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Purpose: HNRNPU haploinsufficiency is associated with Developmental and Epileptic Encephalopathy 54. This neurodevelopmental disorder is characterized by developmental delay, intellectual disability, speech impairment, and early onset epilepsy. We performed genome-wide DNA methylation (DNAm) analysis in a cohort of individuals to develop a diagnostic biomarker and gain functional insights into the molecular pathophysiology of HNRNPU-related disorder. Methods: DNAm profiles of individuals carrying pathogenic HNRNPU variants, identified through an international multi-center collaboration, were assessed using Infinium Methylation EPIC arrays. Statistical and functional correlation analyses were performed comparing the HNRNPU cohort to 56 previously reported DNAm episignatures. Results: A robust and reproducible DNAm episignature and global DNAm profile were identified. Correlation analysis identified partial overlap and similarity of the global HNRNPU DNAm profile to several other rare disorders. Conclusion: This study demonstrates new evidence of a specific and sensitive DNAm episignature associated with pathogenic heterozygous HNRNPU-variants, establishing its utility as a clinical biomarker for the expansion of the EpiSignTM diagnostic test.

Purpose: Witteveen-Kolk syndrome (WITKOS) is a rare, autosomal dominant neurodevelopmental disorder caused by heterozygous loss-of-function alterations in the SIN3A gene. WITKOS has variable expressivity that commonly overlaps with other neurodevelopmental disorders. In this study, we characterized a distinct DNA methylation epigenetic signature (episignature) distinguishing WITKOS from unaffected individuals as well as individuals with other neurodevelopmental disorders with episignatures and described 9 previously unpublished individuals with SIN3A haploinsufficiency.Methods: We studied the phenotypic characteristics and the genome-wide DNA methylation in the peripheral blood samples of 20 individuals with heterozygous alterations in SIN3A. A total of 14 samples were used for the identification of the episignature and building of a predictive diagnostic biomarker, whereas the diagnostic model was used to investigate the methylation pattern of the remaining 6 samples.Results: A predominantly hypomethylated DNA methylation profile specific to WITKOS was identified, and the classifier model was able to diagnose a previously unresolved test case. The episignature was sensitive enough to detect individuals with varying degrees of phenotypic severity carrying SIN3A haploinsufficient variants.Conclusion: We identified a novel, robust episignature in WITKOS due to SIN3A haploinsufficiency. This episignature has the potential to aid identification and diagnosis of individuals with WITKOS.

Contains fulltext : 290808.pdf (Publisher’s version ) (Open Access) PURPOSE: Pathogenic variants in genes involved in the epigenetic machinery are an emerging cause of neurodevelopment disorders (NDDs). Lysine-demethylase 2B (KDM2B) encodes an epigenetic regulator and mouse models suggest an important role during development. We set out to determine whether KDM2B variants are associated with NDD. METHODS: Through international collaborations, we collected data on individuals with heterozygous KDM2B variants. We applied methylation arrays on peripheral blood DNA samples to determine a KDM2B associated epigenetic signature. RESULTS: We recruited a total of 27 individuals with heterozygous variants in KDM2B. We present evidence, including a shared epigenetic signature, to support a pathogenic classification of 15 KDM2B variants and identify the CxxC domain as a mutational hotspot. Both loss-of-function and CxxC-domain missense variants present with a specific subepisignature. Moreover, the KDM2B episignature was identified in the context of a dual molecular diagnosis in multiple individuals. Our efforts resulted in a cohort of 21 individuals with heterozygous (likely) pathogenic variants. Individuals in this cohort present with developmental delay and/or intellectual disability; autism; attention deficit disorder/attention deficit hyperactivity disorder; congenital organ anomalies mainly of the heart, eyes, and urogenital system; and subtle facial dysmorphism. CONCLUSION: Pathogenic heterozygous variants in KDM2B are associated with NDD and a specific epigenetic signature detectable in peripheral blood. 01 januari 2023

SRSF1 (also known as ASF/SF2) is a non-small nuclear ribonucleoprotein (non-snRNP) that belongs to the arginine/serine (R/S) domain family. It recognizes and binds to mRNA, regulating both constitutive and alternative splicing. The complete loss of this proto-oncogene in mice is embryonically lethal. Through international data sharing, we identified 17 individuals (10 females and 7 males) with a neurodevelopmental disorder (NDD) with heterozygous germline SRSF1 variants, mostly de novo, including three frameshift variants, three nonsense variants, seven missense variants, and two microdeletions within region 17q22 encompassing SRSF1. Only in one family, the de novo origin could not be established. All individuals featured a recurrent phenotype including developmental delay and intellectual disability (DD/ID), hypotonia, neurobehavioral problems, with variable skeletal (66.7%) and cardiac (46%) anomalies. To investigate the functional consequences of SRSF1 variants, we performed in silico structural modeling, developed an in vivo splicing assay in Drosophila, and carried out episignature analysis in blood-derived DNA from affected individuals. We found that all loss-of-function and 5 out of 7 missense variants were pathogenic, leading to a loss of SRSF1 splicing activity in Drosophila, correlating with a detectable and specific DNA methylation episignature. In addition, our orthogonal in silico, in vivo, and epigenetics analyses enabled the separation of clearly pathogenic missense variants from those with uncertain significance. Overall, these results indicated that haploinsufficiency of SRSF1 is responsible for a syndromic NDD with ID due to a partial loss of SRSF1-mediated splicing activity.

An expanding range of genetic syndromes are characterized by genome-wide disruptions in DNA methylation profiles referred to as episignatures. Episignatures are distinct, highly sensitive and specific biomarkers that have recently been applied in clinical diagnosis of genetic syndromes. Episignatures are contained within the broader disorder-specific genome-wide DNA methylation changes which can share significant overlap amongst different conditions. In this study we performed functional genomic assessment and comparison of disorder-specific and overlapping genome-wide DNA methylation changes related to 65 genetic syndromes with previously described episignatures. We demonstrate evidence of disorder-specific and recurring genome-wide differentially methylated probes (DMPs) and regions (DMRs). The overall distribution of DMPs and DMRs across the majority of the neurodevelopmental genetic syndromes analyzed showed substantial enrichment in gene promoters and CpG islands, and under-representation of the more variable intergenic regions. Analysis showed significant enrichment of the DMPs and DMRs in gene pathways and processes related to neurodevelopment, including neurogenesis, synaptic signaling and synaptic transmission. This study expands beyond the diagnostic utility of DNA methylation episignatures by demonstrating correlation between the function of the mutated genes and the consequent genomic DNA methylation profiles as a key functional element in the molecular etiology of genetic neurodevelopmental disorders. This article is protected by copyright. All rights reserved.

Clark-Baraitser syndrome is a rare autosomal dominant intellectual disability syndrome caused by pathogenic variants in the

Background Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance. Methods We address the issue of testing marker combinations that affect survival by adapting the recently developed Limitless Arity Multiple-testing Procedure (LAMP), a p-value correction technique for statistical tests for combination of markers. LAMP cannot handle survival data statistics, and hence we extended LAMP for the log-rank test, making it more appropriate for clinical data, with newly introduced theoretical lower bound of the p-value. Results We applied the proposed method to gene combination detection for cancer and obtained gene interactions with statistically significant log-rank p-values. Gene combinations with orders of up to 32 genes were detected by our algorithm, and effects of some genes in these combinations are also supported by existing literature. Conclusion The novel approach for detecting prognostic markers presented here can identify statistically significant markers with no limitations on the order of interaction. Furthermore, it can be applied to different types of genomic data, provided that binarization is possible.

We explore the feasibility of early introduction to automata theory through gamification. We designed a puzzle game that players can answer correctly if they understand the fundamental concepts of automata theory. In our investigation, 90 children played the game, and their actions were recorded in play logs. An analysis of the play logs shows that approximately 60% of the children achieved correct-answer rates of at least 70%, which suggests that primary and lower secondary school students can understand the fundamental concepts of automata theory. Meanwhile, our analysis shows that most of them do not fully understand automata theory, but some of them have a good understanding of the concept.

Although many 3D structures have been solved for proteins to date, functions of some proteins remain unknown. To predict protein functions, comparison of local structures of proteins with predefined model structures, whose functions have been elucidated, is widely performed. For the comparison, the root mean square deviation (RMSD) has been used as a conventional index. In this work, adaptive deviation was incorporated, along with Bregmann Divergence Regularized Machine, in order to detect analogous local structures with such model structures more effectively than the conventional index. key words: machine learning, Bregmann divergence, molecular structures, local structure comparison.

