Molecular EPISTOP, a comprehensive multi-omic analysis of blood from Tuberous Sclerosis Complex infants age birth to two years

We present a comprehensive multi-omic analysis of the EPISTOP prospective clinical trial of early intervention with vigabatrin for pre-symptomatic epilepsy treatment in Tuberous Sclerosis Complex (TSC), in which 93 infants with TSC were followed from birth to age 2 years, seeking biomarkers of epilepsy development. Vigabatrin had profound effects on many metabolites, increasing serum deoxycytidine monophosphate (dCMP) levels 52-fold. Most serum proteins and metabolites, and blood RNA species showed significant change with age. Thirty-nine proteins, metabolites, and genes showed significant differences between age-matched control and TSC infants. Six also showed a progressive difference in expression between control, TSC without epilepsy, and TSC with epilepsy groups. A multivariate approach using enrollment samples identified multiple 3-variable predictors of epilepsy, with the best having a positive predictive value of 0.987. This rich dataset will enable further discovery and analysis of developmental effects, and associations with seizure development in TSC.

Early epileptiform EEG activity in infants with tuberous sclerosis complex predicts epilepsy and neurodevelopmental outcomes

OBJECTIVE

To study the association between timing and characteristics of the first electroencephalography (EEG) with epileptiform discharges (ED-EEG) and epilepsy and neurodevelopment at 24 months in infants with tuberous sclerosis complex (TSC).

METHODS

Patients enrolled in the prospective Epileptogenesis in a genetic model of epilepsy - Tuberous sclerosis complex (EPISTOP) trial, had serial EEG monitoring until the age of 24 months. The timing and characteristics of the first ED-EEG were studied in relation to clinical outcome. Epilepsy-related outcomes were analyzed separately in a conventionally followed group (initiation of vigabatrin after seizure onset) and a preventive group (initiation of vigabatrin before seizures, but after appearance of interictal epileptiform discharges [IEDs]).

RESULTS

Eighty-three infants with TSC were enrolled at a median age of 28 days (interquartile range [IQR] 14-54). Seventy-nine of 83 patients (95%) developed epileptiform discharges at a median age of 77 days (IQR 23-111). Patients with a pathogenic TSC2 variant were significantly younger (P-value .009) at first ED-EEG and more frequently had multifocal IED (P-value .042) than patients with a pathogenic TSC1 variant. A younger age at first ED-EEG was significantly associated with lower cognitive (P-value .010), language (P-value .001), and motor (P-value .013) developmental quotients at 24 months. In the conventional group, 48 of 60 developed seizures. In this group, the presence of focal slowing on the first ED-EEG was predictive of earlier seizure onset (P-value .030). Earlier recording of epileptiform discharges (P-value .019), especially when multifocal (P-value .026) was associated with higher risk of drug-resistant epilepsy. In the preventive group, timing, distribution of IED, or focal slowing, was not associated with the epilepsy outcomes. However, when multifocal IEDs were present on the first ED-EEG, preventive treatment delayed the onset of seizures significantly (P-value <.001).

SIGNIFICANCE

Early EEG findings help to identify TSC infants at risk of severe epilepsy and neurodevelopmental delay and those who may benefit from preventive treatment with vigabatrin.

Prevention of Epilepsy in Infants with Tuberous Sclerosis Complex in the EPISTOP Trial

OBJECTIVE

Epilepsy develops in 70 to 90% of children with tuberous sclerosis complex (TSC) and is often resistant to medication. Recently, the concept of preventive antiepileptic treatment to modify the natural history of epilepsy has been proposed. EPISTOP was a clinical trial designed to compare preventive versus conventional antiepileptic treatment in TSC infants.

METHODS

In this multicenter study, 94 infants with TSC without seizure history were followed with monthly video electroencephalography (EEG), and received vigabatrin either as conventional antiepileptic treatment, started after the first electrographic or clinical seizure, or preventively when epileptiform EEG activity before seizures was detected. At 6 sites, subjects were randomly allocated to treatment in a 1:1 ratio in a randomized controlled trial (RCT). At 4 sites, treatment allocation was fixed; this was denoted an open-label trial (OLT). Subjects were followed until 2 years of age. The primary endpoint was the time to first clinical seizure.

RESULTS

In 54 subjects, epileptiform EEG abnormalities were identified before seizures. Twenty-seven were included in the RCT and 27 in the OLT. The time to the first clinical seizure was significantly longer with preventive than conventional treatment [RCT: 364 days (95% confidence interval [CI] = 223-535) vs 124 days (95% CI = 33-149); OLT: 426 days (95% CI = 258-628) vs 106 days (95% CI = 11-149)]. At 24 months, our pooled analysis showed preventive treatment reduced the risk of clinical seizures (odds ratio [OR] = 0.21, p = 0.032), drug-resistant epilepsy (OR = 0.23, p = 0.022), and infantile spasms (OR = 0, p < 0.001). No adverse events related to preventive treatment were noted.

INTERPRETATION

Preventive treatment with vigabatrin was safe and modified the natural history of seizures in TSC, reducing the risk and severity of epilepsy. ANN NEUROL 2021;89:304-314.

Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics

Tuberous Sclerosis Complex (TSC) is a multisystem genetic disorder with a high risk of early-onset epilepsy and a high prevalence of neurodevelopmental comorbidities, including intellectual disability and autism spectrum disorder (ASD). Therefore, TSC is an interesting disease model to investigate early biomarkers of neurodevelopmental comorbidities when interventions are favourable. We investigated whether early EEG characteristics can be used to predict neurodevelopment in infants with TSC. The first recorded EEG of 64 infants with TSC, enrolled in the international prospective EPISTOP trial (recorded at a median gestational age 42 4/7 weeks) was first visually assessed. EEG characteristics were correlated with ASD risk based on the ADOS-2 score, and cognitive, language, and motor developmental quotients (Bayley Scales of Infant and Toddler Development III) at the age of 24 months. Quantitative EEG analysis was used to validate the relationship between EEG background abnormalities and ASD risk. An abnormal first EEG (OR = 4.1, p-value = 0.027) and more specifically a dysmature EEG background (OR = 4.6, p-value = 0.017) was associated with a higher probability of ASD traits at the age of 24 months. This association between an early abnormal EEG and ASD risk remained significant in a multivariable model, adjusting for mutation and treatment (adjusted OR = 4.2, p-value = 0.029). A dysmature EEG background was also associated with lower cognitive (p-value = 0.029), language (p-value = 0.001), and motor (p-value = 0.017) developmental quotients at the age of 24 months. Our findings suggest that early EEG characteristics in newborns and infants with TSC can be used to predict neurodevelopmental comorbidities.

Is autism driven by epilepsy in infants with Tuberous Sclerosis Complex?

Objective

To evaluate the relationship between age at seizure onset and neurodevelopmental outcome at age 24 months in infants with TSC, as well as the effect on neurodevelopmental outcome of early versus conventional treatment of epileptic seizures with vigabatrin (80–150 mg/kg/day).

Methods

Infants with TSC, aged ≤4 months and without previous seizures were enrolled in a prospective study and closely followed with monthly video EEG and serial standardized neurodevelopmental testing (Bayley Scales of Infant Development and Autism Diagnostic Observation Schedule).

Results

Eighty infants were enrolled. At the age of 24 months testing identified risk of Autism Spectrum Disorder (ASD) in 24/80 children (30.0%), and developmental delay (DD) in 26/80 (32.5%). Children with epilepsy (51/80; 63.8%) had a higher risk of ASD (P = 0.02) and DD (P = 0.001). Overall, no child presented with moderate or severe DD at 24 months (developmental quotient < 55). In 20% of children abnormal developmental trajectories were detected before the onset of seizures. Furthermore, 21% of all children with risk of ASD at 24 months had not developed seizures at that timepoint. There was no significant difference between early and conventional treatment with respect to rate of risk of ASD (P = 0.8) or DD (P = 0.9) at 24 months.

Interpretation

This study confirms a relationship between epilepsy and risk of ASD/DD. However, in this combined randomized/open label study, early treatment with vigabatrin did not alter the risk of ASD or DD at age 2 years.

Protein and peptide profiles in neonatal meconium

AIM

The proteins accumulated in the meconium reflect the intrauterine environment and are naturally excreted by a neonate. The identification and classification of individual meconium proteins may be a valuable source of information about physiological and pathological processes in utero.

METHODS

Proteomic analysis was used to study the protein composition in pooled 50 serial meconium portions from 10 neonates. The proteins were classified based on the gene ontology database. The amounts and relative number of proteins (%) in the identified categories and their subcategories were assessed.

RESULTS

A total of 946 proteins identified in meconium, including 430 represented by two or more peptides were classified into three categories: biological process (n = 401), molecular function (n = 386) and cellular component (n = 422). The highest number of proteins (>25% of the total) was found in the subcategories: developmental processes, signaling, transport, response to stimulus, regulation, metabolic processes, ion binding, extracellular region, membrane and cytoplasm.

