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A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a \"drug network\" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be \"repositioned\" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).

Background Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard. Results Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species—which were not reliably predicted—helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample’s Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall. Conclusions kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation.

Myoclonus classically presents as a brief (10–50 ms duration), non‐rhythmic jerk movement. The etiology could vary considerably ranging from self‐limited to chronic or even progressive disorders, the latter falling into encephalopathic pictures that need a prompt diagnosis. Beyond the etiological classification, others evaluate myoclonus' body distribution (i.e., clinical classification) or the location of the generator (i.e., neurophysiological classification); particularly, knowing the anatomical source of myoclonus gives inputs on the observable clinical patterns, such as EMG bursts duration or EEG correlate, and guides the therapeutic choices. Among all the chronic disorders, myoclonus often presents itself as a manifestation of epilepsy. In this context, myoclonus has many facets. Myoclonus occurs as one, or the only, seizure manifestation while it can also present as a peculiar type of movement disorder; moreover, its electroclinical features within specific genetically determined epileptic syndromes have seldom been investigated. In this review, following a meeting of recognized experts, we provide an up‐to‐date overview of the neurophysiology and nosology surrounding myoclonus. Through the dedicated exploration of epileptic syndromes, coupled with pragmatic guidance, we aim to furnish clinicians and researchers alike with practical advice for heightened diagnostic management and refined treatment strategies. Plain Language Summary In this work, we described myoclonus, a movement characterized by brief, shock‐like jerks. Myoclonus could be present in different diseases and its correct diagnosis helps treatment.

To evaluate in the setting of a stroke unit ward the usefulness of a prolonged (>6 h) video-EEG recording (PVEEG) in identifying non-convulsive status epilepticus (NCSE) in patients with an acute ischemic stroke. Predictors of NCSE were also evaluated. Patients with an acute ischemic stroke, referred to our unit, were included in this prospective observational study. A PVEEG recording was implemented after stroke in all patients during the first week: (a) promptly in those exhibiting a clear or suspected epileptic manifestation; (b) at any time during the routine activity in the remaining patients. After the first week, a standard EEG/PVEEG recording was hooked up only in presence of an evident or suspected epileptic manifestation or as control of a previous epileptic episode. NCSE was identified in 32 of the 889 patients (3.6 %) included in the study. It occurred early (within the first week) in 20/32 (62.5 %) patients and late in the remaining 12. Diagnosis was made on the basis of a specific clinical suspect ( n  = 19, 59.4 %) or without any suspect ( n  = 13, 40.6 %). In a multivariate analysis, a significant association of NCSE was observed with NIHSS score, infarct size and large atherothrombotic etiology. NCSE is not a rare event after an acute ischemic stroke and a delayed diagnosis could worsen patient prognosis. Since NCSE can be difficult to be diagnosed only on clinical grounds, implementation of a prompt PVEEG should be kept available in a stroke unit whenever a patient develop signs, although subtle, consistent with NCSE.

Protein synthesis is traditionally associated with specific cytoplasmic compartments. We now show that OFD1, a centrosomal/basal body protein, interacts with components of the Preinitiation complex of translation (PIC) and of the eukaryotic Initiation Factor (eIF)4F complex and modulates the translation of specific mRNA targets in the kidney. We demonstrate that OFD1 cooperates with the mRNA binding protein Bicc1 to functionally control the protein synthesis machinery at the centrosome where also the PIC and eIF4F components were shown to localize in mammalian cells. Interestingly, Ofd1 and Bicc1 are both involved in renal cystogenesis and selected targets were shown to accumulate in two models of inherited renal cystic disease. Our results suggest a possible role for the centrosome as a specialized station to modulate translation for specific functions of the nearby ciliary structures and may provide functional clues for the understanding of renal cystic disease.

The connection between chromatin nuclear organization and gene activity is vividly illustrated by the observation that transcriptional coregulation of certain genes appears to be directly influenced by their spatial proximity. This fact poses the more general question of whether it is at all feasible that the numerous genes that are coregulated on a given chromosome, especially those at large genomic distances, might become proximate inside the nucleus. This problem is studied here using steered molecular dynamics simulations in order to enforce the colocalization of thousands of knowledge-based gene sequences on a model for the gene-rich human chromosome 19. Remarkably, it is found that most (≈ 88%) gene pairs can be brought simultaneously into contact. This is made possible by the low degree of intra-chromosome entanglement and the large number of cliques in the gene coregulatory network. A clique is a set of genes coregulated all together as a group. The constrained conformations for the model chromosome 19 are further shown to be organized in spatial macrodomains that are similar to those inferred from recent HiC measurements. The findings indicate that gene coregulation and colocalization are largely compatible and that this relationship can be exploited to draft the overall spatial organization of the chromosome in vivo. The more general validity and implications of these findings could be investigated by applying to other eukaryotic chromosomes the general and transferable computational strategy introduced here.

