Autismo in famiglia: quali i tratti in comune? Un contributo di ricerca nell'ambito del Broader Autism Phenotype

Background. L’espressione Broader Autism Phenotype (BAP), la cui versione italiana è “fenotipo autistico ampio”, descrive una condizione subclinica, indagata a partire dagli anni 70 sia in ambito genetico che psicologico, nella quale si collocano soggetti , e soprattutto parenti di primo grado di soggetti con autismo, che presentano tratti riconducibili ai tratti peculiari e ai domini dei Disturbi dello Spettro Autistico (ASD). La maggior parte delle ricerche ha mostrato, mediante Studi Familiari e l’uso di Scale Psicometriche, come questi tratti siano maggiormente presenti nei genitori di soggetti nello Spettro autistico rispetto alla popolazione generale. Pochi risultano dalla letteratura i contributi di ricerca nei quali sono stati analizzati i tratti maggiormente condivisi da genitori e figli ASD. Obiettivi. l’obiettivo principale della nostra ricerca sperimentale è stato valutare, in un campione di 131 genitori italiani di 68 bambini con Disturbo dello Spettro Autistico (ASD), le correlazioni tra i tratti dei genitori misurati con l’AQ e i tratti autistici e le variabili cliniche dei figli misurate rispettivamente mediante l’AQ-child e il protocollo diagnostico. Risultati. Sono stati evidenziati tratti comuni soprattutto tra padri e figli e in particolare nella sfera sociale e comunicativa. Si evince un rapporto di correlazione positivo tra numero di tratti autistici nei padri e tratti autistici nei figli. Alcuni tratti dei padri correlano con il comportamento adattivo dei figli misurato con la scala Vineland . Tra i genitori che hanno superato i criteri soglia per il BAP, sono emersi tratti in comune solo tra le madri e i figli. In particolare a una maggiore o minore tendenza all’elaborazione locale degli stimoli nelle madri corrisponde una maggiore o minore tendenza anche nei figli. L’età della madre correla con la gravità della sintomatologia autistica nei figli misurata con l’ADOS. Dallo studio non è emersa una relazione positiva e significativa tra maggiori tratti autistici nei genitori e gravità della sintomatologia autistica nei figli. Conclusioni. valutare i tratti comuni tra genitori e figli ASD permette di comprendere le modalità di trasmissione dei tratti BAP a livello intergenerazionale e permette di verificare i tratti dei genitori che possono costituire fattori di rischio o protezione all’ASD dei figli. Può inoltre risultare utile nella formulazione di programmi di Parent Coaching più adeguati alle caratteristiche del genitore

Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data

In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is inferring biochemical interactions from different molecular entities such as metabolites. In this area, the metabolome possesses a unique place for reflecting “true exposure” by being sensitive to variation coming from genetics, time, and environmental stimuli. While influenced by many different reactions, often the research interest needs to be focused on variation coming from a certain source, i.e. a certain covariable Xm . Objective Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to Xm . Metabolite values are based on information coming from individuals’ Xm status which might interact with other covariables. Methods Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to Xm is further used. For two datasets, two different network estimation methods are considered. The first is weighted gene co-expression network analysis based on correlation coefficients. The second method is graphical LASSO based on partial correlations. Results We observed that when using the parts related to the specific covariable of interest, resulting estimated networks display higher interconnectedness. Additionally, several groups of biologically associated metabolites (very large density lipoproteins, lipoproteins, etc.) were identified in the human data example. Conclusions This work demonstrates how information on the study design can be incorporated to estimate metabolite networks. As a result, sets of interconnected metabolites can be clustered together with respect to their relation to a covariable of interest

piRNAs, transposon silencing, and Drosophila germline development

Transposons are prominent features of most eukaryotic genomes and mobilization of these elements triggers genetic instability. Transposon silencing is particularly critical in the germline, which maintains the heritable genetic complement. Piwi-interacting RNAs (piRNAs) have emerged as central players in transposon silencing and genome maintenance during germline development. In particular, research on Drosophila oogenesis has provided critical insights into piRNA biogenesis and transposon silencing. In this system, the ability to place piRNA mutant phenotypes within a well-defined developmental framework has been instrumental in elucidating the molecular mechanisms underlying the connection between piRNAs and transposon control

The Caenorhabditis elegans HEN1 Ortholog, HENN-1, Methylates and Stabilizes Select Subclasses of Germline Small RNAs

Small RNAs regulate diverse biological processes by directing effector proteins called Argonautes to silence complementary mRNAs. Maturation of some classes of small RNAs involves terminal 2′-O-methylation to prevent degradation. This modification is catalyzed by members of the conserved HEN1 RNA methyltransferase family. In animals, Piwi-interacting RNAs (piRNAs) and some endogenous and exogenous small interfering RNAs (siRNAs) are methylated, whereas microRNAs are not. However, the mechanisms that determine animal HEN1 substrate specificity have yet to be fully resolved. In Caenorhabditis elegans, a HEN1 ortholog has not been studied, but there is evidence for methylation of piRNAs and some endogenous siRNAs. Here, we report that the worm HEN1 ortholog, HENN-1 (HEN of Nematode), is required for methylation of C. elegans small RNAs. Our results indicate that piRNAs are universally methylated by HENN-1. In contrast, 26G RNAs, a class of primary endogenous siRNAs, are methylated in female germline and embryo, but not in male germline. Intriguingly, the methylation pattern of 26G RNAs correlates with the expression of distinct male and female germline Argonautes. Moreover, loss of the female germline Argonaute results in loss of 26G RNA methylation altogether. These findings support a model wherein methylation status of a metazoan small RNA is dictated by the Argonaute to which it binds. Loss of henn-1 results in phenotypes that reflect destabilization of substrate small RNAs: dysregulation of target mRNAs, impaired fertility, and enhanced somatic RNAi. Additionally, the henn-1 mutant shows a weakened response to RNAi knockdown of germline genes, suggesting that HENN-1 may also function in canonical RNAi. Together, our results indicate a broad role for HENN-1 in both endogenous and exogenous gene silencing pathways and provide further insight into the mechanisms of HEN1 substrate discrimination and the diversity within the Argonaute family

Improving stability of prediction models based on correlated omics data by using network approaches

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset