Sequencing your genome: what does it mean?

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Targeted Protein Degraders- The Druggability Perspective

Targeted Protein degraders (TPDs) show promise in harnessing cellular machinery to eliminate disease-causing proteins, even those previously considered undruggable. Especially if protein turnover is low, targeted protein removal bestows lasting therapeutic effect over typical inhibition. The demonstrated safety and efficacy profile of clinical candidates has fueled the surge in the number of potential candidates across different therapeutic areas. As TPDs often do not comply with Lipinski's rule of five, developing novel TPDs and unlocking their full potential requires overcoming solubility, permeability and oral bioavailability challenges. Tailored in-vitro assays are key to precise profiling and optimization, propelling breakthroughs in targeted protein degradation.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

Identification and expression analysis of lncRNA in seven organs of Rhinopithecus roxellana

Long non-coding RNA (lncRNA) represents a new direction to identify expression profiles and regulatory mechanisms in various organisms. Here, we report the first dataset of lncRNAs of the golden snub-nosed monkey (GSM), including 12,557 putative lncRNAs identified from seven organs. Compared with mRNA, GSM lncRNA had fewer exons and isoforms, and longer length. LncRNA showed more obvious tissue-specific expression than mRNA. However, for the top ten most abundant genes in each organ, mRNAs expression was more tissue-specific than lncRNAs. By identification of specifically expressed lncRNAs and mRNAs in each organ, it indicates that the expression of SEG-lncRNA (specifically expressed lncRNA) and SEG-mRNA (specifically expressed mRNA) had high correlation. In particular, combined our lncRNA and mRNA data, we identified 92 heart SEG-lncRNAs targeted ten mRNA genes in the oxidative phosphorylation pathway and upregulated the expression of these target genes such as ND4, ATP6, and ATP8. These may contribute to GSM adaption to its high-elevation environment. We also identified 171 liver SEG-lncRNAs, which targeted 27 genes associated with the metabolism of xenobiotics and leaded to high expression of these target genes in liver. These lncRNAs may play important roles in GSM adaptation to a folivory diet.

Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

Clinical Genetic Screening in Adult Patients with Kidney Disease

Expanded accessibility of genetic sequencing technologies, such as chromosomal microarray and massively parallel sequencing approaches, is changing the management of hereditary kidney diseases. Genetic causes account for a substantial proportion of pediatric kidney disease cases, and with increased utilization of diagnostic genetic testing in nephrology, they are now also detected at appreciable frequencies in adult populations. Establishing a molecular diagnosis can have many potential benefits for patient care, such as guiding treatment, familial testing, and providing deeper insights on the molecular pathogenesis of kidney diseases. Today, with wider clinical use of genetic testing as part of the diagnostic evaluation, nephrologists have the challenging task of selecting the most suitable genetic test for each patient, and then applying the results into the appropriate clinical contexts. This review is intended to familiarize nephrologists with the various technical, logistical, and ethical considerations accompanying the increasing utilization of genetic testing in nephrology care.

High Prevalence of Connective Tissue Gene Variants in Professional Ballet

BACKGROUND

There is a high prevalence of hypermobility spectrum disorder (HSD) in dancers. While there is no known genetic variant for HSD, hypermobile Ehlers-Danlos syndrome is a genetic disorder that exists within HSD. There are many connective tissue disorders (CTDs) with known (and unknown) genes associated with hypermobility. Hypermobility has distinct advantages for participation in flexibility sports, including ballet.

PURPOSE

To determine the prevalence of gene variants associated with hypermobility in a large professional ballet company.

STUDY DESIGN

Cross-sectional study; Level of evidence, 3.

METHODS

In this cross-sectional investigation, 51 professional male and female dancers from a large metropolitan ballet company were eligible and offered participation after an oral and written informed consent process. Whole blood was obtained from peripheral venipuncture, and DNA was isolated. Isolated DNA was subsequently enriched for the coding exons of 60 genes associated with CTD that included hypermobility as a phenotype, including Ehlers-Danlos syndromes, osteogenesis imperfecta, Marfan syndrome, and others. Genes were targeted with hybrid capture technology. Prepared DNA libraries were then sequenced with next-generation sequencing technology. Genetic database search tools (Human Gene Mutation Database and e!Ensembl, http://useast.ensembl.org/ ) were used to query specific variants. Descriptive statistics were calculated.

