RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease

Clinical characteristics, AR gene variants, and functional domains in 64 patients with androgen insensitivity syndrome

BACKGROUND

Androgen insensitivity syndrome (AIS) is caused by abnormal androgen receptor (AR) genes that show variable genotypes and phenotypes. However, the correlation between genotype and phenotype is unclear.

METHODS

We retrospectively evaluated 64 patients with AIS at Shanghai Children's Hospital from 2015 to 2022. We analysed the clinical data of the patients, including hormone levels, AR gene variants, and functional domains.

RESULTS

Variants occurred in the 3 major functional domains in 56 patients, including 23 patients with complete androgen insensitivity syndrome (CAIS) and 33 with partial androgen insensitivity syndrome (PAIS). The incidence of nonscrotal fusion (P = 0.019) and proximal urethral opening (P = 0.0002) in the ligand-binding domain (LBD) group was higher than that in the non-LBD group. The phallus length in the LBD group was significantly shorter than that in the non-LBD group (P = 0.009). The external masculinization score (EMS) in the LBD group was significantly lower than that in the non-LBD group (P = 0.013). The levels of inhibin-B (INHB; P = 0.0007), basal luteinizing hormone (LH; P = 0.033), LH peak (P = 0.002), and testosterone (T) after human chorionic gonadotropin (HCG) stimulation (P = 0.001) in the LBD group were higher than those in the non-LBD group. There were 53 variants in 64 patients, including 42 reported and 11 novel AR variants, including p.Met247Arg, p.Asp266Glyfs*39, p.Arg362Serfs*140, p.Ala385Val, p.Glu541Asp, p.Pro613Leu, p.Pro695Leu, p.Asn757Asp, c.1616 + 1dup, c.1886-1G > A and exon 5-7 deletion.

CONCLUSIONS

The EMS of patients with AIS in the LBD group was significantly lower than that in the non-LBD group. The phallus length was shorter, and the incidences of proximal urethral opening and nonscrotal fusion were higher, suggesting that the phenotypes in the LBD group were more severe. The levels of INHB, basal LH, peak LH, and T after HCG stimulation in the LBD group were higher than those in the non-LBD group, suggesting that androgen resistance in the LBD group was more severe. We identified 53 variants in 64 patients: 42 reported and 11 novel AR variants. These findings provide new and deeper insight into AIS diagnosis and genetic assessment of AIS.

Isoform changes of action potential regulators in the ventricles of arrhythmogenic phospholamban-R14del humanized mouse hearts

Arrhythmogenic cardiomyopathy (ACM) is characterized by life-threatening ventricular arrhythmias and sudden cardiac death and affects hundreds of thousands of patients worldwide. The deletion of Arginine 14 (p.R14del) in the phospholamban (PLN) gene has been implicated in the pathogenesis of ACM. PLN is a key regulator of sarcoplasmic reticulum (SR) Ca2+ cycling and cardiac contractility. Despite global gene and protein expression studies, the molecular mechanisms of PLN-R14del ACM pathogenesis remain unclear. Using a humanized PLN-R14del mouse model and human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs), we investigated the transcriptome-wide mRNA splicing changes associated with the R14del mutation. We identified >200 significant alternative splicing (AS) events and distinct AS profiles were observed in the right (RV) and left (LV) ventricles in PLN-R14del compared to WT mouse hearts. Enrichment analysis of the AS events showed that the most affected biological process was associated with "cardiac cell action potential", specifically in the RV. We found that splicing of 2 key genes, Trpm4 and Camk2d, which encode proteins regulating calcium homeostasis in the heart, were altered in PLN-R14del mouse hearts and human iPSC-CMs. Bioinformatical analysis pointed to the tissue-specific splicing factors Srrm4 and Nova1 as likely upstream regulators of the observed splicing changes in the PLN-R14del cardiomyocytes. Our findings suggest that aberrant splicing may affect Ca2+-homeostasis in the heart, contributing to the increased risk of arrythmogenesis in PLN-R14del ACM.

Favourable outcome of multisystem venous thrombosis associated with novel SERPINC1 mutation after treated with dabigatran: a case report with 7-year follow-up

BACKGROUND

Mutations in SERPINC1 lead to deficiency in antithrombin (AT) which is an endogenous anticoagulant of normal hemostasis and could result in venous thromboembolism (VTE).

CASE PRESENTATION

A 61-year-old male patient with recurrent thrombosis returned to the hospital with multiple cerebral thrombosis after voluntary cessation of dabigatran therapy. Laboratory tests revealed a type I AT deficiency in this patient and further whole exome sequencing (WES) identified a novel heterozygous frameshift duplication (c.233_236dup, p.Val80Alafs*26) in SERPINC1 gene. Long-term dabigatran treatment was given and no recurrence or side effects were found within the followed 5 years.

CONCLUSION

A multisystem VTE patient with a novel SERPINC1 mutation (c.233_236dup, p.Val80Alafs*26) reached a favourable outcome after dabigatran treatment.

Computational prediction of human deep intronic variation

BACKGROUND

The adoption of whole-genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to discriminate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce.

RESULTS

In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that potentially affect splicing regulatory elements. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground - information, but the use of these tools results in decreased predictive power when compared to black box methods.

CONCLUSIONS

Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.

A Japanese boy with Bardet-Biedl syndrome caused by a novel homozygous variant in the ARL6 gene who was initially diagnosed with retinitis punctata albescens: A case report

PURPOSE

Bardet-Biedl Syndrome (BBS) is an autosomal recessive systemic disorder characterized by retinitis pigmentosa, polydactyly, obesity, intellectual disability, renal impairments, and hypogonadism. The purpose of this study was to determine the ocular characteristics of a boy with BBS caused by a novel homozygous variant in the ARL6 (alternative named BBS3) gene who had been originally diagnosed with retinitis punctata albescens.

METHODS

This was an observational case study. The patient underwent ophthalmological examinations, electroretinography, and genetic analyses using whole-exome sequencing.

RESULTS

A 7-year-old boy was examined in our hospital with complaints of a progressive reduction of his visual acuity and night blindness in both eyes. There was no family history of eye diseases and no consanguineous marriage. Fundus examinations showed numerous white spots in the deep retina and retinal pigment epithelium. Fundus autofluorescence showed hypofluorescence consistent with these spots. Both the scotopic and photopic components of the full-field electroretinographies were non-detectable. Based on these clinical findings, this boy was suspected to have retinitis punctata albescens. Subsequent genetic testing using whole-exome sequencing revealed a novel homozygous variants in the ARL6/BBS3 gene (NM_001278293.3:c.528G>A, (p.Trp176Ter)). A systemic examination by the pediatric department revealed that this boy had a history of a surgical excision of polydactyly on his left foot when he was born, and that he was mildly obese. There were no prominent intellectual or gonadal dysfunctions, no craniofacial or dental abnormalities, no congenital heart disease, and no hearing impairment. He was then clinically and genetically diagnosed with BBS.

CONCLUSION AND IMPORTANCE

In children with night blindness and progressive visual dysfunction, it is important for ophthalmologists to consult clinical geneticists and pediatricians to rule out the possibility of systemic diseases such as BBS.

Altering gene expression using antisense oligonucleotide therapy for hearing loss

Hearing loss affects more than 430 million people, worldwide, and is the third most common chronic physical condition in the United States and Europe (GBD Hearing Loss Collaborators, 2021; NIOSH, 2021; WHO, 2021). The loss of hearing significantly impacts motor and cognitive development, communication, education, employment, and overall quality of life. The inner ear houses the sensory organs for both hearing and balance and provides an accessible target for therapeutic delivery. Antisense oligonucleotides (ASOs) use various mechanisms to manipulate gene expression and can be tailor-made to treat disorders with defined genetic targets. In this review, we discuss the preclinical advancements within the field of the highly promising ASO-based therapies for hereditary hearing loss disorders. Particular focus is on ASO mechanisms of action, preclinical studies on ASO treatments of hearing loss, timing of therapeutic intervention, and delivery routes to the inner ear.

Genetic evaluation of hyperphenylalaninemia patients with tetrahydrobiopterin deficiency in Iranian population: Identification of four novel disease-causing variants

BACKGROUND

Hyperphenylalaninemia (HPA) is the most common inborn error of amino acid metabolism worldwide. At least 2% of HPA cases are caused by a deficiency in tetrahydrobiopterin (BH4) metabolism. Genes such as QDPR and PTS are essential in the BH4 metabolism. This study aims to identify disease-causing variants in HPA patients, which may be helpful in genetic counseling and prenatal diagnosis.

METHODS

A total of 10 HPA patients were enrolled in this study. The coding and adjacent intronic regions of PTS and QDPR genes were examined using Sanger sequencing. Protein modeling was also performed for novel identified variants.

RESULTS

Ten patients and a total of 20 alleles were studied, which led to the identification of 10 different variants. All variants identified in PTS and QDPR were missense, except for the c.383_407del variant in the QDPR. Also, three novel variants were identified in the QDPR, including c.79G>T, c.383_407del and c.488G>A, and a novel variant, c.65C>G, in the PTS.

CONCLUSIONS

Despite the genetic similarities in the disease-causing variants, differences were observed in the Asian and European populations with our populations; As a result, similar but more extensive studies are needed to investigate the distribution of disease-causing variants in genes involved in non-PKU hyperphenylalaninemia.

Clinical Impact of Genetic Diagnosis of Sensorineural Hearing Loss in Adults

HYPOTHESIS

Adult genetic sensorineural hearing loss (SNHL) may be underestimated.

BACKGROUND

The diagnosis of genetic hearing loss is challenging, given its extreme genetic and phenotypic heterogeneity, particularly in adulthood. This study evaluated the utility of next-generation sequencing (NGS) in the etiological diagnosis of adult-onset SNHL.

MATERIALS AND METHODS

Adults (>16 yr old) with SNHL were recruited at the Otolaryngology Department at Marqués de Valdecilla University Hospital (Spain). Environmental factors, acoustic trauma, endolymphatic hydrops, and age-related hearing loss were excluding criteria. An NGS gene panel was used, including 196 genes (OTOgenics v3) or 229 genes (OTOgenics v4) related to syndromic and nonsyndromic hearing loss.

RESULTS

Sixty-five patients were included in the study (average age at the onset of SNHL, 41 yr). Fifteen pathogenic/likely pathogenic variants considered to be causative were found in 15 patients (23% diagnostic yield) in TECTA (4), KCNQ4 (3), GJB2 (2), ACTG1 (1), COL2A1 (1), COCH (1), COCH/COL2A1 (1), STRC (1), and ABHD12 (1). Three patients had syndromic associations (20% of patients with genetic diagnosis) that had not been previously diagnosed (two Stickler type I and one polyneuropathy, hearing loss, ataxia, retinitis pigmentosa, cataract syndrome). Seven variants of unknown significance were found in COL11A1 (1), GSMDE (2), DNTM1 (1), SOX10 (1), EYA4 (1), and TECTA (1).

CONCLUSION

NGS gene panels can provide diagnostic yields greater than 20% for adult SNHL, with a significant proportion of variant of unknown significance that could potentially contribute to increasing diagnostic output. Identifying a genetic cause enables genetic counseling, provides prognostic information and can reveal unrecognized syndromes contributing to an accurate management of their associated manifestations.

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

Novel and Founder Pathogenic Variants in X-Linked Alport Syndrome Families in Greece

Alport syndrome (AS) is the most frequent monogenic inherited glomerulopathy and is also genetically and clinically heterogeneous. It is caused by semi-dominant pathogenic variants in the X-linked COL4A5 (NM_000495.5) gene or recessive variants in the COL4A3/COL4A4 (NM_000091.4/NM_000092.4) genes. The disease manifests in early childhood with persistent microhematuria and can progress to proteinuria and kidney failure in adolescence or early adulthood if left untreated. On biopsy, pathognomonic features include alternate thinning, thickening and lamellation of the glomerular basement membrane (GBM), in the presence of podocyte foot process effacement. Although previous studies indicate a prevalence of AS of about 1/50,000, a recent publication reported a predicted rate of pathogenic COL4A5 variants of 1/2320. We herewith present 98 patients (40 M/58 F) from 26 Greek families. We are selectively presenting the families segregating the X-linked form of AS with pathogenic variants in the COL4A5 gene. We found 21 different pathogenic variants, 12 novel: eight glycine and one proline substitutions in the collagenous domain, one cysteine substitution in the NC1 domain, two premature termination of translation codons, three splicing variants, one 5-bp insertion/frameshift variant, one indel-frameshift variant and four gross deletions. Notably, patients in six families we describe here and three families we reported previously, carried the COL4A5-p.G624D substitution, a founder defect encountered all over Europe which is hypomorphic with mostly milder symptomatology. Importantly, on several occasions, the correct genetic diagnosis reclassified patients as patients with AS, leading to termination of previous immunosuppressive/cyclosporine A therapy and a switch to angiotensin converting enzyme inhibitors (ACEi). With the understanding that all 98 patients span a wide range of ages from infancy to late adulthood, 15 patients (11 M/4 F) reached kidney failure and 11 (10 M/1 F) received a transplant. The prospects of avoiding lengthy diagnostic investigations and erroneous medications, and the advantage of delaying kidney failure with very early administration of renin-angiotensin-aldosterone system (RAAS) blockade, highlights the importance of timely documentation of AS by genetic diagnosis.

Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.

Source discrimination of mine water based on the random forest method

Machine learning is one of the widely used techniques to pattern recognition. Use of the machine learning tools is becoming a more accessible approach for predictive model development in preventing engineering disaster. The objective of the research is to for estimation of water source using the machine learning tools. Random forest classification is a popular machine learning method for developing prediction models in many research settings. The type of mine water in the Pingdingshan coalfield is classified into surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water. Each type of water is encoded with the number 0-4. On the basis of hydrochemical data processing, a random forests model is designed and trained with the hydrochemical data. With respect to the predictive accuracy and robustness, fourfold cross-validation (CV) is adopted for the model training. The results show that the random forests model presented here provides significant guidance for the discrimination of mine water.

Combining genetic constraint with predictions of alternative splicing to prioritize deleterious splicing in rare disease studies

BACKGROUND

Despite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites.

RESULTS

Several existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of splice-altering genetic variation. We infer genic regions with significantly less splice-altering variation than expected to be constrained. The resulting model of regional splicing constraint captures differential splicing constraint across gene and exon categories, and the most constrained genic regions are enriched for pathogenic splice-altering variants. Building from this model, we developed ConSpliceML. This ensemble machine learning approach combines regional splicing constraint with multiple per-nucleotide alternative splicing scores to guide the prediction of deleterious splicing variants in protein-coding genes. ConSpliceML more accurately distinguishes deleterious and benign splicing variants than state-of-the-art splicing prediction methods, especially in "cryptic" splicing regions beyond canonical donor or acceptor splice sites.

CONCLUSION

Integrating a model of genetic constraint with annotations from existing alternative splicing tools allows ConSpliceML to prioritize potentially deleterious splice-altering variants in studies of rare human diseases.

Gene-gene interaction detection with deep learning

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.

Increased chromatin accessibility facilitates intron retention in specific cell differentiation states

Dynamic intron retention (IR) in vertebrate cells is of widespread biological importance. Aberrant IR is associated with numerous human diseases including several cancers. Despite consistent reports demonstrating that intrinsic sequence features can help introns evade splicing, conflicting findings about cell type- or condition-specific IR regulation by trans-regulatory and epigenetic mechanisms demand an unbiased and systematic analysis of IR in a controlled experimental setting. We integrated matched mRNA sequencing (mRNA-Seq), whole-genome bisulfite sequencing (WGBS), nucleosome occupancy methylome sequencing (NOMe-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) data from primary human myeloid and lymphoid cells. Using these multi-omics data and machine learning, we trained two complementary models to determine the role of epigenetic factors in the regulation of IR in cells of the innate immune system. We show that increased chromatin accessibility, as revealed by nucleosome-free regions, contributes substantially to the retention of introns in a cell-specific manner. We also confirm that intrinsic characteristics of introns are key for them to evade splicing. This study suggests an important role for chromatin architecture in IR regulation. With an increasing appreciation that pathogenic alterations are linked to RNA processing, our findings may provide useful insights for the development of novel therapeutic approaches that target aberrant splicing.

Real-world data: a brief review of the methods, applications, challenges and opportunities

Background

The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of digital data, providing a valuable data source beyond the confines of traditional clinical trials, epidemiological studies, and lab-based experiments.

Methods

We provide a brief overview on the type and sources of real-world data and the common models and approaches to utilize and analyze real-world data. We discuss the challenges and opportunities of using real-world data for evidence-based decision making This review does not aim to be comprehensive or cover all aspects of the intriguing topic on RWD (from both the research and practical perspectives) but serves as a primer and provides useful sources for readers who interested in this topic.

Results and Conclusions

Real-world hold great potential for generating real-world evidence for designing and conducting confirmatory trials and answering questions that may not be addressed otherwise. The voluminosity and complexity of real-world data also call for development of more appropriate, sophisticated, and innovative data processing and analysis techniques while maintaining scientific rigor in research findings, and attentions to data ethics to harness the power of real-world data.

Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

It is estimated that 10%-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction of splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants that overlapped with branch sites and collected 5,884 variants across the 5' splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. Whereas the splice-altering mechanism of the 3' intronic variants is complex, that of the 5' is mainly splice-site destruction. Statistical learning combined with these molecular features allows precise prediction of altered splicing from an intronic variant. This statistical model provides the identity and ranking of biological features that determine splicing, which serves as transferable knowledge and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in the human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

Alternative splicing is crucial for a wide range of biological processes. However, limited by the availability of reference genomes, genome-wide patterns of alternative splicing remain unknown in most nonmodel organisms. We present an attention-based convolutional neural network model, DeepASmRNA, for predicting alternative splicing events using only transcriptomic data. DeepASmRNA consists of two parts: identification of alternatively spliced transcripts and classification of alternative splicing events, which outperformed the state-of-the-art method, AStrap, and other deep learning models. Then, we utilize transfer learning to increase the performance in species with limited training data and use an interpretation method to decipher splicing codes. Finally, applying Amborella, DeepASmRNA can identify more AS events than AStrap while maintaining the same level of precision, suggesting that DeepASmRNA has superior sensitivity to identify alternative splicing events. In summary, DeepASmRNA is scalable and interpretable for detecting genome-wide patterns of alternative splicing in species without a reference genome.

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DeepASmRNA uses only the transcriptome to predict alternative splicing events

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DeepASmRNA identifies adjacent HSPs to greatly improve the recall

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DeepASmRNA uses attention-based convolutional neural network to classify AS events

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Transfer learning is used to increase the predictive power of a target species

Global research trends on precision oncology: A systematic review, bibliometrics, and visualized study

BACKGROUND

Advances in next-generation sequencing technologies are changing the ways cancer diagnosis and treatment, which leads to a new branch of precision medicine: "Precision Oncology". This study aims to deliver a structured overview to carry out a bibliometric analysis of precision oncology research over the past 10 years retrospectively.

METHODS

Bibliometric methods including clustering analysis and co-occurrence visualized study were conducted based on publications of academic databases Web of Science Main Collection from 1st January 2012, to 31st December 2021. This study analyzed the information about related research outputs, countries, institutions, authors, cited papers, and hot topics.

RESULTS

7163 papers related to precision oncology were identified. Since 2014, the number of articles has proliferated, and oncology precision has attracted significant attention from scholars worldwide in recent years. The USA leads the research in this field, and the League of European Research Universities is the primary research institution. Research institutions from Asia paid more attention to this field through high-level international cooperation. Besides, there are still many issues expected to be explored and evaluated correctly. Such as the considerable uncertainty that pharmacogenomic methods have no significant influence on patient outcomes.

CONCLUSIONS

Precision oncology serves as an essential method in clinical treatment, and is closely related to biological study, including biochemistry, molecular and genetics, advanced technology, and pharmacology discovery. The future research prospect would be the broad involvement of social participation and global cooperation in oncology precision research to acquire better results via the balance of technology and public health policy.

Exploration of Tools for the Interpretation of Human Non-Coding Variants

The advent of Whole Genome Sequencing (WGS) broadened the genetic variation detection range, revealing the presence of variants even in non-coding regions of the genome, which would have been missed using targeted approaches. One of the most challenging issues in WGS analysis regards the interpretation of annotated variants. This review focuses on tools suitable for the functional annotation of variants falling into non-coding regions. It couples the description of non-coding genomic areas with the results and performance of existing tools for a functional interpretation of the effect of variants in these regions. Tools were tested in a controlled genomic scenario, representing the ground-truth and allowing us to determine software performance.

A Unique Mechanism of a Novel Synonymous PHEX Variant Causing X-Linked Hypophosphatemia

CONTEXT

Synonymous mutations are usually nonpathogenic.

OBJECTIVE

We report here a family with X-linked hypophosphatemia (XLH) due to a novel synonymous PHEX variant with a unique mechanism.

METHODS

We studied a 4-member family (a mother, a son, and 2 daughters), all affected with XLH. Genomic DNA was extracted from peripheral leucocytes. Whole exome sequencing (WES) was used to identify the underlying genetic variant in the proband (the son). Sanger sequencing was used to confirm this variant in the proband and his family members. RT-PCR and sequencing of the cDNA revealed the effect of this variant on the PHEX structure and function.

RESULTS

A synonymous variant in the PHEX gene (c.1701A>C) was identified in all affected members. This variant changes the first nucleotide of exon 17 from adenine to cytosine. Using RT-PCR, this variant was shown to interfere with splicing of exons 16 with 17 resulting in a single shorter PHEX transcript in the proband compared to normal control. Sanger sequencing of the cDNA revealed a complete skipping of exon 17 and direct splicing of exons 16 and 18. This led to a frameshift and an introduction of a new stop codon in the next codon (codon 568), which ultimately led to truncation and loss of the final 183 amino acids of PHEX.

CONCLUSION

This novel variant shows how a synonymous exonic mutation may induce a complex series of changes in the transcription and translation of the gene and causes a disease, a mechanism that is not commonly recognized.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.

Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry: A Comparison Study

Neurotransmitters, such as dopamine and serotonin, are responsible for mediating a wide array of neurologic functions, from memory to motivation. From measurements using fast scan cyclic voltammetry (FSCV), one of the main tools used to detect synaptic efflux of neurochemicals in vivo, principal component regression (PCR), has been commonly used to predict the identity and concentrations of neurotransmitters. However, the sensitivity and discrimination performance of PCR have room for improvement, especially for analyzing mixtures of similar oxidizable neurochemicals. Deep learning may be able to address these challenges. To date, there have been a few studies to apply machine learning to FSCV, but no attempt to apply deep learning to neurotransmitter mixture discrimination and no comparative study have been performed between PCR and deep learning methods to demonstrate which is more accurate for FSCV analysis so far. In this study, we compared the neurochemical identification and concentration estimation performance of PCR and deep learning in an analysis of FSCV recordings of catecholamine and indolamine neurotransmitters. Both analysis methods were tested on in vitro FSCV data with a single or mixture of neurotransmitters at the desired concentration. In addition, the estimation performance of PCR and deep learning was compared in incorporation with in vivo experiments to evaluate the practical usage. Pharmacological tests were also conducted to see whether deep learning would track the increased amount of catecholamine levels in the brain. Using conventional FSCV, we used five electrodes and recorded in vitro background-subtracted cyclic voltammograms from four neurotransmitters, dopamine, epinephrine, norepinephrine, and serotonin, with five concentrations of each substance, as well as various mixtures of the four analytes. The results showed that the identification accuracy errors were reduced 5-20% by using deep learning compared to using PCR for mixture analysis, and the two methods were comparable for single analyte analysis. The applied deep-learning-based method demonstrated not only higher identification accuracy but also better discrimination performance than PCR for mixtures of neurochemicals and even for in vivo testing. Therefore, we suggest that deep learning should be chosen as a more reliable tool to analyze FSCV data compared to conventional PCR methods although further work is still needed on developing complete validation procedures prior to widespread use.

i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability

Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.

Coffin-Lowry Syndrome Induced by RPS6KA3 Gene Variation in China: A Case Report in Twins

Background and objectives: Coffin-Lowry Syndrome (CLS), a rare neurodegenerative disorder, is mainly diagnosed based on clinical manifestations and molecular analyses. In total, about 20 cases of CLS have been reported in China. Here, we report two cases of CLS in identical twin brothers and examine their potential causative mutations. Methods: The Trio mode was used in this analysis, i.e., DNA from the proband and his parents was sequenced. Furthermore, DNA from the proband’s twin brother was used for confirmation. Results: A hemizygous variation was detected in the 11th exon of the RPS6KA3 gene, c.898C>T (p.R300*) of the proband, and the same site variation was detected in his identical twin brother; however, the mutation was not detected in his parents. Conclusions: The RPS6KA3 gene mutation c.898C>T (p.R300*) is the causative factor of familial CLS. The variant detected was reported for the first time in the Chinese population. Additionally, by analyzing the previous literature, we were able to summarize the phenotypic and genetic characteristics of GLS in China.

Identification of a novel heterozygous missense TP63 variant in a Chinese pedigree with split-hand/foot malformation

BACKGROUND

Tumor protein p63 is an important transcription factor regulating epithelial morphogenesis. Variants associated with the TP63 gene are known to cause multiple disorders. In this study, we determined the genetic cause of split-hand/foot malformation in a Chinese pedigree.

METHODS

For this study, we have recruited a Chinese family and collected samples from affected and normal individuals of the family (three affected and two normal). Whole exome sequencing was performed to detect the underlying genetic defect in this family. The potential variant was validated using the Sanger sequencing approach.

RESULTS

Using whole-exome and Sanger sequencing, we identified a novel heterozygous pathogenic missense variant in TP63 (NM_003722.5: c.921G > T; p.Met307Ile). This variant resulted in the substitution of methionine with isoleucine. Structural analysis suggested a resulting change in the structure of a key functional domain of the p63 protein.

CONCLUSION

This novel missense variant expands the TP63 variant spectrum and provides a basis for genetic counseling and prenatal diagnosis of families with split-hand/foot malformation or other TP63-related diseases.

