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Activating signal co-integrator complex 1 (ASCC1) acts with ASCC-ALKBH3 complex in alkylation damage responses. ASCC1 uniquely combines two evolutionarily ancient domains: nucleotide-binding K-Homology (KH) (associated with regulating splicing, transcriptional, and translation) and two-histidine phosphodiesterase (PDE; associated with hydrolysis of cyclic nucleotide phosphate bonds). Germline mutations link loss of ASCC1 function to spinal muscular atrophy with congenital bone fractures 2 (SMABF2). Herein analysis of The Cancer Genome Atlas (TCGA) suggests ASCC1 RNA overexpression in certain tumors correlates with poor survival, Signatures 29 and 3 mutations, and genetic instability markers. We determined crystal structures of Alvinella pompejana (Ap) ASCC1 and Human (Hs) PDE domain revealing high-resolution details and features conserved over 500 million years of evolution. Extending our understanding of the KH domain Gly-X-X-Gly sequence motif, we define a novel structural Helix-Clasp-Helix (HCH) nucleotide binding motif and show ASCC1 sequence-specific binding to CGCG-containing RNA. The V-shaped PDE nucleotide binding channel has two His-Φ-Ser/Thr-Φ (HXT) motifs (Φ being hydrophobic) positioned to initiate cyclic phosphate bond hydrolysis. A conserved atypical active-site histidine torsion angle implies a novel PDE substrate. Flexible active site loop and arginine-rich domain linker appear regulatory. Small-angle X-ray scattering (SAXS) revealed aligned KH-PDE RNA binding sites with limited flexibility in solution. Quantitative evolutionary bioinformatic analyses of disease and cancer-associated mutations support implied functional roles for RNA binding, phosphodiesterase activity, and regulation. Collective results inform ASCC1's roles in transactivation and alkylation damage responses, its targeting by structure-based inhibitors, and how ASCC1 mutations may impact inherited disease and cancer.

Arrival of the novel SARS-CoV-2 has launched a worldwide effort to identify both pre-approved and novel therapeutics targeting the viral proteome, highlighting the urgent need for efficient drug discovery strategies. Even with effective vaccines, infection is possible, and at-risk populations would benefit from effective drug compounds that reduce the lethality and lasting damage of COVID-19 infection. The CoV-2 MacroD-like macrodomain (Mac1) is implicated in viral pathogenicity by disrupting host innate immunity through its mono (ADP-ribosyl) hydrolase activity, making it a prime target for antiviral therapy. We therefore solved the structure of CoV-2 Mac1 from non-structural protein 3 (Nsp3) and applied structural and sequence-based genetic tracing, including newly determined A. pompejana MacroD2 and GDAP2 amino acid sequences, to compare and contrast CoV-2 Mac1 with the functionally related human DNA-damage signaling factor poly (ADP-ribose) glycohydrolase (PARG). Previously, identified targetable features of the PARG active site allowed us to develop a pharmacologically useful PARG inhibitor (PARGi). Here, we developed a focused chemical library and determined 6 novel PARGi X-ray crystal structures for comparative analysis. We applied this knowledge to discovery of CoV-2 Mac1 inhibitors by combining computation and structural analysis to identify PARGi fragments with potential to bind the distal-ribose and adenosyl pockets of the CoV-2 Mac1 active site. Scaffold development of these PARGi fragments has yielded two novel compounds, PARG-345 and PARG-329, that crystallize within the Mac1 active site, providing critical structure-activity data and a pathway for inhibitor optimization. The reported structural findings demonstrate ways to harness our PARGi synthesis and characterization pipeline to develop CoV-2 Mac1 inhibitors targeting the ADP-ribose active site. Together, these structural and computational analyses reveal a path for accelerating development of antiviral therapeutics from pre-existing drug optimization pipelines.

PurposeWe characterize the clinical and molecular phenotypes of six unrelated individuals with intellectual disability and autism spectrum disorder who carry heterozygous missense variants of the PRKAR1B gene, which encodes the R1β subunit of the cyclic AMP-dependent protein kinase A (PKA).MethodsVariants of PRKAR1B were identified by single- or trio-exome analysis. We contacted the families and physicians of the six individuals to collect phenotypic information, performed in vitro analyses of the identified PRKAR1B-variants, and investigated PRKAR1B expression during embryonic development.ResultsRecent studies of large patient cohorts with neurodevelopmental disorders found significant enrichment of de novo missense variants in PRKAR1B. In our cohort, de novo origin of the PRKAR1B variants could be confirmed in five of six individuals, and four carried the same heterozygous de novo variant c.1003C>T (p.Arg335Trp; NM_001164760). Global developmental delay, autism spectrum disorder, and apraxia/dyspraxia have been reported in all six, and reduced pain sensitivity was found in three individuals carrying the c.1003C>T variant. PRKAR1B expression in the brain was demonstrated during human embryonal development. Additionally, in vitro analyses revealed altered basal PKA activity in cells transfected with variant-harboring PRKAR1B expression constructs.ConclusionOur study provides strong evidence for a PRKAR1B-related neurodevelopmental disorder.

