Multiple omics levels of chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is a lymphoproliferative malignancy characterized by the proliferation of functionally mature but incompetent B cells. It is the most prevalent type of leukemia in Western populations, accounting for approximately 25% of new leukemia cases. While recent advances, such as ibrutinib and venetoclax treatment have improved patient outlook, aggressive forms of CLL such as Richter transformation still pose a significant challenge. This discrepancy may be due to the heterogeneity of factors contributing to CLL development at multiple -omics levels. However, information on the omics of CLL is fragmented, hindering multi-omics-based research into potential treatment options. To address this, we aggregated and presented a selection of important aspects of various omics levels of the disease in this review. The purpose of the present literature analysis is to portray examples of CLL studies from different omics levels, including genomics, epigenomics, transcriptomics, epitranscriptomics, proteomics, epiproteomics, metabolomics, glycomics and lipidomics, as well as those identified by multi-omics approaches. The review includes the list of 102 CLL-associated genes with relevant genomics information. While single-omics studies yield substantial and useful data, they omit a significant level of complex biological interplay present in the disease. As multi-omics studies integrate several different layers of data, they may be better suited for complex diseases such as CLL and have thus far yielded promising results. Future multi-omics studies may assist clinicians in improved treatment choices based on CLL subtypes as well as allow the identification of novel biomarkers and targets for treatments.

Whole genome sequencing of mouse lines divergently selected for fatness (FLI) and leanness (FHI) revealed several genetic variants as candidates for novel obesity genes.

BACKGROUND: Analysing genomes of animal model organisms is widely used for understanding the genetic basis of complex traits and diseases, such as obesity, for which only a few mouse models exist, however, without their lean counterparts. OBJECTIVE: To analyse genetic differences in the unique mouse models of polygenic obesity (Fat line) and leanness (Lean line) originating from the same base population and established by divergent selection over more than 60 generations. METHODS: Genetic variability was analysed using WGS. Variants were identified with GATK and annotated with Ensembl VEP. g.Profiler, WebGestalt, and KEGG were used for GO and pathway enrichment analysis. miRNA seed regions were obtained with miRPathDB 2.0, LncRRIsearch was used to predict targets of identified lncRNAs, and genes influencing adipose tissue amount were searched using the IMPC database. RESULTS: WGS analysis revealed 6.3 million SNPs, 1.3 million were new. Thousands of potentially impactful SNPs were identified, including within 24 genes related to adipose tissue amount. SNP density was highest in pseudogenes and regulatory RNAs. The Lean line carries SNP rs248726381 in the seed region of mmu-miR-3086-3p, which may affect fatty acid metabolism. KEGG analysis showed deleterious missense variants in immune response and diabetes genes, with food perception pathways being most enriched. Gene prioritisation considering SNP GERP scores, variant consequences, and allele comparison with other mouse lines identified seven novel obesity candidate genes: 4930441H08Rik, Aff3, Fam237b, Gm36633, Pced1a, Tecrl, and Zfp536. CONCLUSION: WGS revealed many genetic differences between the lines that accumulated over the selection period, including variants with potential negative impacts on gene function. Given the increasing availability of mouse strains and genetic polymorphism catalogues, the study is a valuable resource for researchers to study obesity.

Insight into the complexity of male infertility: a multi-omics review.

Male infertility is a reproductive disorder, accounting for 40-50% of infertility. Currently, in about 70% of infertile men, the cause remains unknown. With the introduction of novel omics and advancement in high-throughput technology, potential biomarkers are emerging. The main purpose of our work was to overview different aspects of omics approaches in association with idiopathic male infertility and highlight potential genes, transcripts, non-coding RNA, proteins, and metabolites worth further exploring. Using the Gene Ontology (GO) analysis, we aimed to compare enriched GO terms from each omics approach and determine their overlapping. A PubMed database screening for the literature published between February 2014 and June 2022 was performed using the keywords: male infertility in association with different omics approaches: genomics, epigenomics, transcriptomics, ncRNAomics, proteomics, and metabolomics. A GO enrichment analysis was performed using the Enrichr tool. We retrieved 281 global studies: 171 genomics (DNA level), 21 epigenomics (19 of methylation and two histone residue modifications), 15 transcriptomics, 31 non-coding RNA, 29 proteomics, two protein posttranslational modification, and 19 metabolomics studies. Gene ontology comparison showed that different omics approaches lead to the identification of different molecular factors and that the corresponding GO terms, obtained from different omics approaches, do not overlap to a larger extent. With the integration of novel omics levels into the research of idiopathic causes of male infertility, using multi-omic systems biology approaches, we will be closer to finding the potential biomarkers and consequently becoming aware of the entire spectrum of male infertility, their cause, prognosis, and potential treatment.

