Pathogenicity Prediction Research Articles

Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2.

CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia (CAH) cases, a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provide critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Here, we analyzed the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of CYP21A2. SNVs of CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. All SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy of between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews’ correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). From our analysis, we identified four predictors of CYP21A2 variant pathogenicity with good performance, CADD, ConSurf, DANN, and PolyPhen2. These results can be used for future analysis to infer the impact of uncharacterized SNVs in CYP21A2.

P.09 Comprehensive database for RYR1-related disorders: concept and progress update

Approximately 300 pathogenic <i>RYR1</i> variants have been reported to date accounting for ∼30% of congenital myopathies. <i>RYR1</i> encodes the type one ryanodine receptor (RyR1), a 2.2 megadalton ion channel expressed in skeletal muscle that tightly regulates intracellular calcium flux. Pathogenic <i>RYR1</i> variants result in a wide spectrum of monoallelic and biallelic congenital, late-onset, and triggered neuromuscular disease phenotypes for which there is no approved treatment. Moreover, the >2000 <i>RYR1</i> variants of uncertain significance (VUS) present a diagnostic challenge with a majority being uncharacterized in <i>in vitro</i> model systems and the limited utility of bioinformatic pathogenicity prediction tools for <i>RYR1</i>. In 2017, the RYR-1 Foundation provided seed funding to develop an international database of nonclinical and clinical data published on <i>RYR1</i>-related disorders to advance therapeutic development, support clinical interpretation, and inform the patient community. To date, two datasets have been developed (1) clinical genotype-phenotype dataset on 2500 patient cases, and (2) nonclinical dataset on cellular and animal model systems that includes >250 unique <i>RYR1</i> variants. Using commercially available variant curation software, human phenotype ontologies linked to <i>RYR1</i> variants are extracted from published works on an ongoing basis and organized by MedDRA system organ class. A Boolean search strategy is used to extract relevant non-clinical published works from PubMed and EMBASE. The database taxonomy also incorporates genetic, bioinformatic, structural, and model system data on each variant, as available. The long-term goal is to make these datasets publicly available via an online platform to encourage international research collaboration on <i>RYR1</i>-related disorders.

Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants.

Background: Existing BRCA2-specific variant pathogenicity prediction algorithms focus on the prediction of the functional impact of a subtype of variants alone. General variant effect predictors are applicable to all subtypes, but are trained on putative benign and pathogenic variants and do not account for gene-specific information, such as hotspots of pathogenic variants. Local, gene-specific information have been shown to aid variant pathogenicity prediction; therefore, our aim was to develop a BRCA2-specific machine learning model to predict pathogenicity of all types of BRCA2 variants. Methods: We developed an XGBoost-based machine learning model to predict pathogenicity of BRCA2 variants. The model utilizes general variant information such as position, frequency, and consequence for the canonical BRCA2 transcript, as well as deleteriousness prediction scores from several tools. We trained the model on 80% of the expert reviewed variants by the Evidence-Based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium and tested its performance on the remaining 20%, as well as on an independent set of variants of uncertain significance with experimentally determined functional scores. Results: The novel gene-specific model predicted the pathogenicity of ENIGMA BRCA2 variants with an accuracy of 99.9%. The model also performed excellently on predicting the functional consequence of the independent set of variants (accuracy was up to 91.3%). Conclusion: This new, gene-specific model is an accurate method for interpreting the pathogenicity of variants in the BRCA2 gene. It is a valuable addition for variant classification and can prioritize unreviewed variants for functional analysis or expert review.

