Genetic Variation in CCL5 Signaling Genes and Triple Negative Breast Cancer: Susceptibility and Prognosis Implications

Triple-negative breast cancer (TNBC) accounts for ~15\u201320% of breast cancer (BC) and has a higher rate of early relapse and mortality compared to other subtypes. The Chemokine (C-C motif) ligand 5 (CCL5) and its signaling pathway have been linked to TNBC. We aimed to investigate the susceptibility and prognostic implications of genetic variation in CCL5 signaling genes in TNBC in the present study. We characterized variants in CCL5 and that of six other CCL5 signaling genes (CCND1, ZMIZ1, CASP8, NOTCH2, MAP3K21, and HS6ST3) among 1,082 unrelated Tunisian subjects (544 BC patients, including 196 TNBC, and 538 healthy controls), assessed the association of the variants with BC-specific overall survival (OVS) and progression-free survival (PFS), and correlated CCL5 mRNA and serum levels with CCL5 genotypes. We found a highly significant association between the CCND1 rs614367-TT genotype (OR = 5.14; P = 0.004) and TNBC risk, and identified a significant association between the rs614367-T allele and decreased PFS in TNBC. A decreased risk of lymph node metastasis was associated with the MAP3K21 rs1294255-C allele, particularly in rs1294255-GC (OR = 0.47; P = 0.001). CCL5 variants (rs2107538 and rs2280789) were linked to CCL5 serum and mRNA levels. In the TCGA TNBC/Basal-like cohort the MAP3K21 rs1294255-G allele was associated with a decreased OVS. High expression of CCL5 in breast tumors was significantly associated with an increased OVS in all BC patients, but particularly in TNBC/Basal-like patients. In conclusion, genetic variation in CCL5 signaling genes may predict not only TNBC risk but also disease aggressiveness

Potential Nutrigenomic Approaches to Reduce the High Incidence of Obesity in Qatar

Obesity prevalence has been growing exponentially over the last few decades, with a high impact in high-income countries, like Qatar. Several approaches are attempting to understand the causes of this phenomenon however more important is what to do to reverse the trends. Obesity is widely studied, mostly in Europe and the Unites States, and a number of studies have demonstrate the role of specific gene patterns, transcriptome and proteome pathways, and gut microbiome strains. The Omics sciences have a great potential to investigate the determinants of non-communicable diseases, such as obesity. Nutritional genomics sciences apply all the Omics approaches to address nutrition-related diseases, investigating the interaction between genes and diet. To date, few data are available from nutrigenomic studies conducted in Middle East and particularly in Qatar to help the design of targeted interventions. The high incidence of obesity and the peculiar genetic make-up of the Qatari population provide opportunities for exploring nutrigenomic approaches to help addressing the problem

Precision medicine in the era of artificial intelligence: implications in chronic disease management.

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases

Genomic characterization of a polyvalent hydrocarbonoclastic bacterium Pseudomonas sp. strain BUN14

Bioremediation offers a viable alternative for the reduction of contaminants from the environment, particularly petroleum and its recalcitrant derivatives. In this study, the ability of a strain of Pseudomonas BUN14 to degrade crude oil, pristane and dioxin compounds, and to produce biosurfactants, was investigated. BUN14 is a halotolerant strain isolated from polluted sediment recovered from the refinery harbor on the Bizerte coast, north Tunisia and capable of producing surfactants. The strain BUN14 was assembled into 22 contigs of 4,898,053 bp with a mean GC content of 62.4%. Whole genome phylogeny and comparative genome analyses showed that strain BUN14 could be affiliated with two validly described Pseudomonas Type Strains, P. kunmingensis DSM 25974T and P. chloritidismutans AW-1T. The current study, however, revealed that the two Type Strains are probably conspecific and, given the priority of the latter, we proposed that P. kunmingensis DSM 25974 is a heteronym of P. chloritidismutans AW-1T. Using GC-FID analysis, we determined that BUN14 was able to use a range of hydrocarbons (crude oil, pristane, dibenzofuran, dibenzothiophene, naphthalene) as a sole carbon source. Genome analysis of BUN14 revealed the presence of a large repertoire of proteins (154) related to xenobiotic biodegradation and metabolism. Thus, 44 proteins were linked to the pathways for complete degradation of benzoate and naphthalene. The annotation of conserved functional domains led to the detection of putative genes encoding enzymes of the rhamnolipid biosynthesis pathway. Overall, the polyvalent hydrocarbon degradation capacity of BUN14 makes it a promising candidate for application in the bioremediation of polluted saline environments

Longitudinal impact of process-oriented guided inquiry learning on the attitudes, self-efficacy and experiences of pre-medical chemistry students

A follow-up study was conducted with foundation-year chemistry students who were taught in an inquiry- and role-based, small-group active learning environment in order to evaluate their attitudes, experiences and self-efficacy during pre-medical chemistry courses. The study adopted a mixedmethods research design that involved both experimental and comparison groups. Using the CAEQ (Chemistry Attitudes and Experiences Questionnaire) and the ASCI v2 (Attitude toward the Study of Chemistry Inventory), the findings of this study indicated that inquiry-based chemistry learning experience improves the students’ intellectual accessibility and emotional satisfaction as well as develops their self-efficacy levels while pursuing intensive pre-medical courses in chemistry. The results of the qualitative data analyses using a course experience questionnaire indicated that the process-oriented guided inquiry learning (POGIL) experience helped the students succeed in rigorous pre-medical chemistry courses and gained some process skills required in the medical programme as listed by the AAMC (American Association of Medical Colleges)

CHAMPION: Chalmers Hierarchical Atomic, Molecular, Polymeric & Ionic Analysis Toolkit

We present CHAMPION: a software developed to automatically detect time-dependent bonds between atoms based on their dynamics, classify the local graph topology around them, and analyze the physicochemical properties of these topologies by statistical physics. In stark contrast to methodologies where bonds are detected based on static conditions such as cut-off distances, CHAMPION considers pairs of atoms to be bound only if they move together and act as a bound pair over time. Furthermore, the time-dependent global bond graph is possible to split into dynamically shifting connected components or subgraphs around a certain chemical motif and thereby allow the physicochemical properties of each such topology to be analyzed by statistical physics. Applicable to condensed matter and liquids in general, and electrolytes in particular, this allows both quantitative and qualitative descriptions of local structure, as well as dynamical processes such as speciation and diffusion. We present here a detailed overview of CHAMPION, including its underlying methodology, implementation and capabilities.Comment: 11 pages, 8 figure