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) is a key member of the phosphatidylinositol-3 kinase-like (PIKK) family of protein kinases with critical roles in DNA-double strand break repair, transcription, metastasis, mitosis, RNA processing, and innate and adaptive immunity. The absence of DNA-PKcs from many model organisms has led to the assumption that DNA-PKcs is a vertebrate-specific PIKK. Here, we find that DNA-PKcs is widely distributed in invertebrates, fungi, plants, and protists, and that threonines 2609, 2638, and 2647 of the ABCDE cluster of phosphorylation sites are highly conserved amongst most Eukaryotes. Furthermore, we identify highly conserved amino acid sequence motifs and domains that are characteristic of DNA-PKcs relative to other PIKKs. These include residues in the Forehead domain and a novel motif we have termed YRPD, located in an α helix C-terminal to the ABCDE phosphorylation site loop. Combining sequence with biochemistry plus structural data on human DNA-PKcs unveils conserved sequence and conformational features with functional insights and implications. The defined generally progressive DNA-PKcs sequence diversification uncovers conserved functionality supported by Evolutionary Trace analysis, suggesting that for many organisms both functional sites and evolutionary pressures remain identical due to fundamental cell biology. The mining of cancer genomic data and germline mutations causing human inherited disease reveal that robust DNA-PKcs activity in tumors is detrimental to patient survival, whereas germline mutations compromising function are linked to severe immunodeficiency and neuronal degeneration. We anticipate that these collective results will enable ongoing DNA-PKcs functional analyses with biological and medical implications.

Not only in the field of education but also in many other forms of social expression and action, a period of fundamental changes can be observed due to the incorporation of new data imposed by the technological revolution. However, the process of integrating Information and Communication Technologies (ICT) in the learning process requires coordination, system change and radical transformations. These changes should be incorporated into curricula, while active teachers should have ongoing training on new technologies. In such a context, this paper comes to explore the views of secondary education teachers on the use of information and communication technologies in the learning process. In particular, a sample survey was conducted to determine the degree of utilization of new information and communication technologies in the learning process, especially in secondary school teachers in the Prefecture of Aitoloakarnania. The statistical software SPSS 23 was used to analyze the data obtained from the questionnaires. Tools and methods of descriptive and inductive statistics were used. The results highlight the low percentage of teachers who have received Level B certification and demonstrate the low level of ICT use by teachers, identifying at the same time all the factors affecting negatively their use and application.

All tumors have DNA mutations, and a predictive understanding of those mutations could inform clinical treatments. However, 40% of the mutations are variants of unknown significance (VUS), with the challenge being to objectively predict whether a VUS is pathogenic and supports the tumor or whether it is benign. To objectively decode VUS, we mapped cancer sequence data and evolutionary trace (ET) scores onto crystallography and cryo-electron microscopy structures with variant impacts quantitated by evolutionary action (EA) measures. As tumors depend on helicases and nucleases to deal with transcription/replication stress, we targeted helicase-nuclease-RPA complexes: (1) XPB-XPD (within TFIIH), XPF-ERCC1, XPG, and RPA for transcription and nucleotide excision repair pathways and (2) BLM, EXO5, and RPA plus DNA2 for stalled replication fork restart. As validation, EA scoring predicts severe effects for most disease mutations, but disease mutants with low ET scores not only are likely destabilizing but also disrupt sophisticated allosteric mechanisms. For sites of disease mutations and VUS predicted to be severe, we found strong co-localization to ordered regions. Rare discrepancies highlighted the different survival requirements between disease and tumor mutations, as well as the value of examining proteins within complexes. In a genome-wide analysis of 33 cancer types, we found correlation between the number of mutations in each tumor and which pathways or functional processes in which the mutations occur, revealing different mutagenic routes to tumorigenesis. We also found upregulation of ancient genes including BLM, which supports a non-random and concerted cancer process: reversion to a unicellular, proliferation-uncontrolled, status by breaking multicellular constraints on cell division. Together, these genes and global analyses challenge the binary "driver" and "passenger" mutation paradigm, support a gradient impact as revealed by EA scoring from moderate to severe at a single gene level, and indicate reduced regulation as well as activity. The objective quantitative assessment of VUS scoring and gene overexpression in the context of functional interactions and pathways provides insights for biology, oncology, and precision medicine.

