Identification of OAF and PVRL1 as candidate genes for an ocular anomaly characterized by Peters anomaly type 2 and ectopia lentis

OAF: a new member of the BRICHOS family

Summary

The 10 known BRICHOS domain-containing proteins in humans have been linked to an unusually long list of pathologies, including cancer, obesity and two amyloid-like diseases. BRICHOS domains themselves have been described as intramolecular chaperones that act to prevent amyloid-like aggregation of their proteins' mature polypeptides. Using structural comparison of coevolution-based AlphaFold models and sequence conservation, we identified the Out at First (OAF) protein as a new member of the BRICHOS family in humans. OAF is an experimentally uncharacterized protein that has been proposed as a candidate biomarker for clinical management of coronavirus disease 2019 infections. Our analysis revealed how structural comparison of AlphaFold models can discover remote homology relationships and lead to a better understanding of BRICHOS domain molecular mechanism.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants

With the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV's clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present SVInterpreter, a free Web application, which analyzes both balanced and unbalanced SVs using topologically associated domains (TADs) as genome units. Among others, gene-associated data (as function and dosage sensitivity), phenotype similarity scores, and copy number variants (CNVs) scoring metrics are retrieved for an informed SV interpretation. For evaluation, we retrospectively applied SVInterpreter to 97 balanced (translocations and inversions) and 125 unbalanced (deletions, duplications, and insertions) previously published SVs, and 145 SVs identified from 20 clinical samples. Our results showed the ability of SVInterpreter to support the evaluation of SVs by (1) confirming more than half of the predictions of the original studies, (2) decreasing 40% of the variants of uncertain significance, and (3) indicating several potential position effect events. To our knowledge, SVInterpreter is the most comprehensive TAD-based tool to identify the possible disease-causing candidate genes and to assist prediction of the clinical outcome of SVs. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py.

Novel methylation gene panel in adjacent normal tissues predicts poor prognosis of colorectal cancer in Taiwan

BACKGROUND

It is evident that current clinical criteria are suboptimal to accurately estimate patient prognosis. Studies have identified epigenetic aberrant changes as novel prognostic factors for colorectal cancer (CRC).

AIM

To estimate whether a methylation gene panel in different clinical stages can reflect a different prognosis.

METHODS

We enrolled 120 CRC patients from Tri-Service General Hospital in Taiwan and used the candidate gene approach to select six genes involved in carcinogenesis pathways. Patients were divided into two groups based on the methylation status of the six evaluated genes, namely, the < 3 aberrancy group and ≥ 3 aberrancy group. Various tumor stages were divided into two subgroups (local and advanced stages) on the basis of the pathological type of the following tissues: Tumor and adjacent normal tissues (matched normal). We assessed DNA methylation in tumors and adjacent normal tissues from CRC patients and analyzed the association between DNA methylation with different cancer stages and the prognostic outcome including time to progression (TTP) and overall survival.

RESULTS

We observed a significantly increasing trend of hazard ratio as the number of hypermethylated genes increased both in normal tissue and tumor tissue. The 5-year TTP survival curves showed a significant difference between the ≥ 3 aberrancy group and the < 3 aberrancy group. Compared with the < 3 aberrancy group, a significantly shorter TTP was observed in the ≥ 3 aberrancy group. We further analyzed the interaction between CRC prognosis and different cancer stages (local and advanced) according to the methylation status of the selected genes in both types of tissues. There was a significantly shorter 5-year TTP for tumors at advanced stages with the promoter methylation status of selected genes than for those with local stages. We found an interaction between cancer stages and the promoter methylation status of selected genes in both types of tissues.

CONCLUSION

Our data provide a significant association between the methylation markers in normal tissues with advanced stage and prognosis of CRC. We recommend using these novel markers to assist in clinical decision-making.

RNA sequencing-based transcriptomic profiles of embryonic lens development for cataract gene discovery

Isolated or syndromic congenital cataracts are heterogeneous developmental defects, making the identification of the associated genes challenging. In the past, mouse lens expression microarrays have been successfully applied in bioinformatics tools (e.g., iSyTE) to facilitate human cataract-associated gene discovery. To develop a new resource for geneticists, we report high-throughput RNA sequencing (RNA-seq) profiles of mouse lens at key embryonic stages (E)10.5 (lens pit), E12.5 (primary fiber cell differentiation), E14.5 and E16.5 (secondary fiber cell differentiation). These stages capture important events as the lens develops from an invaginating placode into a transparent tissue. Previously, in silico whole-embryo body (WB)-subtraction-based "lens-enriched" expression has been effective in prioritizing cataract-linked genes. To apply an analogous approach, we generated new mouse WB RNA-seq datasets and show that in silico WB subtraction of lens RNA-seq datasets successfully identifies key genes based on lens-enriched expression. At ≥2 counts-per-million expression, ≥1.5 log₂ fold-enrichment (p < 0.05) cutoff, E10.5 lens exhibits 1401 enriched genes (17% lens-expressed genes), E12.5 lens exhibits 1937 enriched genes (22% lens-expressed genes), E14.5 lens exhibits 2514 enriched genes (31% lens-expressed genes), and E16.5 lens exhibits 2745 enriched genes (34% lens-expressed genes). Biological pathway analysis identified genes associated with lens development, transcription regulation and signaling pathways, among other functional groups. Furthermore, these new RNA-seq data confirmed high expression of established cataract-linked genes and identified new potential regulators in the lens. Finally, we developed new lens stage-specific UCSC Genome Brower annotation tracks and made these publicly accessible through iSyTE ( https://research.bioinformatics.udel.edu/iSyTE/ ) for user-friendly visualization of lens gene expression/enrichment to prioritize genes from high-throughput data from cataract cases.

Realizing the significance of noncoding functionality in clinical genomics

Clinical genomics promises unprecedented precision in understanding the genetic basis of disease. Understanding the impact of variation across the genome is required to realize this potential. Currently, clinical genomics analyses focus on protein-coding genes. However, the noncoding genome is substantially larger than the protein-coding counterpart, and contains structural, regulatory, and transcribed information that needs to be incorporated into genome annotations if the full extent of the opportunity to use genomic information in healthcare is to be realized. This article reviews the challenges and opportunities in unlocking the clinical significance of coding and noncoding genomic information and translating its utility in practice.

Most of the DNA in the genome does not consist of genes that code for proteins, and understanding the function of these less examined parts of our genetic material is essential to fully understand human development and disease. Brian Gloss and Marcel Dinger at the Garvan Institute of Medical Research in Sydney, Australia, review the challenges and opportunities in unraveling the clinical significance of all parts of our DNA. Many regions of DNA that do not encode protein molecules perform crucial functions in regulating the activity and interactions of the protein-coding genes. Variations in these regions may significantly influence the risks and causes of disease. Studying all parts of the genome will be critical for ensuring that the powerful modern techniques of genetic analysis have maximal impact on healthcare.