Transcriptomic signatures for human male infertility.

Introduction: Male infertility is a common, complex disorder. A better understanding of pathogenesis and etiology is needed for timely diagnosis and treatment. The aim of this study, therefore, was to identify genes involved in the pathogenesis of idiopathic male infertility based on data from transcriptomic level supported with data from genomic level. Materials and methods: First, we performed whole gene expression analysis in 20 testis biopsy samples of patients with severely impaired (10) and normal spermatogenesis (10). Further, we have performed systematic review of comparable male infertility studies and overlapped the most significantly expressed genes identified in our study with the most differentially expressed genes from selected studies. Gene Ontology analysis and KEGG functional enrichment have been performed with Enrichr analysis tool. Additionally, we have overlapped these genes with the genes where rare variants have been identified previously. Results: In 10 patients with severely impaired spermatogenesis and 10 controls, we identified more than 1,800 differentially expressed genes (p < 0.001). With the systematic review of three previously performed microarray studies that have met inclusion criteria we identified 257 overlapped differentialy expressed genes (144 downregulated and 113 upregulated). Intersection of genes from transcriptomic studies with genes with identified rare variants revealed a total of 7 genes linked with male infertility phenotype (CYP11A1, CYP17A1, RSPH3, TSGA10, AKAP4, CCIN, NDNF). Conclusion: Our comprehensive study highlighted the role of four genes in pathogenesis of male infertility and provided supporting evidence for three promising candidate genes which dysfunction may result in a male infertility disorder.

Differential alternative polyadenylation response to high-fat diet between polygenic obese and healthy lean mice.

Obesity's complex etiology due to the interplay of environment and genetics makes it a more challenging research and health problem. Some of the contributing genetic factors that have not yet been examined in detail entail mRNA polyadenylation (PA). Genes with multiple PA sites express mRNA isoforms differing in coding sequence or 3'UTR through alternative polyadenylation (APA). Alterations in PA have been associated with various diseases; however, its contribution to obesity is not well-researched. Following an 11-week high-fat diet, the APA sites in the hypothalamus of two unique mouse models for polygenic obesity (Fat line) and healthy leanness (Lean line) were determined using whole transcriptome termini site sequencing (WTTS-seq). We found 17 genes of interest with differentially expressed APA (DE-APA) isoforms, among which seven were previously associated with obesity or obesity-related traits (Pdxdc1, Smyd3, Rpl14, Copg1, Pcna, Ric3, Stx3) but have not yet been studied in the context of APA. The remaining ten genes (Ccdc25, Dtd2, Gm14403, Hlf, Lyrm7, Mrpl3, Pisd-ps3, Sbsn, Slx1b, Spon1) represent novel candidates associated with obesity/adiposity due to variability brought about by differential usage of APA sites. Our results provide insights into the relationship between PA and the hypothalamus in the context of obesity, by being the first study of DE-APA sites and DE-APA isoforms in these mouse models. Future studies are needed further to explore the role of APA isoforms in polygenic obesity by expanding the scope of research to other metabolically important tissues (such as liver and adipose tissues) and investigating the potential for targeting PA as a therapeutic strategy for obesity management.

Towards a Multi-Omics of Male Infertility.