RESULTS

Of 51 dancers, 32 (63%) agreed to participate in DNA analysis (mean ± SD age, 24.3 ± 4.4 years; 18 men, 14 women). Twenty-eight dancers had at least 1 variant in the 60 genes tested, for an 88% prevalence. A total of 80 variants were found. A variant in 26 of the 60 genes was found in at least 1 dancer. Among the 28 dancers with variants, 16 were found in the TTN gene; 10 in ZNF469; 5 in RYR1; 4 in COL12A1; 3 in ABCC6 and COL6A2; 2 in ADAMTS2, CBS, COL1A2, COL6A3, SLC2A10, TNC, and TNXB; and 1 in ATP6V0A2, B4GALT7, BMP1, COL11A1, COL5A2, COL6A1, DSE, FBN1, FBN2, NOTCH1, PRDM5, SMAD3, and TGFBR1. Nine variants found in this population have never been reported. No identified variant was identical to any other variant. No identified variant was known to be disease causing. In the general population, the prevalence of each variant ranges from never reported to 0.33%. In the study population, the prevalence of each variant was 3.13%. There was no association between hypermobility scores and genetic variants.

CONCLUSION

Genetic variants in CTD-associated genes are highly prevalent (88%) in professional ballet dancers. This may significantly account for the high degree of motion in this population.

Emerging roles of histone modifications and HDACs in RNA splicing

Histone modifications and RNA splicing, two seemingly unrelated gene regulatory processes, greatly increase proteome diversity and profoundly influence normal as well as pathological eukaryotic cellular functions. Like many histone modifying enzymes, histone deacetylases (HDACs) play critical roles in governing cellular behaviors and are indispensable in numerous biological processes. While the association between RNA splicing and histone modifications is beginning to be recognized, a lack of knowledge exists regarding the role of HDACs in splicing. Recent studies however, reveal that HDACs interact with spliceosomal and ribonucleoprotein complexes, actively control the acetylation states of splicing-associated histone marks and splicing factors, and thereby unexpectedly could modulate splicing. Here, we review the role of histone/protein modifications and HDACs in RNA splicing and discuss the convergence of two parallel fields, which supports the argument that HDACs, and perhaps most histone modifying enzymes, are much more versatile and far more complicated than their initially proposed functions. Analogously, an HDAC-RNA splicing connection suggests that splicing is regulated by additional upstream factors and pathways yet to be defined or not fully characterized. Some human diseases share common underlying causes of aberrant HDACs and dysregulated RNA splicing and, thus, further support the potential link between HDACs and RNA splicing.

Pharmacogenetics: Role of Single Nucleotide Polymorphisms

Genome sequencing methods have basically similar algorithms, although they show a few differences between the platforms. The human genome contains approximately three billion base pairs, and this amount is huge and therefore impossible to sequence at one step. However, this amount is not a problem for developed technology. Researchers break DNA into small random pieces and then sequence and reassemble. Library preparation, sequencing, bioinformatic approaches and reporting. High-quality library preparation is critical and the most important part of the next-generation sequencing workflow. Successful sequencing directly requires high-quality libraries. Sequencing is second step and all high-throughput sequencing approaches are generally based on conventional Sanger sequencing. After preparation of library and sequencing, later steps are completely computer-based (in silico) approaches called as bioinformatics.

Aptamer Bioinformatics

Aptamers are short nucleic acid sequences capable of specific, high-affinity molecular binding. They are isolated via SELEX (Systematic Evolution of Ligands by Exponential Enrichment), an evolutionary process that involves iterative rounds of selection and amplification before sequencing and aptamer characterization. As aptamers are genetic in nature, bioinformatic approaches have been used to improve both aptamers and their selection. This review will discuss the advancements made in several enclaves of aptamer bioinformatics, including simulation of aptamer selection, fragment-based aptamer design, patterning of libraries, identification of lead aptamers from high-throughput sequencing (HTS) data and in silico aptamer optimization.