Temporal Dynamic Analysis of Alternative Splicing During Embryonic Development in Zebrafish

Alternative splicing is pervasive in mammalian genomes and involved in embryo development, whereas research on crosstalk of alternative splicing and embryo development was largely restricted to mouse and human and the alternative splicing regulation during embryogenesis in zebrafish remained unclear. We constructed the alternative splicing atlas at 18 time-course stages covering maternal-to-zygotic transition, gastrulation, somitogenesis, pharyngula stages, and post-fertilization in zebrafish. The differential alternative splicing events between different developmental stages were detected. The results indicated that abundance alternative splicing and differential alternative splicing events are dynamically changed and remarkably abundant during the maternal-to-zygotic transition process. Based on gene expression profiles, we found splicing factors are expressed with specificity of developmental stage and largely expressed during the maternal-to-zygotic transition process. The better performance of cluster analysis was achieved based on the inclusion level of alternative splicing. The biological function analysis uncovered the important roles of alternative splicing during embryogenesis. The identification of isoform switches of alternative splicing provided a new insight into mining the regulated mechanism of transcript isoforms, which always is hidden by gene expression. In conclusion, we inferred that alternative splicing activation is synchronized with zygotic genome activation and discovered that alternative splicing is coupled with transcription during embryo development in zebrafish. We also unveiled that the temporal expression dynamics of splicing factors during embryo development, especially co-orthologous splicing factors. Furthermore, we proposed that the inclusion level of alternative splicing events can be employed for cluster analysis as a novel parameter. This work will provide a deeper insight into the regulation of alternative splicing during embryogenesis in zebrafish.

Novel Gene Variants Associated with Primary Ciliary Dyskinesia

OBJECTIVES

To determine the demographic, clinical, and genetic profile of Turkish Caucasian PCD cases.

METHODS

Targeted next-generation sequencing (t-NGS) of 46 nuclear genes was performed in 21 unrelated PCD cases. Sanger sequencing confirmed of potentially disease-related variations, and genotype-phenotype correlations were evaluated.

RESULTS

Disease-related variations were identified in eight different genes (CCDC39, CCDC40, CCDC151, DNAAF2, DNAAF4, DNAH11, HYDIN, RSPH4A) in 52.4% (11/21) of the cases. The frequency of variations for CCDC151, DNAH11, and DNAAF2 genes which were highly mutated genes in the cohort was 18% in 11 patients. Each of the remaining gene variations was detected once (9%) in different patients. The variants, p.R482fs*12 in CCDC151, p.E216* in DNAAF2, p.I317* in DNAAF4, p.L318P and p.R1865* in DNAH11, and p.N1505D and p.L1167P in HYDIN gene were identified as novel variations. Interestingly, varying phenotypic findings were identified even in patients with the same mutation, which once again confirmed that PCD has a high phenotypic heterogeneity and shows individual differences.

CONCLUSION

This t-NGS panel is potentially helpful for exact and rapid identification of reported/novel PCD-disease-causing variants to establish the molecular diagnosis of ciliary diseases.

DeepIII: Predicting Isoform-Isoform Interactions by Deep Neural Networks and Data Fusion

Alternative splicing enables a gene translating into different isoforms and into the corresponding proteoforms, which actually accomplish various biological functions of a living body. Isoform-isoform interactions (IIIs) provide a higher resolution interactome to explore the cellular processes and disease mechanisms than the canonically studied protein-protein interactions (PPIs), which are often recorded at the coarse gene level. The knowledge of IIIs is critical to map pathways, understand protein complexity and functional diversity, but the known IIIs are very scanty. In this paper, we propose a deep learning based method called DeepIII to systematically predict genome-wide IIIs by integrating diverse data sources, including RNA-seq datasets of different human tissues, exon array data, domain-domain interactions (DDIs) of proteins, nucleotide sequences and amino acid sequences. Particularly, DeepIII fuses these data to learn the representation of isoform pairs with a four-layer deep neural networks, and then performs binary classification on the learnt representation to achieve the prediction of IIIs. Experimental results show that DeepIII achieves a superior prediction performance to the state-of-the-art solutions and the III network constructed by DeepIII gives more accurate isoform function prediction. Case studies further confirm that DeepIII can differentiate the individual interaction partners of different isoforms spliced from the same gene. The code and datasets of DeepIII are available at http://mlda.swu.edu.cn/codes.php?name=DeepIII.

DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the associations between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses gene-isoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two sub-networks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC (area under the precision-recall curve) of 0.9141, macro F-measure of 0.9155, G-mean of 0.9278 and balanced accuracy of 0.9303 across 732 diseases, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborates the superiority of DeepIDA. The code of DeepIDA is available at http://mlda.swu.edu.cn/codes.php?name=DeepIDA.

Novel Genetic Diagnoses in Septo-Optic Dysplasia

Septo-optic dysplasia (SOD) is a developmental phenotype characterized by midline neuroradiological anomalies, optic nerve hypoplasia, and pituitary anomalies, with a high degree of variability and additional systemic anomalies present in some cases. While disruption of several transcription factors has been identified in SOD cohorts, most cases lack a genetic diagnosis, with multifactorial risk factors being thought to play a role. Exome sequencing in a cohort of families with a clinical diagnosis of SOD identified a genetic diagnosis in 3/6 families, de novo variants in SOX2, SHH, and ARID1A, and explored variants of uncertain significance in the remaining three. The outcome of this study suggests that investigation for a genetic etiology is warranted in individuals with SOD, particularly in the presence of additional syndromic anomalies and when born to older, multigravida mothers. The identification of causative variants in SHH and ARID1A further expands the phenotypic spectra associated with these genes and reveals novel pathways to explore in septo-optic dysplasia.