Infertility is a common problem affecting one in six couples and in 30% of infertile couples, the male factor is a major cause. A large number of genes are involved in spermatogenesis and a significant proportion of male infertility phenotypes are of genetic origin. Studies on infertility have so far primarily focused on chromosomal abnormalities and sequence variants in protein-coding genes and have identified a large number of disease-associated genes. However, it has been shown that a multitude of factors across various omics levels also contribute to infertility phenotypes. The complexity of male infertility has led to the understanding that an integrated, multi-omics analysis may be optimal for unravelling this disease. While there is a vast array of different factors across omics levels associated with infertility, the present review focuses on known factors from the genomics, epigenomics, transcriptomics, proteomics, metabolomics, glycomics, lipidomics, miRNomics, and integrated omics levels. These include: repeat expansions in AR, POLG, ATXN1, DMPK, and SHBG, multiple SNPs, copy number variants in the AZF region, disregulated miRNAs, altered H3K9 methylation, differential MTHFR, MEG3, PEG1, and LIT1 methylation, altered protamine ratios and protein hypo/hyperphosphorylation. This integrative review presents a step towards a multi-omics approach to understanding the complex etiology of male infertility. Currently only a few genetic factors, namely chromosomal abnormalities and Y chromosome microdeletions, are routinely tested in infertile men undergoing intracytoplasmic sperm injection. A multi-omics approach to understanding infertility phenotypes may yield a more holistic view of the disease and contribute to the development of improved screening methods and treatment options. Therefore, beside discovering as of yet unknown genetic causes of infertility, integrating multiple fields of study could yield valuable contributions to the understanding of disease development. Future multi-omics studies will enable to synthesise fragmented information and facilitate biomarker discovery and treatments in male infertility.

Genome-wide screening for genetic variants in polyadenylation signal (PAS) sites in mouse selection lines for fatness and leanness.

Alternative polyadenylation (APA) determines mRNA stability, localisation, translation and protein function. Several diseases, including obesity, have been linked to APA. Studies have shown that single nucleotide polymorphisms in polyadenylation signals (PAS-SNPs) can influence APA and affect phenotype and disease susceptibility. However, these studies focussed on associations between single PAS-SNP alleles with very large effects and phenotype. Therefore, we performed a genome-wide screening for PAS-SNPs in the polygenic mouse selection lines for fatness and leanness by whole-genome sequencing. The genetic variants identified in the two lines were overlapped with locations of PAS sites obtained from the PolyASite 2.0 database. Expression data for selected genes were extracted from the microarray expression experiment performed on multiple tissue samples. In total, 682 PAS-SNPs were identified within 583 genes involved in various biological processes, including transport, protein modifications and degradation, cell adhesion and immune response. Moreover, 63 of the 583 orthologous genes in human have been previously associated with human diseases, such as nervous system and physical disorders, and immune, endocrine, and metabolic diseases. In both lines, PAS-SNPs have also been identified in genes broadly involved in APA, such as Polr2c, Eif3e and Ints11. Five PAS-SNPs within 5 genes (Car, Col4a1, Itga7, Lat, Nmnat1) were prioritised as potential functional variants and could contribute to the phenotypic disparity between the two selection lines. The developed PAS-SNPs catalogue presents a key resource for planning functional studies to uncover the role of PAS-SNPs in APA, disease susceptibility and fat deposition.

How Can We Advance Integrative Biology Research in Animal Science in 21st Century? Experience at University of Ljubljana from 2002 to 2022.

In this perspective analysis, we strive to answer the following question: how can we advance integrative biology research in the 21st century with lessons from animal science? At the University of Ljubljana, Biotechnical Faculty, Department of Animal Science, we share here our three lessons learned in the two decades from 2002 to 2022 that we believe could inform integrative biology, systems science, and animal science scholarship in other countries and geographies. Cultivating multiomics knowledge through a conceptual lens of integrative biology is crucial for life sciences research that can stand the test of diverse biological, clinical, and ecological contexts. Moreover, in an era of the current COVID-19 pandemic, animal nutrition and animal science, and the study of their interactions with human health (and vice versa) through integrative biology approaches hold enormous prospects and significance for systems medicine and ecosystem health.

Shared Genetic Risk Factors Between Cancer and Cardiovascular Diseases.

