From evidence-based medicine to genomic medicine

Assessing the impact of jigsaw technique for cooperative learning in undergraduate medical education: merits, challenges, and forward prospects

BACKGROUND

Jigsaw method is a structured cooperative-learning technique that lays the groundwork towards achieving collective competence, which forms the core of effective clinical practice. It promotes deep learning and effectively enhances team-work among students, hence creating a more inclusive environment.

OBJECTIVE

Present study was designed to introduce jigsaw model of cooperative learning to early-year undergraduate medical students, measure its effectiveness on their academic performance, and evaluate the perspectives of both students and faculty members regarding the same.

METHODS

It was a mixed method research, involving eighty second-year undergraduate medical students. The jigsaw cooperative learning approach was introduced in two themes within neurosciences module. Students were divided into two equal groups, with one group experiencing typical small-group discussions (SGDs) in first theme and other group exposed to jigsaw approach. The groups were then reversed for second theme. Following the activity, an assessment comprising multiple-choice-questions was conducted to evaluate the impact of jigsaw technique on students' academic performance, with scores from both groups compared. Student perspectives were gathered through self-designed and validated questionnaire, while faculty perceptions were obtained through focus group discussions. Quantitative data were analyzed using SPSS v22, while thematic analysis was performed for qualitative data.

RESULTS

The students of jigsaw group displayed significantly higher median assessment score percentage compared to control group (p = 0.003). Moreover, a significantly greater number of students achieved scores ≥ 60% in jigsaw group compared to control group (p = 0.006). The questionnaire responses indicated a favorable perception of this technique among students, in terms of acceptance, positive interdependence, improvement of interpersonal skills, and comparison with typical SGDs. This technique was also well-perceived within the educational context by faculty members.

CONCLUSION

The jigsaw method is associated with higher levels of academic performance among students when compared to typical small-group discussion. The students and faculty perceived this technique to be an effective cooperative learning strategy in terms of enhanced student engagement, active participation, and a sense of inclusivity.

Investigating the Role of 17β-Estradiol on the Serotonergic System, Targeting Soy Isoflavones as a Strategy to Reduce Menopausal Depression: A Mechanistic Review

Low serotonin is one factor implicated in the development of depression. 17β-estradiol (E2) has been shown to modulate gene expression regulating the neurotransmission of serotonin. Sex hormone levels fluctuate dramatically during the menopausal transition, coinciding with a 14-fold increased risk of depression. This review aimed to examine the effect of soy isoflavones to support decreased and variable E2 levels before and after menopause, linked to an investigation of the pathophysiological mechanisms underlying the protective influence of E2 on the serotonin pathway. The overall aim of this review is to assess the potential of soy isoflavones to reduce depression in middle-aged women. A systematic literature search was performed in three stages. 1,421 papers were screened for relevance to the research aims and objectives. 63 papers were selected based on pre-defined inclusion/exclusion criteria (13 reviews, 24 mechanistic and 26 intervention studies) and critically appraised. Available research supported the hypotheses that E2 increases serotonin synthesis and availability through stimulation of tryptophan hydroxylase-2 (TPH-2) and decreased degradation by monoamine oxidase-A (MAO-A). There was less scientific agreement on the effects of E2 on serotonin transporter (SERT) and serotonin receptors 1 A and 2 A. Studies varied widely on the effectiveness of soy isoflavones in reducing depressive symptoms in (peri)menopausal women. Animal and human studies acknowledge women's increased risk of depression linked to fluctuating E2 rather than absolute levels. However, mechanisms linking E2 variability with depression remain an underrepresented area of research. Study limitations and heterogeneity may contribute to varying results for soy isoflavones and some effects of E2 on the serotonin pathway.

Hereditary gynecologic tumors and precision cancer medicine

Gynecologic cancers are more often caused by genetic factors than other cancers. Genetic testing has become a promising avenue for the prevention, prognosis, and treatment of cancers. This review describes molecular features of gynecologic tumors linked to hereditary syndromes, gives an overview of the current state of clinical management, and clarifies the role of gynecology in the treatment of hereditary tumors. Typical hereditary gynecologic tumors include hereditary breast and ovarian cancer, Lynch syndrome, Peutz-Jeghers syndrome, and Cowden syndrome. Multigene panel testing, which analyzes a preselected subset of genes for genetic variants, has recently become the first-choice test because it can provide more accurate risk assessment than a single test. Furthermore, comprehensive genomic cancer profiling enables personalized cancer treatment and aids in germline findings.

