Personal genomes, quantitative dynamic omics and personalized medicine

Structural peculiarities of tandem repeats and their clinical significance

While it is well established that a mere 2% of human DNA nucleotides are involved in protein coding, the remainder of the DNA plays a vital role in the preservation of normal cellular genetic function. A significant proportion of tandem repeats (TRs) are present in non-coding DNA. TRs - specific sequences of nucleotides that entail numerous repetitions of a given fragment. In this study, we employed our novel algorithm grounded in finite automata theory, which we refer to as Dafna, to investigate for the first time the likelihood of these nucleotide sequences forming non-canonical DNA structures (NS). Such structures include G-quadruplexes, i-motifs, hairpins, and triplexes. The tandem repeats under consideration in our research encompassed sequences containing 1 to 6 nucleotides per repeated fragment. For comparison, we employed a set of randomly generated sequences of the same length (60 nucleotides) as a benchmark. The outcomes of our research exposed a disparity between the potential for NS formation in random sequences and tandem repeats. Our findings affirm that the propensity of DNA and RNA to form NS is closely tied to various genetic disorders, including Huntington's disease, Fragile X syndrome, and Friedreich's ataxia. In the concluding discussion, we present a proposal for a new therapeutic mechanism to address these diseases. This novel approach revolves around the ability of specific nucleic acid fragments to form multiple types of NS.

CYBA as a Potential Biomarker for Renal Cell Carcinoma: Evidence from an Integrated Genetic Analysis

BACKGROUND/AIM

Oxidative stress plays an important role in various pathogenic processes, and disruption in the coordinated production of NADPH oxidase (NOX)-derived reactive oxygen species has been associated with carcinogenesis. However, little is known about whether genetic variants in NOX can contribute to the development of renal cell carcinoma (RCC).

PATIENTS AND METHODS

This study aimed to bridge this knowledge gap by analysing the association of 10 single-nucleotide polymorphisms in the phagocyte NOX genes, CYBA and CYBB, with RCC risk and tumour characteristics in 630 RCC patients and controls. Differential gene expression and patient prognosis analyses were performed using gene expression data obtained from public databases.

RESULTS

Multivariate analysis and multiple testing corrections revealed the A allele of rs7195830 in CYBA to be a significant risk allele for RCC, compared to the G allele [odds ratio (OR)=1.70, 95% confidence interval (CI)=1.27-2.26, p<0.001]. A pooled analysis of 17 renal cancer gene expression datasets revealed a higher CYBA expression in RCC than in normal tissues. Moreover, high CYBA expression was associated with advanced tumour characteristics and worse patient prognosis.

CONCLUSION

CYBA might play an oncogenic role in RCC and serve as a predictive indicator of patient prognosis.

Single Nucleotide Variants Associated with Colorectal Cancer Among Iranian Patients: A Narrative Review

Colorectal cancer has been considered as one of the complicated multi-stage processes after adenoma-carcinoma sequence. Therefore, studies of the molecular dysregulation basis could present information on the recognition of the potent biomarkers and treatment targets for this disease. Even though outcomes of the patients with colorectal cancer have been improved largely with current annual screening plans, it is necessary to have reliable prognostic biomarkers because of the disease heterogeneity. There is a significant relationship between SNP in IL1RN* 2 (IL1ra), −509 C/T (TGFB1), rs11556218 T>G and rs4778889 T/C (IL16), miRNA-binding site polymorphisms in IL16, rs4464148 (SMAD7), rs6983267 (EGF), GSTT1, TACG haplotype (CTLA4), 1793G> A (MTHFR), Leu/Leu genotype of (EXO1), −137 G/C (IL18), C/T genotype (XRCC3), I3434T (XRCC7), MGMT, C3435T (MDR1), ff genotype of FokI, 677CT+TT (MTHFR), G2677T/A (MDR1) and CRC. Increased risk has been observed in VDR ApaI genotype “aa”. Finally, the protective effect has been explored in the TACA haplotype (CTLA4). According to the findings, the genetic polymorphisms in the immunity-associated genes are related to the CRC amongst the Iranian patients. Therefore, more large-scale functional investigations are necessary for confirming the results.