Cancer and cardiovascular diseases (CVD) account for approximately 27.5 million deaths every year. While they share some common environmental risk factors, their shared genetic risk factors are not yet fully understood. The aim of the present study was to aggregate genetic risk factors associated with the comorbidity of cancer and CVDs. For this purpose, we: (1) created a catalog of genes associated with cancer and CVDs, (2) visualized retrieved data as a gene-disease network, and (3) performed a pathway enrichment analysis. We performed screening of PubMed database for literature reporting genetic risk factors in patients with both cancer and CVD. The gene-disease network was visualized using Cytoscape and the enrichment analysis was conducted using Enrichr software. We manually reviewed the 181 articles fitting the search criteria and included 13 articles in the study. Data visualization revealed a highly interconnected network containing a single subnetwork with 56 nodes and 146 edges. Genes in the network with the highest number of disease interactions were JAK2, TTN, TET2, and ATM. The pathway enrichment analysis revealed that genes included in the study were significantly enriched in DNA damage repair (DDR) pathways, such as homologous recombination. The role of DDR mechanisms in the development of CVDs has been studied in previously published research; however, additional functional studies are required to elucidate their contribution to the pathophysiology to CVDs.

Bioethics in human embryology: the double-edged sword of embryo research.

There has been a significant increase in the use of assisted reproductive therapies (ARTs) over the past several decades, allowing many couples with infertility to conceive. Despite the achievements in this field, a mounting body of evidence concerning the epigenetic risks associated with ART interventions such as ovarian hormonal stimulation, intracytoplasmic sperm injection (ICSI), and in vitro culture (IVC) of oocytes and embryos has also emerged. Induced development of multiple follicles, the IVC media itself, and extended culture may alter the epigenome of both gametes and embryos, resulting in yet to be fully understood developmental, postnatal, and adult life health consequences. Investigators have attempted to decipher the molecular mechanisms mediating ART-induced epigenetic changes using either human samples or animal models with some success. As research in this field continues to expand, the ethical responsibilities of embryologists and researchers have become critically important. Here, we briefly discuss the ethical aspects of ART research, concentrating on the constraints arising from the perceived 'unnaturalness' of many of these procedures. Secondly, we focus on the bioethics and morality of human embryo research in general and how ethically acceptable model systems may be used to mimic early human embryogenesis. Lastly, we review the 14-day culture limit of human embryos and the notion that this rule could be considered of taken into account using new technologies and cues from animal models. The 'black box' of early post-implantation embryogenesis might be revealed using embryo models. As long as this distinct moral line has been drawn and closely followed, we should not fear scientific growth in embryo research. Although in vitro fertilization (IVF) is ethically acceptable, research with human embryos to improve its success raises serious ethical concerns that are in need of constant revisiting.Glossary index: Moral status: the ascription of obligations and rights to embryos on the basis of sentience; Sentience: the capacity of the developing embryo to experience feelings and sensations, such as the awareness of pain; Ectogenesis: the growth of the embryo in an artificial environment outside the mother's body.

MicroRNAs in Leukemias: A Clinically Annotated Compendium.

Leukemias are a group of malignancies of the blood and bone marrow. Multiple types of leukemia are known, however reliable treatments have not been developed for most leukemia types. Furthermore, even relatively reliable treatments can result in relapses. MicroRNAs (miRNAs) are a class of short, noncoding RNAs responsible for epigenetic regulation of gene expression and have been proposed as a source of potential novel therapeutic targets for leukemias. In order to identify central miRNAs for leukemia, we conducted data synthesis using two databases: miRTarBase and DISNOR. A total of 137 unique miRNAs associated with 16 types of leukemia were retrieved from miRTarBase and 86 protein-coding genes associated with leukemia were retrieved from the DISNOR database. Based on these data, we formed a visual network of 248 miRNA-target interactions (MTI) between leukemia-associated genes and miRNAs associated with ≥4 leukemia types. We then manually reviewed the literature describing these 248 MTIs for interactions identified in leukemia studies. This manually curated data was then used to visualize a network of 64 MTIs identified in leukemia patients, cell lines and animal models. We also formed a visual network of miRNA-leukemia associations. Finally, we compiled leukemia clinical trials from the ClinicalTrials database. miRNAs with the highest number of MTIs were miR-125b-5p, miR-155-5p, miR-181a-5p and miR-19a-3p, while target genes with the highest number of MTIs were TP53, BCL2, KIT, ATM, RUNX1 and ABL1. The analysis of 248 MTIs revealed a large, highly interconnected network. Additionally, a large MTI subnetwork was present in the network visualized from manually reviewed data. The interconnectedness of the MTI subnetwork suggests that certain miRNAs represent central disease molecules for multiple leukemia types. Additional studies on miRNAs, their target genes and associated biological pathways are required to elucidate the therapeutic potential of miRNAs in leukemia.