基于网络和基因差异表达信息的癌症致病基因预测

<p indent="0mm">In genome research, the discovery of disease-causing pathogenic genes is a major challenge in genome research. Recently, using the available biological data, many researchers have employed computational methods for pathogenic gene prediction. However, most of these computational methods are based on gene interaction networks or other similar networks, and the potential connection between the local network of specific genes and their differential expression information is rarely considered. This paper explores the biological properties of cancerous pathogenic genes and their neighbors based on a local network structure and the differential expression information of genes. Furthermore, machine learning methods are employed for predicting cancerous pathogenic genes based on the newly discovered biological properties. First, the expression data of 21 cancers and their pathogenic genes are obtained from the TCGA (The Cancer Genome Atlas) and OMIM (Online Mendelian Inheritance in Man) databases. Human-protein interaction and tissue-specific interaction networks corresponding to each cancer gene are used as background networks to analyze the potential biological properties between the neighborhood information of different networks and the changed information of the gene expression. Then, based on the discovered biological properties, a vector representation method for gene node features is established, and the support vector machine is employed for pathogenic gene prediction. Experimental results are verified using ICGC (International Cancer Genome Consortium), COSMIC (Catalogue Of Somatic Mutations In Cancer), NCG (Network of Cancer Genes), OncoKB (Oncology Knowledge Base), and other standard databases, as well as using related literature, disease annotations and pathway enrichments. Results show that the definition of gene features can be used to distinguish cancerous pathogenic genes from other genes, thus providing powerful hypotheses for cancerous pathogenic gene prediction. Furthermore, reliable pathogenic gene candidates are obtained for related biological experiments to promote research on the pathogenic mechanism of cancer, a complex disease.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey.

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

The inherited cerebellar ataxias: an update.

This narrative review aims at providing an update on the management of inherited cerebellar ataxias (ICAs), describing main clinical entities, genetic analysis strategies and recent therapeutic developments. Initial approach facing a patient with cerebellar ataxia requires family medical history, physical examination, exclusions of acquired causes and genetic analysis, including Next-Generation Sequencing (NGS). To guide diagnosis, several algorithms and a new genetic nomenclature for recessive cerebellar ataxias have been proposed. The challenge of NGS analysis is the identification of causative variant, trio analysis being usually the most appropriate option. Public genomic databases as well as pathogenicity prediction software facilitate the interpretation of NGS results. We also report on key clinical points for the diagnosis of the main ICAs, including Friedreich ataxia, CANVAS, polyglutamine spinocerebellar ataxias, Fragile X-associated tremor/ataxia syndrome. Rarer forms should not be neglected because of diagnostic biomarkers availability, disease-modifying treatments, or associated susceptibility to malignancy. Diagnostic difficulties arise from allelic and phenotypic heterogeneity as well as from the possibility for one gene to be associated with both dominant and recessive inheritance. To complicate the phenotype, cerebellar cognitive affective syndrome can be associated with some subtypes of cerebellar ataxia. Lastly, we describe new therapeutic leads: antisense oligonucleotides approach in polyglutamine SCAs and viral gene therapy in Friedreich ataxia. This review provides support for diagnosis, genetic counseling and therapeutic management of ICAs in clinical practice.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00415-022-11383-6.

A computational approach to biological pathogenicity.

The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.

Prevalence estimates of putatively pathogenic leptin variants in the gnomAD database.

Homozygosity for pathogenic variants in the leptin gene leads to congenital leptin deficiency causing severe early-onset obesity. This monogenic form of obesity has mainly been detected in patients from consanguineous families. Prevalence estimates for the general population using the Exome Aggregation Consortium (ExAC) database reported a low frequency of leptin mutations. One in approximately 15 million individuals will be homozygous for a deleterious leptin variant. With the present study, we aimed to extend these findings utilizing the augmented Genome Aggregation Database (gnomAD) v2.1.1 including more than 140,000 samples. In total, 68 non-synonymous and 7 loss-of-function leptin variants were deposited in gnomAD. By predicting functional implications with the help of in silico tools, like SIFT, PolyPhen2 and MutationTaster2021, the prevalence of hetero- and homozygosity for putatively pathogenic variants (n = 32; pathogenic prediction by at least two tools) in the leptin gene were calculated. Across all populations, the estimated prevalence for heterozygosity for functionally relevant variants was approximately 1:2,100 and 1:17,830,000 for homozygosity. This prevalence deviated between the individual populations. Accordingly, people from East Asia and individuals of mixed ethnicities ('Others') were at greater risk to carry a possibly damaging leptin variant. Generally, this study emphasises the scarcity of pathogenic leptin variants in the general population with varying prevalence for distinct study groups.

Landscape of exitrons in gastric cancer.