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

Background: Alzheimer's Disease (AD) incidence is almost double in female than male, suggesting sex‐specific AD risk genes remain unknown. Method: We designed a statistical physics approach that exploits freely available but massive evolutionary and phylogenetic coupling data on sequence variation and speciation. These couplings lead to quantifiable values for the selection pressure exerted on the genes within a population. We may then compare a gene's influence in sequenced cases vs controls cohorts and test the hypothesis that significant deviations identify genes linked to disease risk. Result: In 4768 AD cases and 4689 healthy controls (HC), we discovered 122 genes under greater selection pressure (q < 0.01). These genes overlapped (p = 3.10‐5) and interacted (z = 7.16) with AD GWAS genes. They also interacted mutually (n = 57, p = 0.0019) and with AD‐related processes (p = 1.0‐16). More than 50% of the genes exhibited dysregulation in AD brains in snRNAseq analysis, suggesting participation in pathogenic or protective processes in AD. Furthermore, expression of these genes correlated with increased or decreased deposition of plaques and tangles in patient brains. Moreover, the candidates are enriched in modifiers of neurodegeneration in Drosophila: knockdown or overexpression of 64 genes ameliorated or worsened age‐dependent neuronal dysfunction (p<0.05). Robustness to down‐sampling allowed to analyze smaller, sex‐separated cohorts. We identified 82 genes in males and 69 genes in females (15 genes overlapped, p < 10‐53), indicating shared as well as sex‐specific AD mechanisms. The male and the female genes overlapped (p = 6.5‐6 and 1.5‐4) and interacted (z = 5.63 and 6.18) with the AD genes. Remarkably, using these gene sets as features for predicting AD risk in separate training and testing cohorts successfully differentiated between AD cases and controls with very high accuracy, even when blinded to APOE genotype. Notably, we predicted the risk with AUCs of 0.83 with APOE, 0.83, and 0.82 in combined, males, and females, respectively. Conclusion: A new statistical physics approach discovered male and female AD genes, predicting AD risk with very high accuracy. These results identify further genetic differences leading to AD in males and females, and show the power of quantitative phylogenetics to probe complex human diseases. [ABSTRACT FROM AUTHOR]

Cyprus would be paradise on earth if it weren't for its history and geography. At the crossroads of Europe, Asia, and Africa, it has a history of hate and war. Today it's a nation divided by force, with thousands of Greek Cypriots missing or barred from their homes. Turkey's continuing occupation of Cyprus is a cause of war between Greece and Turkey according to NATO. (EMS)

Objective: To assess the impact of somatic gene mutations on survival among patients undergoing resection of colorectal liver metastases (CLM). Background: Patients undergoing CLM resection have heterogeneous outcomes, and accurate risk stratification is necessary to optimize patient selection for surgery. Methods: Next-generation sequencing of 50 cancer-related genes was performed from primary tumors and/or liver metastases in 401 patients undergoing CLM resection. Missense TP53 mutations were classified by the evolutionary action score (EAp53)—a novel approach that dichotomizes mutations as low or high risk. Results: The most frequent somatic gene mutations were TP53 (65.6%), followed by KRAS (48.1%) and APC (47.4%). Double mutation in RAS / TP53 , identified in 31.4% of patients, was correlated with primary tumor location in the right colon (P = 0.006). On multivariable analysis, RAS/TP53 double mutation was an independent predictor of shorter overall survival (hazard ratio 2.62, 95% confidence interval 1.41–4.87, P = 0.002). In patients with co-mutated RAS , EAp53 high-risk mutations were associated with shorter 5-year overall survival of 12.2%, compared with 55.7% for TP53 wild type (P < 0.001). The negative prognostic effects of RAS and TP53 mutations were limited to tumors harboring mutations in both genes. Conclusions: Concomitant RAS and TP53 mutations are associated with decreased survival after CLM resection. A high EAp53 predicts a subset of patients with worse prognosis. These preliminary analyses suggest that surgical resection of liver metastases should be carefully considered in this subset of patients. [ABSTRACT FROM AUTHOR]

Background: To study how genetic backgrounds modulate neurodegenerative risk requires genotype‐phenotype association methods effective in small patient cohorts. Method: We developed a genome sequence analysis framework rooted in Statistical Physics, which yields a reliable measure of gene functional impact in any chosen population. When comparing two populations, such as cases and controls, shifts in gene importance identify those likely to drive phenotype differences. Results: In repeated case‐control genome sequence studies of Alzheimer's Disease (AD) focused on a single sex, APOE allele, or ethnic ancestry, we identified gene sets that met criteria for success, including prior GWAS studies, post‐mortem of AD brain tissues expression, live Drosophila experiments, and risk modeling. Conclusion: Statistical Physics of fitness landscape is a new tool to characterize genes and mutations that enhance or protect from AD, powerful enough to identify similarities, complementarities, and differences in risk gene in target subgroups of about 1000 subjects. [ABSTRACT FROM AUTHOR]