Genetic variants of the hypoxia-inducible factor 3 alpha subunit (Hif3a) gene in the Fat and Lean mouse selection lines.

BACKGROUND: Adipose tissue hypoxia and members of the hypoxia-inducible factor alpha (HIFA) are involved in development of obesity. However, the mechanism and functions of HIF3A, one of three HIFA paralogs, in fat deposition have not been sufficiently studied. METHODS AND RESULTS: In the present study, we investigated whether Hif3a sequence variants are associated with divergent fat deposition in mouse selection lines for fatness and leanness. Sequencing and RFLP were used to analyse sequence variants within Hif3a. To identify candidate regulatory variants, we performed literature screening and used databases and bioinformatics tools like Ensembl, MethPrimer, TargetScanMouse, miRDB, PolyAsite, RISE, LncRRIsearch, RNAfold, PredictProtein, CAIcal, and switches.ELM Resource. There are 90 sequence variants in Hif3a between the two mouse lines. While most Fat line variants locate within intronic regions, Lean line variants are mainly in 3' UTR. We constructed a map of Hif3a potential regulatory regions and identified 39 regulatory variants by integrating data on constrained and regulatory elements, CpGs, and miRNAs and lncRNAs binding sites. Moreover, 3' UTR and two exonic variants may influence mRNA stability, translation rate and protein functionality. We propose as priority candidates for further functional studies a missense (rs37398126) and synonymous (rs37739792) variants, and intronic (rs47471302) variant that overlap conserved element in promoter region and predicted lncRNAs binding site. CONCLUSION: The results indicate a potential involvement of Hif3a in fat deposition. Additionally, approach used in the present study may serve as a general guideline for constructing an integrative gene map for prioritizing candidate gene variants with phenotypic effects.

Syndromic male subfertility: A network view of genome-phenome associations.

BACKGROUND: Male infertility is a disorder of the reproductive system with a highly complex genetic landscape. In most cases, the reason for male infertility remains unknown; however, the importance of genetic abnormalities in the diagnosis of subfertility/infertility is becoming increasingly recognized. Several syndromes include impaired male fertility in the clinical picture, although a comprehensive analysis of genetic causes of the syndromology perspective of male reproduction is not yet available. OBJECTIVES: (1) To develop a catalog of syndromes and corresponding genes associated with impaired male fertility and (2) to visualize an up-to-date genome-phenome network of syndromic male subfertility. MATERIALS AND METHODS: Published literature was retrieved from the Online Mendelian Inheritance in Man, Orphanet, Human Phenotype Ontology and PubMed databases using keywords "male infertility," "syndrome," "gene," and "case report"; time period from 1980 to September, 2021. Retrieved data were organized as a catalog and complemented with identification numbers of syndromes (MIM ID) and genes (Gene ID). The genome-phenome network and the phenome network were visualized using Cytoscape and Gephi software platforms. Protein-protein interaction analysis was performed using STRING tool. RESULTS: Retrieved syndromes were presented as (1) a catalog containing 63 syndromes and 93 associated genes, (2) a genome-phenome network including CHD7 and WT1 genes and Noonan and Kartagener syndromes, and (3) a phenome network including 63 syndromes, and 25 categories of clinical features. DISCUSSION: The developed catalog will contribute to the advances and translational impact toward understanding the factors of syndromic male infertility. Visualized networks provide simple, flexible tools for clinicians and researchers to quickly generate hypotheses and gain a deeper understanding of underlying mechanisms affecting male reproduction. CONCLUSION: Recognition of the significance of genome-phenome visualization as part of network medicine can help expedite efforts toward unravelling molecular mechanisms and enable advances personal/precision medicine of male reproduction and other complex traits.

Transcriptomics of receptive endometrium in women with sonographic features of adenomyosis.