SummaryBackgroundExitron is a new type of non-canonical alternative splicing. Accumulating evidence implies exitron may have pathological function and contribute to another source of anti-tumor immunogenicity in various cancers. Its role in gastric cancer remains poorly understood. Large-scale, multi-omics analysis could comprehensively characterize the landscape of exitrons in gastric cancer, reveal undiscovered mechanism and hopefully identify molecular biomarkers for predicting immunotherapy response.MethodsWe collected datasets from five studies for analysis. RNA sequencing was used for exitron identification. Somatic mutations were detected by whole exome sequencing. Neopeptides were confirmed by proteome mass spectrometry.Findings42174 gastric cancer-specific exitrons (GCSEs) were identified in 632 patients. GCSEs were clinically relevant to gender, age, Lauren type, tumor stage and prognosis. Tissue specificity test and pathogenic exitron prediction revealed their unique functional impact. GCSEs were mutually exclusive with mutations and demonstrated both unique and complementary function against TP53 mutation in gastric cancer. We further established splicing regulatory network to reveal upstream regulation of exitron splicing. We also evaluated the immunogenicity and diagnostic potential of GCSEs. Evidence of GCSEs-derived neopeptide expression was validated by whole proteome mass spectrometry. PD-1 and Siglecs were significantly increased in high neoantigen load patients. But exitron-related biomarkers failed to predict immunotherapy response, possibly due to small sample size and insufficient sequencing depth.InterpretationThe present study provided a comprehensive multidimensional landscape of gastric cancer exitrons and underscores insights into underexplored mechanism in gastric cancer pathology.FundingThe Guangdong Provincial Key Laboratory of Precision Medicine for Gastroinstestinal Cancer (2020B121201004), the Guangdong Provincial Major Talents Project (No. 2019JC05Y361) and National Natural Science Foundation of China (grant number:82172960 and 81872013).

Identification and in silico analysis of a spectrum of SLC4A11 variations in Indian familial and sporadic cases of congenital hereditary endothelial dystrophy

BackgroundCongenital hereditary endothelial dystrophy (CHED) is a rare form of corneal dystrophy caused by SLC4A11 gene variations. This study aims to find the genetic alterations in SLC4A11, in two Indian familial CHED cases with affected members n = 3 and n = 2 respectively and five sporadic CHED cases using direct sequencing, followed by in silico analysis and characterization of the identified variants.ResultsAll three affected members of the first CHED family were identified with a novel homozygous c.1514C > G (p.Ser489Trp) variation while second family showed presence of a compound heterozygous variation c.529A > C (p.Arg161Arg) + c.2461insT (p.Val805fs). Among five sporadic cases, two showed novel changes, homozygous c.1487G > T (p.Ser480Ile) and c.620-2A > G, while the other one had previously reported homozygous c.2653C > T (p.Arg869Cys) variation. The remaining two cases did not reveal the presence of SLC4A11-related pathogenic variations. The identified variations were excluded from the Indian control (n = 80). In silico analysis using homology-based protein modeling and pathogenicity prediction tools, which revealed these alterations as pathogenic, changing their protein stability, local flexibility, residue contact clashes, and the hydrogen bond interactions.ConclusionsThis study contributed to the CHED mutational spectrum, adding four novel variations and confirming a previously reported one. It demonstrates different type of variations in CHED cases, including coding, non-coding, homozygous, synonymous, and compound heterozygous variations. The identified variations revealed different degrees of pathogenic effects in silico. Moreover, two sporadic cases could not be identified with pathogenic variation emphasizing the involvement of other genes or genetic mechanisms.

Towards a Better Understanding of Genotype-Phenotype Correlations and Therapeutic Targets for Cardiocutaneous Genes: The Importance of Functional Studies above Prediction.

Genetic variants in gene-encoding proteins involved in cell–cell connecting structures, such as desmosomes and gap junctions, may cause a skin and/or cardiac phenotype, of which the combination is called cardiocutaneous syndrome. The cardiac phenotype is characterized by cardiomyopathy and/or arrhythmias, while the skin particularly displays phenotypes such as keratoderma, hair abnormalities and skin fragility. The reported variants associated with cardiocutaneous syndrome, in genes DSP, JUP, DSC2, KLHL24, GJA1, are classified by interpretation guidelines from the American College of Medical Genetics and Genomics. The genotype–phenotype correlation, however, remains poorly understood. By providing an overview of variants that are assessed for a functional protein pathology, we show that this number (n = 115) is low compared to the number of variants that are assessed by in silico algorithms (>5000). As expected, there is a mismatch between the prediction of variant pathogenicity and the prediction of the functional effect compared to the real functional evidence. Aiding to improve genotype–phenotype correlations, we separate variants into ‘protein reducing’ or ‘altered protein’ variants and provide general conclusions about the skin and heart phenotype involved. We conclude by stipulating that adequate prognoses can only be given, and targeted therapies can only be designed, upon full knowledge of the protein pathology through functional investigation.

Study of the frequency and clinical features of maturity-onset diabetes in the young in the pediatric and adolescent diabetes population in Iran.

Maturity-onset diabetes of the young (MODY), an autosomal dominant disease, is frequently misdiagnosed as type 1 or 2 diabetes. Molecular diagnosis is essential to distinguish them. This study was done to investigate theprevalence of MODY subtypes and patients' clinical characteristics. A total of 43 out of 230 individuals with diabetes were selected based on the age of diagnosis >6months, family history of diabetes, absence of marked obesity, and measurable C-peptide. Next-generation and direct SANGER sequencing was performed to screen MODY-related mutations. The variants were interpreted using the Genome Aggregation Database (genomAD), Clinical Variation (ClinVar), and pathogenicity prediction tools. There were 23 males (53.5%), and the mean age at diabetes diagnosis was 6.7±3.6years. Sixteen heterozygote single nucleotide variations (SNVs) from 14 patients (14/230, 6%) were detected, frequently GCK (37.5%) and BLK (18.7%). Two novel variants were identified in HNF4A and ABCC8. Half of the detected variants were categorized as likely pathogenic. Most prediction tools predicted Ser28Cys in HNF4A as benign and Tyr123Phe in ABCC8 as a pathogenic SNV. Six cases (42.8%) with positive MODY SNVs had islet autoantibodies. At diagnosis, age, HbA1c, and C-peptide level were similar between SNV-positive and negative patients. This is the first study investigating 14 variants of MODY in Iran. The results recommend genetic screening for MODY in individuals with unusual type 1 or 2 diabetes even without family history. Treatment modifies depending on the type of patients' MODY and is associated with the quality of life.

Prevalence of escherichia coli and its antibiotic susceptibility profiles among patients presenting with signs and symptoms of UTI in Ramadi city

One of the most prevalent infections, urinary tract infection (UTI) can affect anyone from a newborn to an elderly person. The most common cause is a bacteria called uropathogenic Escherichia coli (UPEC). The ability to adhere to host epithelial cells in the urinary system is the most important predictor of pathogenicity, however fimbriae, pili, flagella, and secreted (toxins) virulence factors all play a role in the pathogenicity of UroPathogenic Escherichia coli strains. Effective management of UTIs requires up-to-date information on the antibiotic susceptibility pattern of uropathogens. Thirty midstream urine samples were collected from patients at Al-Ramadi Teaching Hospital's Urology Clinic. Isolates of E. coli (the most prevalent bacteria) from the cultured samples are tested for susceptibility to the most common antibiotics in use today. Thirty (75%) of the total 40 samples were culture positive; the most common organisms found were Escherichia coli (16 (53.3), Citrobacter freundi 2 (6.7%), Klebsiella pneumonia 8(26.7%), Streptococcus spp 3 (10%), and Staphylococcus aureus 1(3.3%). Females (33.3%) were more likely to be affected than males (20%), and those younger than 13 years old were disproportionately affected.

Computational biology insights into genotype-clinical phenotype-protein phenotype relationships between novel SLC26A2 variants identified in inherited skeletal dysplasias

BackgroundPathogenic variants in the transmembrane sulfate transporter protein SLC26A2 are associated with different phenotypes of inherited chondrodysplasias. As limited data is published from India, in this study we sought to elucidate the molecular basis of inherited chondrodysplasias in an Indian cohort. MethodsMolecular screening of 32 fetuses with antenatally diagnosed lethal skeletal dysplasia was performed by next generation sequencing and Sanger sequencing. The genotype-protein phenotype characterization was done using computational biology techniques like homology modelling, stability and pathogenicity predictions. ResultsWe identified five rare autosomal recessive SLC26A2 [NM_000112.4] variants, including three homozygous c.796dupA(p.Thr266Asnfs*12), c.1724delA(p.Lys575Serfs*10), and c.1375_1377dup(p.Val459dup) and two heterozygous variants (c.532C > T(p.Arg178*)) and (c.1382C > T(p.Ala461Val)) in compound heterozygous form in a total of four foetuses. Genotype-protein phenotype annotations highlighted that the clinically severe achondrogenesis 1B causative c.796dupA(p.Thr266Asnfs*12) and c.1724delA(p.Lys575Serfs*10)variants impact SLC26A2 protein structure by deletion of the protein core and transmembrane STAS domains, respectively. In clinically moderate atelosteogenesis type 2 phenotype, the c.1382C > T(p.Ala461Val) variant is predicted to distort alpha helix conformation and alter the bonding properties and free energy dynamics of transmembrane domains and the c.532C > T(p.Arg178*) variant results in loss of both core transmembrane and STAS domains of the SLC26A2 protein. The c.1375_1377dup(p.Val459dup) variant identified in clinically milder atelosteogenesis type II-diastrophic dysplasia spectrum lethal phenotype is predicted to decrease the Qualitative Model Energy Analysis (QMean), which affects major geometrical aspects of the SLC26A2 protein structure. ConclusionWe expand the spectrum of SLC26A2 related lethal chondrodysplasia and report three novel variants correlating clinical severity and protein phenotype within the lethal spectrum of this rare dysplasia. We demonstrate the relevance of structural characterization to aid novel variant reclassification to provide better prenatal management and reproductive options to families with lethal antenatal skeletal disorder.

Identification of Germinal Neurofibromin Hotspots.

Neurofibromin is engaged in many cellular processes and when the proper protein functioning is impaired, it causes neurofibromatosis type 1 (NF1), one of the most common inherited neurological disorders. Recent advances in sequencing and screening of the NF1 gene have increased the number of detected variants. However, the correlation of these variants with the clinic remains poorly understood. In this study, we analyzed 4610 germinal NF1 variants annotated in ClinVar and determined on exon level the mutational spectrum and potential pathogenic regions. Then, a binomial and sliding windows test using 783 benign and 938 pathogenic NF1 variants were analyzed against functional and structural regions of neurofibromin. The distribution of synonymous, missense, and frameshift variants are statistically significant in certain regions of neurofibromin suggesting that the type of variant and its associated phenotype may depend on protein disorder. Indeed, there is a negative correlation between the pathogenic fraction prediction and the disorder data, suggesting that the higher an intrinsically disordered region is, the lower the pathogenic fraction is and vice versa. Most pathogenic variants are associated to NF1 and our analysis suggests that GRD, CSRD, TBD, and Armadillo1 domains are hotspots in neurofibromin. Knowledge about NF1 genotype–phenotype correlations can provide prognostic guidance and aid in organ-specific surveillance.

In silico mutational analysis to identify the role and pathogenicity of BCL-w missense variants

BackgroundIntrinsic pathway of apoptosis is generally mediated by BCL-2 (B cell lymphoma 2) family of proteins; they either induce or inhibit the apoptosis. Overexpression of BCL-2 in cancer cell may lead to delay in apoptosis. BCL-w is the pro-survival member of the BCL-2 family. BCL2L2 gene is present on chromosome number 14 in humans, and it encodes BCL-w protein; BCL-w protein is 193 amino acids residues in length. Interactions among the BCL-2 proteins are very specific. The fate of cell is determined by the ratio of pro-apoptotic proteins to pro-survival proteins. BCL-w promotes cell survival. Studies suggested that overexpression of BCL-w protein is associated with many cancers including DLBCL, BL, colorectal cancers, gastric cancers, and many more. The cause of overexpression is translocations or gene amplification which will subsequently result in cancerous activity. ProcessFor in-silico analysis, BCL2L2 gene was retrieved from UniProt (UniProt ID: Q92843). 54 missense variants have been collected in BCL-w proteins from COSMIC database. Different tools were used to detect the deleteriousness of the variants. ResultIn silico mutational study reveals how the non-synonymous mutations directly affect the protein’s native structure and its function. Variant mutational analysis with PolyPhen-2 revealed that out of 55 variants, 28 of the missense mutations was probably damaging with a score ranging from 0.9 to 1, while 24 variants were benign with a score ranging from 0 to 0.4. ConclusionsThis in silico work aims to determine how missense mutations in BCL-w protein affect the activity of the protein, the stability of the protein, and to determine the pathogenicity of the variants. Prediction of pathogenicity of variants will reveal if the missense mutation has a damaging effect on the native structure of protein or not. Prediction of protein stability will reveal whether the mutation has a stabilizing or destabilizing effect on the protein.