Epilepsy affects approximately 3 % of the world’s population, and sudden death is a significant cause of death in this population. Sudden unexpected death in epilepsy (SUDEP) accounts for up to 17 % of all these cases, which increases the rate of sudden death by 24-fold as compared to the general population. The underlying mechanisms are still not elucidated, but recent studies suggest the possibility that a common genetic channelopathy might contribute to both epilepsy and cardiac disease to increase the incidence of death via a lethal cardiac arrhythmia. We performed genetic testing in a large cohort of individuals with epilepsy and cardiac conduction disorders in order to identify genetic mutations that could play a role in the mechanism of sudden death. Putative pathogenic disease-causing mutations in genes encoding cardiac ion channel were detected in 24 % of unrelated individuals with epilepsy. Segregation analysis through genetic screening of the available family members and functional studies are crucial tasks to understand and to prove the possible pathogenicity of the variant, but in our cohort, only two families were available. Despite further research should be performed to clarify the mechanism of coexistence of both clinical conditions, genetic analysis, applied also in post-mortem setting, could be very useful to identify genetic factors that predispose epileptic patients to sudden death, helping to prevent sudden death in patients with epilepsy.

Objectives The gold standard for distinguishing epileptic seizures (ES) from non‐epileptic events is video‐EEG monitoring. In some cases, video alone might suffice, leading to increased utilization of home videos, to support the diagnosis. This study aimed to assess the feasibility of such practice and its accuracy compared to video‐EEG, to identify key signs and symptoms of functional seizure (FS) and to establish if self‐reported questionnaires would improve diagnostic accuracy. Methods All consecutive patients ≥14 years presenting to six Italian epilepsy centers with either recurrent paroxysmal events of uncertain nature or with confirmed ES were enrolled. Subjects had to record home videos of the events and to respond to ad‐hoc questionnaires. De‐identified data were randomly assigned to pairs of evaluators blinded to the gold standard diagnosis, one epileptologist and one neurologist, to predict the correct diagnosis in two steps: Step 1 (home video alone) and Step 2 (patient's and witness' questionnaires). Results Ninety‐four videos (48 ES; 45 FS; 1 other), obtained from 36 patients, were independently evaluated by the 16 reviewers, providing a total of 188 assessments. Diagnostic accuracy for the whole group was 55.3% among epileptologists and 48.9% among neurologists (p = 0.6892) but was significantly higher in the FS subgroup (71.1%) compared to ES (41.7%) (p = 0.0043). Significance Diagnostic accuracy was moderate, without significant differences between epileptologists and neurologists, while it was higher for FS compared to ES. The addition of questionnaires did not improve accuracy. Eye closure for FS and abrupt ending for ES emerged as the only diagnostic signs. Thus, homemade videos have a role in differentiating these disorders. Plain Language Summary This study aimed to assess if home videos can distinguish epileptic seizures from functional seizures. Patients over 14 years from six Italian centers recorded videos of their episodes and answered questionnaires. Data were evaluated by epileptologists and neurologists. Results showed moderate diagnostic accuracy, with higher accuracy for functional seizures compared to epileptic seizures. Key diagnostic signs included eye closure for functional seizures and abrupt endings for epileptic seizures. Questionnaires did not improve accuracy. In conclusion, home videos can help differentiate these events.

Background Cyclic Vomiting Syndrome (CVS) is a rare functional gastrointestinal disorder, which has a considerable burden on quality of life of both children and their family. Aim of the study was to evaluate the diagnostic modalities and therapeutic approach to CVS among Italian tertiary care centers and the differences according to subspecialties, as well as to explore whether potential predictive factors associated with either a poor outcome or a response to a specific treatment. Methods Cross-sectional multicenter web-based survey involving members of the Italian Society of Pediatric Gastroenterology, Hepatology and Nutrition (SIGENP) and Italian Society of Pediatric Neurology (SINP). Results A total of 67 responses were received and analyzed. Most of the respondent units cared for less than 20 patients. More than half of the patients were referred after 3 to 5 episodes, and a quarter after 5 attacks. We report different diagnostic approaches among Italian clinicians, which was particularly evident when comparing gastroenterologists and neurologists. Moreover, our survey demonstrated a predilection of certain drugs during emetic phase according to specific clinic, which reflects the cultural background of physicians. Conclusion In conclusion, our survey highlights poor consensus amongst clinicians in our country in the diagnosis and the management of children with CVS, raising the need for a national consensus guideline in order to standardize the practice.

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.