BACKGROUND: Women with uterine adenomyosis seeking assisted reproduction have been associated with compromised endometrial receptivity to embryo implantation. To understand the mechanisms involved in this process, we aimed to compare endometrial transcriptome profiles during the window of implantation (WOI) between women with and without adenomyosis. METHODS: We obtained endometrial biopsies LH-timed to the WOI from women with sonographic features of adenomyosis (n=10) and controls (n=10). Isolated RNA samples were subjected to RNA sequencing (RNA-seq) by the Illumina NovaSeq 6000 platform and endometrial receptivity classification with a molecular tool for menstrual cycle phase dating (beREADY®, CCHT). The program language R and Bioconductor packages were applied to analyse RNA-seq data in the setting of the result of accurate endometrial dating. To suggest robust candidate pathways, the identified differentially expressed genes (DEGs) associated with the adenomyosis group in the receptive phase were further integrated with 151, 173 and 42 extracted genes from published studies that were related to endometrial receptivity in healthy uterus, endometriosis and adenomyosis, respectively. Enrichment analyses were performed using Cytoscape ClueGO and CluePedia apps. RESULTS: Out of 20 endometrial samples, 2 were dated to the early receptive phase, 13 to the receptive phase and 5 to the late receptive phase. Comparison of the transcriptomics data from all 20 samples provided 909 DEGs (p<0.05; nonsignificant after adjusted p value) in the adenomyosis group but only 4 enriched pathways (Bonferroni p value < 0.05). The analysis of 13 samples only dated to the receptive phase provided suggestive 382 DEGs (p<0.05; nonsignificant after adjusted p value) in the adenomyosis group, leading to 33 enriched pathways (Bonferroni p value < 0.05). These included pathways were already associated with endometrial biology, such as "Expression of interferon (IFN)-induced genes" and "Response to IFN-alpha". Data integration revealed pathways indicating a unique effect of adenomyosis on endometrial molecular organization (e.g., "Expression of IFN-induced genes") and its interference with endometrial receptivity establishment (e.g., "Extracellular matrix organization" and "Tumour necrosis factor production"). CONCLUSIONS: Accurate endometrial dating and RNA-seq analysis resulted in the identification of altered response to IFN signalling as the most promising candidate of impaired uterine receptivity in adenomyosis.

MicroRNA-Target Interaction Regulatory Network in Alzheimer's Disease.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA-target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein-protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.

Integration and Visualization of Regulatory Elements and Variations of the EPAS1 Gene in Human.

Endothelial PAS domain-containing protein 1 (EPAS1), also HIF2α, is an alpha subunit of hypoxia-inducible transcription factor (HIF), which mediates cellular and systemic response to hypoxia. EPAS1 has an important role in the transcription of many hypoxia-responsive genes, however, it has been less researched than HIF1α. The aim of this study was to integrate an increasing number of data on EPAS1 into a map of diverse OMICs elements. Publications, databases, and bioinformatics tools were examined, including Ensembl, MethPrimer, STRING, miRTarBase, COSMIC, and LOVD. The EPAS1 expression, stability, and activity are tightly regulated on several OMICs levels to maintain complex oxygen homeostasis. In the integrative EPAS1 map we included: 31 promoter-binding proteins, 13 interacting miRNAs and one lncRNA, and 16 post-translational modifications regulating EPAS1 protein abundance. EPAS1 has been associated with various cancer types and other diseases. The development of neuroendocrine tumors and erythrocytosis was shown to be associated with 11 somatic and 20 germline variants. The integrative map also includes 12 EPAS1 target genes and 27 interacting proteins. The study introduced the first integrative map of diverse genomics, transcriptomics, proteomics, regulomics, and interactomics data associated with EPAS1, to enable a better understanding of EPAS1 activity and regulation and support future research.

