Prodromal Cognitive Changes as a Prognostic Indicator of Forthcoming Huntington's Disease Severity: A Retrospective Longitudinal Study.

BACKGROUND: Cognitive changes in Huntington's disease (HD) precede motor manifestations. ENROLL-HD platform includes four cognitive measures of information processing speed (IPS). Our group is eager to seek clinical markers in the life stage that is as close as possible to the age of onset (ie, the so called prodromal HD phase) because this is the best time for therapeutic interventions. OBJECTIVES: Our study aimed to test whether cognitive scores in prodromal ENROLL-HD mutation carriers show the potential to predict the severity of motor and behavioral changes once HD became fully manifested. METHODS: From the global ENROLL-HD cohort of 21,343 participants, we first selected a premanifest Cohort#1 (ie, subjects with Total Motor Score (TMS) <10 and Diagnostic Confidence Level (DCL) <4, N = 1.222). From this cohort, we then focused on a prodromal Cohort#2 of subjects who were ascertained to phenoconvert into manifest HD at follow-up visits (ie, subjects from 6 ≤ TMS≤9 and DCL <4 to TMS≥10 and DCL = 4, n = 206). RESULTS: The main results of our study showed that low IPS before phenoconversion in Cohort#2 predicted the severity of motor and behavioral manifestations. By combining the four IPS cognitive measures (eg, the Categorical Verbal Fluency Test; Stroop Color Naming Test; Stroop Word Reading; Symbol Digit Modalities Test), we generated a Composite Cognition Score (CCS). The lower the CCS score the higher the TMS and the apathy scores in the same longitudinally followed-up patients after phenoconversion. CONCLUSIONS: CCS might represent a clinical instrument to predict the prognosis of mutation carriers who are close to manifesting HD.

The Notch1 signaling pathway directly modulates the human RANKL-induced osteoclastogenesis.

Notch signaling is an evolutionary conserved pathway with a key role in tissue homeostasis, differentiation and proliferation. It was reported that Notch1 receptor negatively regulates mouse osteoclast development and formation by inhibiting the expression of macrophage colony-stimulating factor in mesenchymal cells. Nonetheless, the involvement of Notch1 pathway in the generation of human osteoclasts is still controversial. Here, we report that the constitutive activation of Notch1 signaling induced a differentiation block in human mononuclear CD14+ cells directly isolated from peripheral blood mononuclear cells (PBMCs) upon in vitro stimulation to osteoclasts. Additionally, using a combined approach of single-cell RNA sequencing (scRNA-Seq) simultaneously with a panel of 31 oligo-conjugated antibodies against cell surface markers (AbSeq assay) as well as unsupervised learning methods, we detected four different cell stages of human RANKL-induced osteoclastogenesis after 5 days in which Notch1 signaling enforces the cell expansion of specific subsets. These cell populations were characterized by distinct gene expression and immunophenotypic profiles and active Notch1, JAK/STAT and WNT signaling pathways. Furthermore, cell-cell communication analyses revealed extrinsic modulators of osteoclast progenitors including the IL7/IL7R and WNT5a/RYK axes. Interestingly, we also report that Interleukin-7 receptor (IL7R) was a downstream effector of Notch1 pathway and that Notch1 and IL7R interplay promoted cell expansion of human RANKL-induced osteoclast progenitors. Taken together, these findings underline a novel cell pattern of human osteoclastogenesis, outlining the key role of Notch1 and IL-7R signaling pathways.

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants.

Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2's ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks.

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

Profiling of cell-free DNA methylation and histone signatures in pediatric NAFLD: A pilot study.

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in children and adolescents, increasing the risk of its progression toward nonalcoholic steatohepatitis (NASH), cirrhosis, and cancer. There is an urgent need for noninvasive early diagnostic and prognostic tools such as epigenetic marks (epimarks), which would replace liver biopsy in the future. We used plasma samples from 67 children with biopsy-proven NAFLD, and as controls we used samples from 20 children negative for steatosis by ultrasound. All patients were genotyped for patatin-like phospholipase domain containing 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), membrane bound O-acyltransferase domain containing 7 (MBOAT7), and klotho-ß (KLB) gene variants, and data on anthropometric and biochemical parameters were collected. Furthermore, plasma cell-free DNA (cfDNA) methylation was quantified using a commercially available kit, and ImageStream(X) was used for the detection of free circulating histone complexes and variants. We found a significant enrichment of the levels of histone macroH2A1.2 in the plasma of children with NAFLD compared to controls, and a strong correlation between cfDNA methylation levels and NASH. Receiver operating characteristic curve analysis demonstrated that combination of cfDNA methylation, PNPLA3 rs738409 variant, coupled with either high-density lipoprotein cholesterol or alanine aminotransferase levels can strongly predict the progression of pediatric NAFLD to NASH with area under the curve >0.87. Conclusion: Our pilot study combined epimarks and genetic and metabolic markers for a robust risk assessment of NAFLD development and progression in children, offering a promising noninvasive tool for the consistent diagnosis and prognosis of pediatric NAFLD. Further studies are necessary to identify their pathogenic origin and function.

KDM6A missense variants hamper H3 histone demethylation in lung squamous cell carcinoma.

KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease; it is also a known cancer driver gene, with multiple somatic mutations found in a few cancer types. In this study, we looked at eleven missense variants in lung squamous cell carcinoma, one of the most common lung cancer subtypes, to see how they affect the KDM6A catalytic mechanisms. We found that they influence the interaction with histone H3 and the exposure of the trimethylated Lys27, which is critical for wild-type physiological function to varying degrees, by altering the conformational transition.

MiRLog and dbmiR: Prioritization and functional annotation tools to study human microRNA sequence variants.

The recent identification of noncoding variants with pathogenic effects suggests that these variations could underlie a significant number of undiagnosed cases. Several computational methods have been developed to predict the functional impact of noncoding variants, but they exhibit only partial concordance and are not integrated with functional annotation resources, making the interpretation of these variants still challenging. MicroRNAs (miRNAs) are small noncoding RNA molecules that act as fine regulators of gene expression and play crucial functions in several biological processes, such as cell proliferation and differentiation. An increasing number of studies demonstrate a significant impact of miRNA single nucleotide variants (SNVs) both in Mendelian diseases and complex traits. To predict the functional effect of miRNA SNVs, we implemented a new meta-predictor, MiRLog, and we integrated it into a comprehensive database, dbmiR, which includes a precompiled list of all possible miRNA allelic SNVs, providing their biological annotations at nucleotide and miRNA levels. MiRLog and dbmiR were used to explore the genetic variability of miRNAs in 15,708 human genomes included in the gnomAD project, finding several ultra-rare SNVs with a potentially deleterious effect on miRNA biogenesis and function representing putative contributors to human phenotypes.

A comparative benchmark of classic DNA motif discovery tools on synthetic data.

Hundreds of human proteins were found to establish transient interactions with rather degenerated consensus DNA sequences or motifs. Identifying these motifs and the genomic sites where interactions occur represent one of the most challenging research goals in modern molecular biology and bioinformatics. The last twenty years witnessed an explosion of computational tools designed to perform this task, whose performance has been last compared fifteen years ago. Here, we survey sixteen of them, benchmark their ability to identify known motifs nested in twenty-nine simulated sequence datasets, and finally report their strengths, weaknesses, and complementarity.