Unsolved challenges in pediatric whole-exome sequencing: A literature analysis

Whole-exome sequencing (WES) has been instrumental in the discovery of novel genes and mechanisms causing Mendelian diseases. While this technology is now being successfully applied in a number of clinics, particularly to diagnose patients with rare diseases, it also raises a number of ethical, legal and social issues. In order to identify what challenges were directly foreseen by technology users, we performed a systematic review of the literature. In this paper, we focus on recent publications related to the use of WES in the pediatric context and analyze the most prominent challenges raised by technology users. This is particularly relevant considering that a) most patients currently undergoing testing using WES to identify the genetic basis for rare diseases are children and b) their lack of capacity to consent for themselves makes them a vulnerable population and generates the need for specific ethical, legal and regulatory procedures. We identified key challenges that related to four main categories: (1) intake; (2) sequence production and analysis; (3) reporting of results and counseling considerations and (4) collaborative data interpretation and data sharing. We then contextualize these challenges in light of the recent recommendations and guidelines, published by professional societies that have significant potential to impact the field.

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.

Unsolved challenges of clinical whole-exome sequencing: a systematic literature review of end-users' views

BACKGROUND

Whole-exome sequencing (WES) consists in the capture, sequencing and analysis of all exons in the human genome. Originally developed in the research context, this technology is now increasingly used clinically to inform patient care. The implementation of WES into healthcare poses significant organizational, regulatory, and ethical hurdles, which are widely discussed in the literature.

METHODS

In order to inform future policy decisions on the integration of WES into standard clinical practice, we performed a systematic literature review to identify the most important challenges directly reported by technology users.

RESULTS

Out of 2094 articles, we selected and analyzed 147 which reported a total of 23 different challenges linked to the production, analysis, reporting and sharing of patients' WES data. Interpretation of variants of unknown significance, incidental findings, and the cost and reimbursement of WES-based tests were the most reported challenges across all articles.

CONCLUSIONS

WES is already used in the clinical setting, and may soon be considered the standard of care for specific medical conditions. Yet, technology users are calling for certain standards and guidelines to be published before this technology replaces more focused approaches such as gene panels sequencing. In addition, a number of infrastructural adjustments will have to be made for clinics to store, process and analyze the amounts of data produced by WES.

Proteome-wide drug screening using mass spectrometric imaging of bead-arrays

A fundamental challenge in the drug discovery process is to develop compounds with high efficacy and minimal side-effects. We describe a new approach to proteome-wide drug screening for detection of on- and off-target binding which combines the advantages of mass spectrometry with microarray technology. The method involves matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) of agarose micro-beads randomly arrayed at high-density in custom micro-well plates. Each bead carries a unique protein target and a corresponding photocleavable mass-tag for coding (PC-Mass-Tag). Compounds bound to specific protein beads and a photo-released coding PC-Mass-Tag are detected simultaneously using MALDI-MSI. As an initial demonstration of this approach, two kinase-targeted drugs, Dasatinib and Brigatinib (AP26113), were simultaneously screened against a model 50-member kinase-bead library. A MALDI-MSI scan performed at the equivalent density of 495,000 beads in the footprint of a microscope slide yielded 100% sensitivity for detecting known strong interactions with no false positives.

Only one health, and so many omics

The development of new approaches based on wide profiling methods in studying biological and medical systems is bringing large amounts of data on a daily basis. The causes of complex diseases have been directed to the genome examination bringing formidable knowledge. We can study genome, but also proteome, exome, transcriptome, epigenome, metabolome, and newcomers too such as microbiome, connectome and exposome. The title of this editorial is paraphrasing the famous saying of Victor Schlichter from Buenos Aires children hospital in Argentina who said "How unfair! Only one health, and so many diseases". Today there is indeed a whole lot of omics. We think that we are lucky to have all the omics possible, but we also wanted to stress the importance of future holistic approach in integrating the knowledge omics has rewarded us.