Sivasubbu S, Scaria V. Genomics of rare genetic diseases-experiences from India

Home to a culturally heterogeneous population, India is also a melting pot of genetic diversity. The population architecture characterized by multiple endogamous groups with specific marriage patterns, including the widely prevalent practice of consanguinity, not only makes the Indian population distinct from rest of the world but also provides a unique advantage and niche to understand genetic diseases. Centuries of genetic isolation of population groups have amplified the founder effects, contributing to high prevalence of recessive alleles, which translates into genetic diseases, including rare genetic diseases in India.Rare genetic diseases are becoming a public health concern in India because a large population size of close to a billion people would essentially translate to a huge disease burden for even the rarest of the rare diseases. Genomics-based approaches have been demonstrated to accelerate the diagnosis of rare genetic diseases and reduce the socio-economic burden. The Genomics for Understanding Rare Diseases: India Alliance Network (GUaRDIAN) stands for providing genomic solutions for rare diseases in India. The consortium aims to establish a unique collaborative framework in health care planning, implementation, and delivery in the specific area of rare genetic diseases. It is a nation-wide collaborative research initiative catering to rare diseases across multiple cohorts, with over 240 clinician/scientist collaborators across 70 major medical/research centers. Within the GUaRDIAN framework, clinicians refer rare disease patients, generate whole genome or exome datasets followed by computational analysis of the data for identifying the causal pathogenic variations. The outcomes of GUaRDIAN are being translated as community services through a suitable platform providing low-cost diagnostic assays in India. In addition to GUaRDIAN, several genomic investigations for diseased and healthy population are being undertaken in the country to solve the rare disease dilemma.In summary, rare diseases contribute to a significant disease burden in India. Genomics-based solutions can enable accelerated diagnosis and management of rare diseases. We discuss how a collaborative research initiative such as GUaRDIAN can provide a nation-wide framework to cater to the rare disease community of India.

Longitudinal personal DNA methylome dynamics in a human with a chronic condition

Epigenomics regulates gene expression and is as important as genomics in precision personal health, as it is heavily influenced by environment and lifestyle. We profiled whole-genome DNA methylation and the corresponding transcriptome of peripheral blood mononuclear cells collected from a human volunteer over a period of 36 months, generating 28 methylome and 57 transcriptome datasets. We found that DNA methylomic changes are associated with infrequent glucose level alteration, whereas the transcriptome underwent dynamic changes during events such as viral infections. Most DNA meta-methylome changes occurred 80-90 days before clinically detectable glucose elevation. Analysis of the deep personal methylome dataset revealed an unprecedented number of allelic differentially methylated regions that remain stable longitudinally and are preferentially associated with allele-specific gene regulation. Our results revealed that changes in different types of 'omics' data associate with different physiological aspects of this individual: DNA methylation with chronic conditions and transcriptome with acute events.

Precision medicine in diabetes: an opportunity for clinical translation

Metabolic disorders present a public health challenge of staggering proportions. In diabetes, there is an urgent need to better understand disease heterogeneity, clinical trajectories, and related comorbidities. A pressing and timely question is whether we are ready for precision medicine in diabetes. Some biological insights that have emerged during the last decade have already been used to direct clinical decision making, especially in monogenic forms of diabetes. However, much work is necessary to integrate high-dimensional explorations into complex disease architectures, less penetrant biological alterations, and broader phenotypes, such as type 2 diabetes. In addition, for precision medicine to take hold in diabetes, reproducibility, interpretability, and actionability remain key guiding objectives. In this review, we examine how mounting data sets generated during the last decade to understand biological variability are now inspiring new venues to clarify diabetes nosology and ultimately translate findings into more effective prevention and treatment strategies.