Genome-wide comparative analyses of GATA transcription factors among 19 Arabidopsis ecotype genomes: Intraspecific characteristics of GATA transcription factors

GATA transcription factors (TFs) are widespread eukaryotic regulators whose DNA-binding domain is a class IV zinc finger motif (CX2CX17-20CX2C) followed by a basic region. Due to the low cost of genome sequencing, multiple strains of specific species have been sequenced: e.g., number of plant genomes in the Plant Genome Database (http://www.plantgenome.info/) is 2,174 originated from 713 plant species. Thus, we investigated GATA TFs of 19 Arabidopsis thaliana genome-widely to understand intraspecific features of Arabidopsis GATA TFs with the pipeline of GATA database (http://gata.genefamily.info/). Numbers of GATA genes and GATA TFs of each A. thaliana genome range from 29 to 30 and from 39 to 42, respectively. Four cases of different pattern of alternative splicing forms of GATA genes among 19 A. thaliana genomes are identified. 22 of 2,195 amino acids (1.002%) from the alignment of GATA domain amino acid sequences display variations across 19 ecotype genomes. In addition, maximally four different amino acid sequences per each GATA domain identified in this study indicate that these position-specific amino acid variations may invoke intraspecific functional variations. Among 15 functionally characterized GATA genes, only five GATA genes display variations of amino acids across ecotypes of A. thaliana, implying variations of their biological roles across natural isolates of A. thaliana. PCA results from 28 characteristics of GATA genes display the four groups, same to those defined by the number of GATA genes. Topologies of bootstrapped phylogenetic trees of Arabidopsis chloroplasts and common GATA genes are mostly incongruent. Moreover, no relationship between geographical distribution and their phylogenetic relationships was found. Our results present that intraspecific variations of GATA TFs in A. thaliana are conserved and evolutionarily neutral along with 19 ecotypes, which is congruent to the fact that GATA TFs are one of the main regulators for controlling essential mechanisms, such as seed germination and hypocotyl elongation.

Identification and Classification of Rare Variants in NPC1 and NPC2 in Quebec

Niemann–Pick disease type C (NPC) is a treatable autosomal recessive neurodegenerative condition which leads to a variety of progressive manifestations. Despite most cases being diagnosed at a young age, disease prevalence may be underestimated, especially in adults, and interpretation of NPC1 and NPC2 variants can be difficult. This study aims to identify potential pathogenic variants in a large cohort of healthy individuals and classify their risk of pathogenicity to assist with future interpretation of variants. The CARTaGENE (CaG) cohort was used to identify possible variants of NPC1 and NPC2. Nine-hundred and eleven RNA samples and 198 exome sequencing were screened for genetic variants through a bio-informatic pipeline performing alignment and variant calling. The identified variants were analyzed using annotations for allelic frequency, pathogenicity and conservation scores. The ACMG guidelines were used to classify the variants. These were then compared to existing databases and previous studies of NPC prevalence, including the Tübingen NPC database. Thirty-two distinct variants were identified after running the samples in the RNA-sequencing pipeline, two of which were classified as pathogenic and 21 of which were not published previously. Furthermore, 46 variants were both identified in our population and with the Tübingen database, the majority of which were of uncertain significance. Ten additional variants were found in our exome-sequencing sample. This study of a sample from a population living in Quebec demonstrates a variety of rare variants, some of which were already described in the literature as well as some novel variants. Classifying these variants is arduous given the scarcity of available literature, even so in a population of healthy individuals. Yet using this data, we were able to identify two pathogenic variants within our population and several new variants not previously identified.

Trends in the characteristics of human functional genomic data on the gene expression omnibus, 2001-2017

The gene expression omnibus (GEO) is the world's largest public repository of functional genomic data. Despite its broad use in secondary genomic analyses, the temporal trends in the characteristics of genomic data on GEO, including experimental procedures, geographic origin, funder(s), and related disease, have not been examined. We identified 75,376 Series deposited to the GEO during 2001-2017 and built a database of all human genomic data (39,076 Series, 51.8% of all Series). Using the associated publications, we obtained funding information and identified the related disease area. Of the Series with classified disease areas, the two most common were cancer (n = 12,688, 32.5%) and immunologic diseases (n = 2,393, 6.1%), while the percentages of all other disease areas were below 5%, including neurological diseases (n = 1733, 4.4%), infectious diseases (n = 1225, 3.1%), diabetes (n = 828, 2.1%), and cardiovascular diseases (n = 299, 0.8%). In recent years, there has been a significant increase in the use of high-throughput sequencing (HTS), protein array and multiple-platform technologies, as well as in the proportion of North American deposits. Compared to those from other regions, North American deposits appeared to lead the shift from array-based to HTS technologies (odds ratio [OR], 95% confidence intervals [CI] = 3.39, 3.23-3.55, P = 9.40E-323), and were less likely to focus on a major disease area (OR = 0.64, 95% CI: 0.61-0.67, P = 5.02E-107), suggesting a greater emphasis on basic science in North America. Furthermore, the Series utilizing HTS were less likely to be disease-classified compared to other technologies (OR = 0.39, 95% CI: 0.37-0.41, P = 1.00E-322), suggesting a preferential use or adoption of HTS in basic science settings. Finally, funding from the NHGRI, NCI, NIEHS, and NCCR resulted in a higher number of GEO Series per grant than other NIH institutes, demonstrating different preferences on genomic studies among awardees of NIH institutes. Our findings demonstrate geographic, technological, and funding disparities in the trends of GEO deposit characteristics.

