and oxidative stress and discuss the most prominent molecules that play an important role in CVD pathophysiology. Details are given regarding common pharmacological treatments used for cardiovascular distress and how some of them are acting upon ROS-related pathways and molecules. Novel therapies, recently proposed ROS biomarkers, as well as future challenges in the field are addressed. It is apparent that the search for a better understanding of how ROS are contributing to the pathophysiology of CVD is far from over, and new approaches and more suitable biomarkers are needed for the latter to be accomplished

APOBECs orchestrate genomic and epigenomic editing across health and disease

APOBEC proteins can deaminate cytosine residues in DNA and RNA. This can lead to somatic mutations, DNA breaks, RNA modifications, or DNA demethylation in a selective manner. APOBECs function in various cellular compartments and recognize different nucleic acid motifs and structures. They orchestrate a wide array of genomic and epigenomic modifications, thereby affecting various cellular functions positively or negatively, including immune editing, viral and retroelement restriction, DNA damage responses, DNA demethylation, gene expression, and tissue homeostasis. Furthermore, the cumulative increase in genomic and epigenomic editing with aging could also, at least in part, be attributed to APOBEC function. We synthesize our cumulative understanding of APOBEC activity in a unifying overview and discuss their genomic and epigenomic impact in physiological, pathological, and technological contexts

Integrative OMICS data-driven procedure using a derivatized meta-analysis approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example