Multi-omics to characterize the functional relationships of R-loops with epigenetic modifications, RNAPII transcription and gene expression

Abnormal accumulation of R-loops results in replication stress, genome instability, chromatin alterations and gene silencing. Little research has been done to characterize functional relationships among R-loops, histone marks, RNA polymerase II (RNAPII) transcription and gene regulation. We built extremely randomized trees (ETs) models to predict the genome-wide R-loops using RNAPII and multiple histone modifications chromatin immunoprecipitation (ChIP)-seq, DNase-seq, Global Run-On sequencing (GRO-seq) and R-loop profiling data. We compared the performance of ET models to multiple machine learning approaches, and the proposed ET models achieved the best and extremely robust performances. Epigenetic profiles are highly predictive of R-loops genome-widely and they are strongly associated with R-loop formation. In addition, the presence of R-loops is significantly correlated with RNAPII transcription activity, H3K4me3 and open chromatin around the transcription start site, and H3K9me1 and H3K9me3 around the transcription termination site. RNAPII pausing defects were correlated with 5′R-loops accumulation, and transcriptional termination defects and read-throughs were correlated with 3′R-loops accumulation. Furthermore, we found driver genes with 5′R-loops and RNAPII pausing defects express significantly higher and genes with 3′R-loops and read-through transcription express significantly lower than genes without R-loops. These driver genes are enriched with chromosomal instability, Hippo–Merlin signaling Dysregulation, DNA damage response and TGF-β pathways, indicating R-loops accumulating at the 5′ end of genes play oncogenic roles, whereas at the 3′ end of genes play tumor-suppressive roles in tumorigenesis.

A deep convolutional neural network to predict the curve progression of adolescent idiopathic scoliosis: a pilot study

BACKGROUND

Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that predominantly occurs in girls. While skeletal growth and maturation influence the development of AIS, accurate prediction of curve progression remains difficult because the prognosis for deformity differs among individuals. The purpose of this study is to develop a new diagnostic platform using a deep convolutional neural network (DCNN) that can predict the risk of scoliosis progression in patients with AIS.

METHODS

Fifty-eight patients with AIS (49 females and 9 males; mean age: 12.5 ± 1.4 years) and a Cobb angle between 10 and 25 degrees (mean angle: 18.7 ± 4.5) were divided into two groups: those whose Cobb angle increased by more than 10 degrees within two years (progression group, 28 patients) and those whose Cobb angle changed by less than 5 degrees (non-progression group, 30 patients). The X-ray images of three regions of interest (ROIs) (lung [ROI1], abdomen [ROI2], and total spine [ROI3]), were used as the source data for learning and prediction. Five spine surgeons also predicted the progression of scoliosis by reading the X-rays in a blinded manner.

RESULTS

The prediction performance of the DCNN for AIS curve progression showed an accuracy of 69% and an area under the receiver-operating characteristic curve of 0.70 using ROI3 images, whereas the diagnostic performance of the spine surgeons showed inferior at 47%. Transfer learning with a pretrained DCNN contributed to improved prediction accuracy.

CONCLUSION

Our developed method to predict the risk of scoliosis progression in AIS by using a DCNN could be a valuable tool in decision-making for therapeutic interventions for AIS.

Relevance and Regulation of Alternative Splicing in Plant Heat Stress Response: Current Understanding and Future Directions

Alternative splicing (AS) is a major mechanism for gene expression in eukaryotes, increasing proteome diversity but also regulating transcriptome abundance. High temperatures have a strong impact on the splicing profile of many genes and therefore AS is considered as an integral part of heat stress response. While many studies have established a detailed description of the diversity of the RNAome under heat stress in different plant species and stress regimes, little is known on the underlying mechanisms that control this temperature-sensitive process. AS is mainly regulated by the activity of splicing regulators. Changes in the abundance of these proteins through transcription and AS, post-translational modifications and interactions with exonic and intronic cis-elements and core elements of the spliceosomes modulate the outcome of pre-mRNA splicing. As a major part of pre-mRNAs are spliced co-transcriptionally, the chromatin environment along with the RNA polymerase II elongation play a major role in the regulation of pre-mRNA splicing under heat stress conditions. Despite its importance, our understanding on the regulation of heat stress sensitive AS in plants is scarce. In this review, we summarize the current status of knowledge on the regulation of AS in plants under heat stress conditions. We discuss possible implications of different pathways based on results from non-plant systems to provide a perspective for researchers who aim to elucidate the molecular basis of AS under high temperatures.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Splicing mutations in the CFTR gene as therapeutic targets

The marketing approval, about ten years ago, of the first disease modulator for patients with cystic fibrosis harboring specific CFTR genotypes (~5% of all patients) brought new hope for their treatment. To date, several therapeutic strategies have been approved and the number of CFTR mutations targeted by therapeutic agents is increasing. Although these drugs do not reverse the existing disease, they help to increase the median life expectancy. However, on the basis of their CFTR genotype, ~10% of patients presently do not qualify for any of the currently available CFTR modulator therapies, particularly patients with splicing mutations (~12% of the reported CFTR mutations). Efforts are currently made to develop therapeutic agents that target disease-causing CFTR variants that affect splicing. This highlights the need to fully identify them by scanning non-coding regions and systematically determine their functional consequences. In this review, we present some examples of CFTR alterations that affect splicing events and the different therapeutic options that are currently developed and tested for splice switching.