MathIOmica: An Integrative Platform for Dynamic Omics

Multiple omics data are rapidly becoming available, necessitating the use of new methods to integrate different technologies and interpret the results arising from multimodal assaying. The MathIOmica package for Mathematica provides one of the first extensive introductions to the use of the Wolfram Language to tackle such problems in bioinformatics. The package particularly addresses the necessity to integrate multiple omics information arising from dynamic profiling in a personalized medicine approach. It provides multiple tools to facilitate bioinformatics analysis, including importing data, annotating datasets, tracking missing values, normalizing data, clustering and visualizing the classification of data, carrying out annotation and enumeration of ontology memberships and pathway analysis. We anticipate MathIOmica to not only help in the creation of new bioinformatics tools, but also in promoting interdisciplinary investigations, particularly from researchers in mathematical, physical science and engineering fields transitioning into genomics, bioinformatics and omics data integration.

Use of the "Exposome" in the Practice of Epidemiology: A Primer on -Omic Technologies

The exposome has been defined as the totality of exposures individuals experience over the course of their lives and how those exposures affect health. Three domains of the exposome have been identified: internal, specific external, and general external. Internal factors are those that are unique to the individual, and specific external factors include occupational exposures and lifestyle factors. The general external domain includes sociodemographic factors such as educational level and financial status. Eliciting information on the exposome is daunting and not feasible at present; the undertaking may never be fully realized. A variety of tools have been identified to measure the exposome. Biomarker measurements will be one of the major tools in exposomic studies. However, exposure data can also be obtained from other sources such as sensors, geographic information systems, and conventional tools such as survey instruments. Proof-of-concept studies are being conducted that show the promise of exposomic investigation and the integration of different kinds of data. The inherent value of exposomic data in epidemiologic studies is that they can provide greater understanding of the relationships among a broad range of chemical and other risk factors and health conditions and ultimately lead to more effective and efficient disease prevention and control.

A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia

We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.

BioDB extractor: customized data extraction system for commonly used bioinformatics databases

Background

Diverse types of biological data, primary as well as derived, are available in various formats and are stored in heterogeneous resources. Database-specific as well as integrated search engines are available for carrying out efficient searches of databases. These search engines however, do not support extraction of subsets of data with the same level of granularity that exists in typical database entries. In order to extract fine grained subsets of data, users are required to download complete or partial database entries and write scripts for parsing and extraction.

Results

BioDBExtractor (BDE) has been developed to provide 26 customized data extraction utilities for some of the commonly used databases such as ENA (EMBL-Bank), UniprotKB, PDB, and KEGG. BDE eliminates the need for downloading entries and writing scripts. BDE has a simple web interface that enables input of query in the form of accession numbers/ID codes, choice of utilities and selection of fields/subfields of data by the users.

Conclusions

BDE thus provides a common data extraction platform for multiple databases and is useful to both, novice and expert users. BDE, however, is not a substitute to basic keyword-based database searches. Desired subsets of data, compiled using BDE can be subsequently used for downstream processing, analyses and knowledge discovery.

Availability

BDE can be accessed from http://bioinfo.net.in/BioDB/Home.html.

Intronic polymorphisms of the SMAD7 gene in association with colorectal cancer

Based on genome-wide association studies (GWAS) a linkage between several variants such as single nucleotide polymorphisms (SNPs) in intron 3 of SMAD7 (mothers against decapentaplegic homolog7) were, rs12953717, rs4464148 and rs4939827 has been noted for susceptibility to colorectal cancer (CRC). In this study we investigated the relationship of rs12953717 and rs4464148 with risk of CRC among 487 Iranian individuals based on a case- control study. Genotyping of SNPs was performed by PCR-RFLP and for confirming the outcomes, 10% of genotyping cases were sequenced with RFLP. Comparing the case and control group, we have found significant association between the rs4464148 SNP and lower risk of CRC. The AG genotype showed decreased risk with and odds ratio of 0.635 (adjusted OR=0.635, 95% CI: 0.417-0.967, p=0.034). There was no significant difference in the distribution of SMAD7 gene rs12953717 TT genotype between two groups of the population evaluated (adjusted OR=1.604, 95% CI: 0.978-2.633, p=0.061). On the other hand, rs12953717 T allele showed a statistically significant association with CRC risk (adjusted OR=1.339, 95% CI: 1.017-1.764, p=0.037). In conclusion, we found a significant association between CRC risk and the rs4464148 AG genotype. Furthermore, the rs12953717 T allele may act as a risk factor. This association may be caused by alternative splicing of pre mRNA. Although we observed a strong association with rs4464148 GG genotype in affected women, we did not detect the same association in CRC male patients.