Nowadays, the design of the representation of images is one of the most crucial factors in the performance of visual categorization. A common pipeline employed in most of recent researches for obtaining an image representa- tion consists of two steps: the encoding step and the pooling step. In this paper, we introduce the Mahalanobis metric to the two popular image patch encoding modules, Histogram Encoding and Fisher Encoding, that are used for Bag- of-Visual-Word method and Fisher Vector method, respectively. Moreover, for the proposed Fisher Vector method, a close-form approximation of Fisher Vector can be derived with the same assumption used in the original Fisher Vector, and the codebook is built without resorting to time-consuming EM (Expectation-Maximization) steps. Experimental evaluation of multi-class classification demonstrates the effectiveness of the proposed encoding methods.

We study the problem of learning sparse structure changes between two Markov networks $P$ and $Q$. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes \emph{directly} via estimating the ratio between two Markov network models. In this paper, we give sufficient conditions for \emph{successful change detection} with respect to the sample size $n_p, n_q$, the dimension of data $m$, and the number of changed edges $d$. When using an unbounded density ratio model we prove that the true sparse changes can be consistently identified for $n_p = \Omega(d^2 \log \frac{m^2+m}{2})$ and $n_q = \Omega({n_p^2})$, with an exponentially decaying upper-bound on learning error. Such sample complexity can be improved to $\min(n_p, n_q) = \Omega(d^2 \log \frac{m^2+m}{2})$ when the boundedness of the density ratio model is assumed. Our theoretical guarantee can be applied to a wide range of discrete/continuous Markov networks., Comment: Rerun experiments, added a new image change detection experiment. Changed some typos in the proof of Proposition 6 and 11

Diabetic nephropathy is a major complication in diabetes and a leading cause of end-stage renal failure. Glomerular podocytes are functionally and structurally injured early in diabetic nephropathy. A non-obese type 2 diabetes model, the spontaneously diabetic Torii (SDT) rat, is of increasing preclinical interest because of its pathophysiological similarities to human type 2 diabetic complications including diabetic nephropathy. However, podocyte injury in SDT rat glomeruli and the effect of angiotensin II receptor blocker treatment in the early stage have not been reported in detail. Therefore, we have evaluated early stages of glomerular podocyte damage and the beneficial effect of early treatment with losartan in SDT rats using desmin as a sensitive podocyte injury marker. Moreover, we have developed an automated, computational glomerulus recognition method and illustrated its specific application for quantitatively studying glomerular desmin immunoreactivity. This state-of-the-art method enabled automatic recognition and quantification of glomerular desmin-positive areas, eliminating the need to laboriously trace glomerulus borders by hand. The image analysis method not only enabled assessment of a large number of glomeruli, but also clearly demonstrated that glomerular injury was more severe in the juxtamedullary region than in the superficial cortex region. This applied not only in SDT rat diabetic nephropathy but also in puromycin aminonucleoside-induced nephropathy, which was also studied. The proposed glomerulus image analysis method combined with desmin immunohistochemistry should facilitate evaluations in preclinical drug efficacy studies as well as elucidation of the pathophysiology of diabetic nephropathy.

SUMMARY Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering

Classification tasks in computer vision and brain-computer interface research have presented several applications such as biometrics and cognitive training. However, determining suitable data representation has been challenging, and recent approaches have deviated from the familiar form of one vector for each data sample. This paper considers a kernel between vector sets, the mean polynomial kernel, motivated by recent studies where data are approximated by linear subspaces, in particular, methods on Grassmann manifolds. The kernel supports vector sequences as input. We discuss how the kernel can be associated with the Grassmann Projection kernel, and provide experimental results showing how it outperforms existing subspace-based methods on Grassmann manifolds.

Protein-ligand interaction prediction plays an important role in drug design and discovery. However, wet lab procedures are inherently time consuming and expensive due to the vast number of candidate compounds and target genes. Hence, computational approaches became imperative and have become popular due to their promising results and practicality. Such methods require high accuracy and precision outputs for them to be useful, thus, the problem of devising such an algorithm remains very challenging. In this paper we propose an algorithm employing both support vector machines (SVM) and an extension of canonical correlation analysis (CCA). Following assumptions of recent chemogenomic approaches, we explore the effects of incorporating bias on similarity of compounds. We introduce kernel weighted CCA as a means of uncovering any underlying relationship between similarity of ligands and known ligands of target proteins. Experimental results indicate statistically significant improvement in the area under the ROC curve (AUC) and F-measure values obtained as opposed to those gathered when only SVM, or SVM with kernel CCA is employed, which translates to better quality of prediction.