An Educational Assessment of Evidence Used for Variant Classification: A Report of the Association for Molecular Pathology

The Association for Molecular Pathology Variant Interpretation Testing Among Laboratories (VITAL) Working Group convened to evaluate the Standards and Guidelines for the Interpretation of Sequence Variants implementation into clinical practice, identify problematic classification rules, and define implementation challenges. Variants and associated clinical information were provided to volunteer respondents. Participant variant classifications were compared with intended consensus-derived classifications of the Working Group. The 24 variant challenges received 1379 responses; 1119 agreed with the intended response (81%; 95% CI, 79% to 83%). Agreement ranged from 44% to 100%, with 16 challenges (67%; 47% to 82%) reaching consensus (≥80% agreement). Participant classifications were also compared to a calculated interpretation of the ACMG Guidelines using the participant-reported criteria as input. The 24 variant challenges had 1368 responses with specific evidence provided and 1121 (82%; 80% to 84%) agreed with the calculated interpretation. Agreement for challenges ranged from 63% to 98%; 15 (63%; 43% to 79%) reaching consensus. Among 81 individual participants, 32 (40%; 30% to 50%) reached agreement with at least 80% of the intended classifications and 42 (52%; 41% to 62%) with the calculated classifications. This study demonstrated that although variant classification remains challenging, published guidelines are being utilized and adapted to improve variant calling consensus. This study identified situations where clarifications are warranted and provides a model for competency assessment.

Alteration in DNA-binding affinity of Wilms tumor 1 protein due to WT1 genetic variants associated with steroid - resistant nephrotic syndrome in children

Approximately one third of children with steroid-resistant nephrotic syndrome (SRNS) carry pathogenic variants in one of the many associated genes. The WT1 gene coding for the WT1 transcription factor is among the most frequently affected genes. Cases from the Czech national SRNS database were sequenced for exons 8 and 9 of the WT1 gene. Eight distinct exonic WT1 variants in nine children were found. Three children presented with isolated SRNS, while the other six manifested with additional features. To analyze the impact of WT1 genetic variants, wild type and mutant WT1 proteins were prepared and the DNA-binding affinity of these proteins to the target EGR1 sequence was measured by microscale thermophoresis. Three WT1 mutants showed significantly decreased DNA-binding affinity (p.Arg439Pro, p.His450Arg and p.Arg463Ter), another three mutants showed significantly increased binding affinity (p.Gln447Pro, p.Asp469Asn and p.His474Arg), and the two remaining mutants (p.Cys433Tyr and p.Arg467Trp) showed no change of DNA-binding affinity. The protein products of WT1 pathogenic variants had variable DNA-binding affinity, and no clear correlation with the clinical symptoms of the patients. Further research is needed to clarify the mechanisms of action of the distinct WT1 mutants; this could potentially lead to individualized treatment of a so far unfavourable disease.

LOEN: Lensless opto-electronic neural network empowered machine vision

Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.

Roles of Physicochemical and Structural Properties of RNA-Binding Proteins in Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning

Trans-acting splicing factors play a pivotal role in modulating alternative splicing by specifically binding to cis-elements in pre-mRNAs. There are approximately 1500 RNA-binding proteins (RBPs) in the human genome, but the activities of these RBPs in alternative splicing are unknown. Since determining RBP activities through experimental methods is expensive and time consuming, the development of an efficient computational method for predicting the activities of RBPs in alternative splicing from their sequences is of great practical importance. Recently, a machine learning model for predicting the activities of splicing factors was built based on features of single and dual amino acid compositions. Here, we explored the role of physicochemical and structural properties in predicting their activities in alternative splicing using machine learning approaches and found that the prediction performance is significantly improved by including these properties. By combining the minimum redundancy–maximum relevance (mRMR) method and forward feature searching strategy, a promising feature subset with 24 features was obtained to predict the activities of RBPs. The feature subset consists of 16 dual amino acid compositions, 5 physicochemical features, and 3 structural features. The physicochemical and structural properties were as important as the sequence composition features for an accurate prediction of the activities of splicing factors. The hydrophobicity and distribution of coil are suggested to be the key physicochemical and structural features, respectively.

Copper-histidine therapy in an infant with novel splice-site variant in the ATP7A gene of Menkes disease: the first experience in South East Asia and literature review

Menkes disease (MD) is an X linked recessive multi-systemic disorder of copper metabolism, resulting from an ATP7A gene mutation. We report a male infant aged 4 months who presented with kinky hair, hypopigmented skin, epilepsy and delayed development. Magnetic resonance imaging (MRI) of brain demonstrated multiple tortuosities of intracranial vessels and brain atrophy. Investigation had showed markedly decreased serum copper and ceruloplasmin. The novel c.2172+1G>T splice-site mutation in the ATP7A gene confirmed MD. He was treated with subcutaneous administration of locally prepared copper-histidine (Cu-His). Following the therapy, hair manifestation was restored and serum ceruloplasmin was normalised 1 month later. Despite the treatment, epilepsy, neurodevelopment and osteoporosis still progressed. He died from severe respiratory tract infection at the age of 9.5 months. These findings suggest that the benefit of Cu-His in our case is limited which might be related to severe presentations and degree of ATP7A mutation.

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.

Evolution of the Human Brain Can Help Determine Pathophysiology of Neurodevelopmental Disorders

The evolution of humans brought about a co-occurring evolution of the human brain, which is far larger and more complex than that of many other organisms. The brain has evolved characteristically in humans in many respects, including macro-and micro-anatomical changes in the brain structure, changes in gene expression, and cell populations and ratios. These characteristics are essential for the execution of higher functions, such as sociality, language, and cognition, which express humanity, and are thought to have been acquired over evolutionary time. However, with the acquisition of higher functions also comes the risk of the disease in which they fail. This review focuses on human brain evolution and neurodevelopmental disorders (NDDs) and discusses brain development, molecular evolution, and human brain evolution. Discussing the potential for the development and pathophysiology of NDDs acquired by human brain evolution will provide insights into the acquisition and breakdown of higher functions from a new perspective.