Metabolome progression during early gut microbial colonization of gnotobiotic mice

The microbiome has been implicated directly in host health, especially host metabolic processes and development of immune responses. These are particularly important in infants where the gut first begins being colonized, and such processes may be modeled in mice. In this investigation we follow longitudinally the urine metabolome of ex-germ-free mice, which are colonized with two bacterial species, Bacteroides thetaiotaomicron and Bifidobacterium longum. High-throughput mass spectrometry profiling of urine samples revealed dynamic changes in the metabolome makeup, associated with the gut bacterial colonization, enabled by our adaptation of non-linear time-series analysis to urine metabolomics data. Results demonstrate both gradual and punctuated changes in metabolite production and that early colonization events profoundly impact the nature of small molecules circulating in the host. The identified small molecules are implicated in amino acid and carbohydrate metabolic processes, and offer insights into the dynamic changes occurring during the colonization process, using high-throughput longitudinal methodology.

Computational cancer biology: education is a natural key to many locks

Background

Oncology is a field that profits tremendously from the genomic data generated by high-throughput technologies, including next-generation sequencing. However, in order to exploit, integrate, visualize and interpret such high-dimensional data efficiently, non-trivial computational and statistical analysis methods are required that need to be developed in a problem-directed manner.

Discussion

For this reason, computational cancer biology aims to fill this gap. Unfortunately, computational cancer biology is not yet fully recognized as a coequal field in oncology, leading to a delay in its maturation and, as an immediate consequence, an under-exploration of high-throughput data for translational research.

Summary

Here we argue that this imbalance, favoring ’wet lab-based activities’, will be naturally rectified over time, if the next generation of scientists receives an academic education that provides a fair and competent introduction to computational biology and its manifold capabilities. Furthermore, we discuss a number of local educational provisions that can be implemented on university level to help in facilitating the process of harmonization.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.

Whole-exome sequencing identifies tetratricopeptide repeat domain 7A (TTC7A) mutations for combined immunodeficiency with intestinal atresias

BACKGROUND

Combined immunodeficiency with multiple intestinal atresias (CID-MIA) is a rare hereditary disease characterized by intestinal obstructions and profound immune defects.

OBJECTIVE

We sought to determine the underlying genetic causes of CID-MIA by analyzing the exomic sequences of 5 patients and their healthy direct relatives from 5 unrelated families.

METHODS

We performed whole-exome sequencing on 5 patients with CID-MIA and 10 healthy direct family members belonging to 5 unrelated families with CID-MIA. We also performed targeted Sanger sequencing for the candidate gene tetratricopeptide repeat domain 7A (TTC7A) on 3 additional patients with CID-MIA.

RESULTS

Through analysis and comparison of the exomic sequence of the subjects from these 5 families, we identified biallelic damaging mutations in the TTC7A gene, for a total of 7 distinct mutations. Targeted TTC7A gene sequencing in 3 additional unrelated patients with CID-MIA revealed biallelic deleterious mutations in 2 of them, as well as an aberrant splice product in the third patient. Staining of normal thymus showed that the TTC7A protein is expressed in thymic epithelial cells, as well as in thymocytes. Moreover, severe lymphoid depletion was observed in the thymus and peripheral lymphoid tissues from 2 patients with CID-MIA.

CONCLUSIONS

We identified deleterious mutations of the TTC7A gene in 8 unrelated patients with CID-MIA and demonstrated that the TTC7A protein is expressed in the thymus. Our results strongly suggest that TTC7A gene defects cause CID-MIA.