Systematic Search for Novel Circulating Biomarkers Associated with Extracellular Vesicles in Alzheimer's Disease: Combining Literature Screening and Database Mining Approaches.

miRNAs play an important role in neurodegenerative diseases. Many miRNA-target gene interactions (MTI) have been experimentally confirmed and associated with Alzheimer's disease (AD). miRNAs may also be contained within extracellular vesicles (EVs), mediators of cellular communication and a potential source of circulating biomarkers in body fluids. Therefore, EV-associated miRNAs (EV-miRNAs) in peripheral blood could support earlier and less invasive AD diagnostics. We aimed to prioritize EV-related miRNA with AD-related genes and to identify the most promising candidates for novel AD biomarkers. A list of unique EV-miRNAs from the literature was combined with a known set of AD risk genes and enriched for MTI. Additionally, miRNAs associated with the AD phenotype were combined with all known target genes in MTI enrichment. Expression in different sample types was analyzed to identify AD-associated miRNAs with the greatest potential as AD circulating biomarkers. Four common MTI were observed between EV-miRNAs and AD-associated miRNAs: hsa-miR-375-APH1B, hsa-miR-107-CDC42SE2, hsa-miR-375-CELF2, and hsa-miR-107-IL6. An additional 61 out of 169 unique miRNAs (36.1%) and seven out of 84 unique MTI (8.3%), observed in the body fluids of AD patients, were proposed as very strong AD-circulating biomarker candidates. Our analysis summarized several potential novel AD biomarkers, but further studies are needed to evaluate their potential in clinical practice.

Integrative Map of HIF1A Regulatory Elements and Variations.

Hypoxia-inducible factor (HIF) family of transcription factors (HIF1A, EPAS1, and HIF3A) are regulators of the cellular response to hypoxia. They have been shown to be involved in development of various diseases such as cancer, diabetes, and erythrocytosis. A complete map of connections between HIF family of genes with various omics types has not yet been developed. The main aim of the present analysis was to construct the integrative map of genomic elements associated with HIF1A gene and prioritize potentially deleterious variants. Various genomic databases and bioinformatics tools were used, including Ensembl, MirTarBase, STRING, Cytoscape, MethPrimer, CADD, SIFT, and UALCAN. Integrative HIF1A gene map was visualized and includes transcriptional and post-transcriptional regulators, downstream targets, and genetic variants. One CpG island overlaps transcription start site of the HIF1A gene. Out of over 450 missense variants, four have predicted deleterious effect on protein function by at least five bioinformatics tools. Currently there are 85 miRNAs reported to target HIF1A. HIF1A downstream targets include protein-coding genes, long noncoding RNAs, and microRNAs (hypoxamiRs). The study presents the first integration of heterogeneous molecular interactions associated with HIF1A gene enabling a holistic view of the gene and lays the groundwork for supplementing the data in the future.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays.

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Identification of Variants Associated With Rare Hematological Disorder Erythrocytosis Using Targeted Next-Generation Sequencing Analysis.

An erythrocytosis is present when the red blood cell mass is increased, demonstrated as elevated hemoglobin and hematocrit in the laboratory evaluation. Congenital predispositions for erythrocytosis are rare, with germline variants in several genes involved in oxygen sensing (VHL, EGLN1, and EPAS1), signaling for hematopoietic cell maturation (EPOR and EPO), and oxygen transfer (HBB, HBA1, HBA2, and BPGM) that were already associated with the eight congenital types (ECYT1-8). Screening for variants in known congenital erythrocytosis genes with classical sequencing approach gives a correct diagnosis for only up to one-third of the patients. The genetic background of erythrocytosis is more heterogeneous, and additional genes involved in erythropoiesis and iron metabolism could have a putative effect on the development of erythrocytosis. This study aimed to detect variants in patients with yet unexplained erythrocytosis using the next-generation sequencing (NGS) approach, targeting genes associated with erythrocytosis and increased iron uptake and implementing the diagnostics of congenital erythrocytosis in Slovenia. Selected 25 patients with high hemoglobin, high hematocrit, and no acquired causes were screened for variants in the 39 candidate genes. We identified one pathogenic variant in EPAS1 gene and three novel variants with yet unknown significance in genes EPAS1, JAK2, and SH2B3. Interestingly, a high proportion of patients were heterozygous carriers for two variants in HFE gene, otherwise pathogenic for the condition of iron overload. The association between the HFE variants and the development of erythrocytosis is not clearly understood. With a targeted NGS approach, we determined an actual genetic cause for the erythrocytosis in one patient and contributed to better management of the disease for the patient and his family. The effect of variants of unknown significance on the enhanced production of red blood cells needs to be further explored with functional analysis. This study is of great significance for the improvement of diagnosis of Slovenian patients with unexplained erythrocytosis and future research on the etiology of this rare hematological disorder.