Omics-based molecular techniques in oral pathology centred cancer: prospect and challenges in Africa

BACKGROUND

The completion of the human genome project and the accomplished milestones in the human proteome project; as well as the progress made so far in computational bioinformatics and "big data" processing have contributed immensely to individualized/personalized medicine in the developed world.

MAIN BODY

At the dawn of precision medicine, various omics-based therapies and bioengineering can now be applied accurately for the diagnosis, prognosis, treatment, and risk stratification of cancer in a manner that was hitherto not thought possible. The widespread introduction of genomics and other omics-based approaches into the postgraduate training curriculum of diverse medical and dental specialties, including pathology has improved the proficiency of practitioners in the use of novel molecular signatures in patient management. In addition, intricate details about disease disparity among different human populations are beginning to emerge. This would facilitate the use of tailor-made novel theranostic methods based on emerging molecular evidences.

CONCLUSION

In this review, we examined the challenges and prospects of using currently available omics-based technologies vis-à-vis oral pathology as well as prompt cancer diagnosis and treatment in a resource limited setting.

Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria

PurposeThe 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification.MethodsWe undertook an iterative process of refining the ACMG-AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions.ResultsOur implementation: (i) separated ambiguous ACMG-AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the "clinical criteria" style of the guidelines with additive, semiquantitative criteria.ConclusionSherloc builds on the strong framework of 33 rules established by the ACMG-AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/.

DMET-analyzer: automatic analysis of Affymetrix DMET data

Background

Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs.

Results

We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of clinical datasets produced in the University Hospital of Catanzaro, Italy.

Conclusion

DMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies. Using such tool user may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment. Moreover annotations and the direct link to external databases may increase the biological knowledge extracted. The system is freely available for academic purposes at: https://sourceforge.net/projects/dmetanalyzer/files/

Molecular diagnostics: the changing culture of medical microbiology

Diagnostic molecular biology is arguably the fastest growing area in current laboratory-based medicine. Growth of the so called 'omics' technologies has, over the last decade, led to a gradual migration away from the 'one test, one pathogen' paradigm, toward multiplex approaches to infectious disease diagnosis, which have led to significant improvements in clinical diagnostics and ultimately improved patient care.

The search for reliable biomarkers of disease in multiple chemical sensitivity and other environmental intolerances

Whilst facing a worldwide fast increase of food and environmental allergies, the medical community is also confronted with another inhomogeneous group of environment-associated disabling conditions, including multiple chemical sensitivity (MCS), fibromyalgia, chronic fatigue syndrome, electric hypersensitivity, amalgam disease and others. These share the features of poly-symptomatic multi-organ cutaneous and systemic manifestations, with postulated inherited/acquired impaired metabolism of chemical/physical/nutritional xenobiotics, triggering adverse reactions at exposure levels far below toxicologically-relevant values, often in the absence of clear-cut allergologic and/or immunologic involvement. Due to the lack of proven pathogenic mechanisms generating measurable disease biomarkers, these environmental hypersensitivities are generally ignored by sanitary and social systems, as psychogenic or "medically unexplained symptoms". The uncontrolled application of diagnostic and treatment protocols not corresponding to acceptable levels of validation, safety, and clinical efficacy, to a steadily increasing number of patients demanding assistance, occurs in many countries in the absence of evidence-based guidelines. Here we revise available information supporting the organic nature of these clinical conditions. Following intense research on gene polymorphisms of phase I/II detoxification enzyme genes, so far statistically inconclusive, epigenetic and metabolic factors are under investigation, in particular free radical/antioxidant homeostasis disturbances. The finding of relevant alterations of catalase, glutathione-transferase and peroxidase detoxifying activities significantly correlating with clinical manifestations of MCS, has recently registered some progress towards the identification of reliable biomarkers of disease onset, progression, and treatment outcomes.

Data standards for Omics data: the basis of data sharing and reuse

To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data.

Nutrigenomics and nutrigenetics

The nutrients are able to interact with molecular mechanisms and modulate the physiological functions in the body. The Nutritional Genomics focuses on the interaction between bioactive food components and the genome, which includes Nutrigenetics and Nutrigenomics. The influence of nutrients on f genes expression is called Nutrigenomics, while the heterogeneous response of gene variants to nutrients, dietary components and developing nutraceticals is called Nutrigenetics. Genetic variation is known to affect food tolerances among human subpopulations and may also influence dietary requirements and raising the possibility of individualizing nutritional intake for optimal health and disease prevention on the basis of an individual's genome. Nutrigenomics provides a genetic understanding for how common dietary components affect the balance between health and disease by altering the expression and/or structure of an individual's genetic makeup. Nutrigenetics describes that the genetic profile have impact on the response of body to bioactive food components by influencing their absorption, metabolism, and site of action.In this way, considering different aspects of gene-nutrient interaction and designing appropriate diet for every specific genotype that optimize individual health, diagnosis and nutritional treatment of genome instability, we could prevent and control conversion of healthy phenotype to diseases.