Identified novel heterozygous HTRA1 pathogenic variants in Chinese patients with HTRA1-associated dominant cerebral small vessel disease

Background: Homozygous and compound heterozygous mutations in HTRA1 cause cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL). Recently, heterozygous pathogenic variants in HTRA1 were described in patients with autosomal dominant cerebral small vessel disease (CSVD). Here, we investigated the genetic variants in a cohort of Chinese patients with CSVD. Methods: A total of 95 Chinese index patients with typical characteristics of CSVD were collected. Whole exome sequencing was performed in the probands, followed by Sanger sequencing. Pathogenicity prediction software was applied to evaluate the pathogenicity of the identified variants. Results: We detected five heterozygous HTRA1 pathogenic variants in five index patients. These pathogenic variants included four known variants (c.543delT, c.854C>T, c.889G>A, and c.824C>T) and one novel variant (c.472 + 1G>A). Among them, c.854C>T, c.824C>T, and c.472 + 1G>A have never been reported in China and c.889G>A was once reported in homozygous but never in heterozygous. Three of them were distributed in exon 4, one in exon 2, and another splicing variant in intron 1. Four out of five probands presented typical features of CARASIL but less severe. The common clinical features included lacunar infarction, cognitive decline, alopecia, and spondylosis. All of them showed leukoencephalopathy, and the main involved cerebral area include periventricular and frontal area, centrum semiovale, thalamus, and corpus callosum. Anterior temporal lobes and external capsule involvement were also observed. Three probands had intracranial microbleeds. Conclusion: Our study expanded the mutation spectrum of HTRA1, especially in Chinese populations, and provided further evidence for “hot regions” in exon 1–4, especially in exon 4, in heterozygous HTRA1 pathogenic variants. Our work further supported that patients with heterozygous HTRA1 pathogenic variants presented with similar but less-severe features than CARASIL but in an autosomal dominantly inherited pattern.

Analysis of genomic alterations in cancer associated human pancreatic stellate cells

Pancreatic stellate cells (PSCs) constitute important cells of the pancreatic microenvironment and their close interaction with cancer cells is important in pancreatic cancer. It is currently not known whether PSCs accumulate genetic alterations that contribute to tumor biology. Our aim was to analyze genetic alterations in cancer associated PSCs. PSC DNA was matched to DNA isolated from pancreatic cancer patients’ blood (n = 5) and analyzed by Next-Generation Sequencing (NGS). Bioinformatic analysis was performed using the GATK software and pathogenicity prediction scores. Sanger sequencing was carried out to verify specific genetic alterations in a larger panel of PSCs (n = 50). NGS and GATK analysis identified on average 26 single nucleotide variants in PSC DNA as compared to the matched blood DNA that could be visualized with the Integrative Genomics Viewer. The absence of PDAC driver mutations (KRAS, p53, p16/INK4a, SMAD4) confirmed that PSC isolations were not contaminated with cancer cells. After filtering the variants, using different pathogenicity scores, ten genes were identified (SERPINB2, CNTNAP4, DENND4B, DPP4, FGFBP2, MIGA2, POLE, SNRNP40, TOP2B, and ZDHHC18) in single samples and confirmed by Sanger sequencing. As a proof of concept, functional analysis using control and SERPINB2 knock-out fibroblasts revealed functional effects on growth, migration, and collagen contraction. In conclusion, PSC DNA exhibit a substantial amount of single nucleotide variants that might have functional effects potentially contributing to tumor aggressiveness.

MvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed Pathogenicity Prediction Tool for missense variants (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at http://www.mvppt.club/.

X-CAP improves pathogenicity prediction of stopgain variants

Stopgain substitutions are the third-largest class of monogenic human disease mutations and often examined first in patient exomes. Existing computational stopgain pathogenicity predictors, however, exhibit poor performance at the high sensitivity required for clinical use. Here, we introduce a new classifier, termed X-CAP, which uses a novel training methodology and unique feature set to improve the AUROC by 18% and decrease the false-positive rate 4-fold on large variant databases. In patient exomes, X-CAP prioritizes causal stopgains better than existing methods do, further illustrating its clinical utility. X-CAP is available at https://github.com/bejerano-lab/X-CAP.