CONCLUSION

The composition of meconium proteins identified in this study may be a rich source of new biomarkers for use in neonatology with a potential to predict later development.

Smooth muscle contamination analysis in clinical oncology gene expression research

Gene expression profiling is one of the most explored methods for studying cancers and microarray data repositories have become a rich and important resource. The most common human cancers develop in organs that are walled by smooth muscles. The only method of sample extraction free of unintentional contamination with surrounding tissue is microdissection. Nevertheless, such an approach is implemented infrequently. In the light of the above, there is a possibility of smooth muscle contamination in a large portion of publicly available data. In this study, 2292 publicly available microarrays were analysed to develop a simple screening method for detecting smooth muscle contamination. Microarray Inspector software was used to perform the tests since it has the unique ability to use many selected genes and probesets in a single group as a tissue definition. Furthermore, the test was dataset-independent. Two strategies of tissue definition were explored and compared. The first one depended on Tissue Specific Genes Database (TiSGeD) and BioGPS web resources, which themselves were based on meta-analysis of thousands of microarrays. The second method was based on a differential gene expression analysis of a few hundred preselected arrays. The comparison of the two methods proved the latter to be superior. Among the tested samples of undefined contamination, nearly half were identified to possibly contain significant smooth muscle traces. The obtained results equip researches with a simple method of examining microarray data for smooth muscle contamination. The presented work serves as an example of how to create definitions when searching for other possible contaminations.

Smooth muscle contamination analysis in clinical oncology gene expression research

Gene expression profiling is one of the most explored methods for studying cancers and microarray data repositories have become a rich and important resource. The most common human cancers develop in organs that are walled by smooth muscles. The only method of sample extraction free of unintentional contamination with surrounding tissue is microdissection. Nevertheless, such an approach is implemented infrequently. In the light of the above, there is a possibility of smooth muscle contamination in a large portion of publicly available data. In this study, 2292 publicly available microarrays were analysed to develop a simple screening method for detecting smooth muscle contamination. Microarray Inspector software was used to perform the tests since it has the unique ability to use many selected genes and probesets in a single group as a tissue definition. Furthermore, the test was dataset-independent. Two strategies of tissue definition were explored and compared. The first one depended on Tissue Specific Genes Database (TiSGeD) and BioGPS web resources, which themselves were based on meta-analysis of thousands of microarrays. The second method was based on a differential gene expression analysis of a few hundred preselected arrays. The comparison of the two methods proved the latter to be superior. Among the tested samples of undefined contamination, nearly half were identified to possibly contain significant smooth muscle traces. The obtained results equip researches with a simple method of examining microarray data for smooth muscle contamination. The presented work serves as an example of how to create definitions when searching for other possible contaminations.

Microarray Inspector: tissue cross contamination detection tool for microarray data

Microarray technology changed the landscape of contemporary life sciences by providing vast amounts of expression data. Researchers are building up repositories of experiment results with various conditions and samples which serve the scientific community as a precious resource. Ensuring that the sample is of high quality is of utmost importance to this effort. The task is complicated by the fact that in many cases datasets lack information concerning pre-experimental quality assessment. Transcription profiling of tissue samples may be invalidated by an error caused by heterogeneity of the material. The risk of tissue cross contamination is especially high in oncological studies, where it is often difficult to extract the sample. Therefore, there is a need of developing a method detecting tissue contamination in a post-experimental phase. We propose Microarray Inspector: customizable, user-friendly software that enables easy detection of samples containing mixed tissue types. The advantage of the tool is that it uses raw expression data files and analyses each array independently. In addition, the system allows the user to adjust the criteria of the analysis to conform to individual needs and research requirements. The final output of the program contains comfortable to read reports about tissue contamination assessment with detailed information about the test parameters and results. Microarray Inspector provides a list of contaminant biomarkers needed in the analysis of adipose tissue contamination. Using real data (datasets from public repositories) and our tool, we confirmed high specificity of the software in detecting contamination. The results indicated the presence of adipose tissue admixture in a range from approximately 4% to 13% in several tested surgical samples.

Microarray Inspector: tissue cross contamination detection tool for microarray data

Microarray technology changed the landscape of contemporary life sciences by providing vast amounts of expression data. Researchers are building up repositories of experiment results with various conditions and samples which serve the scientific community as a precious resource. Ensuring that the sample is of high quality is of utmost importance to this effort. The task is complicated by the fact that in many cases datasets lack information concerning pre-experimental quality assessment. Transcription profiling of tissue samples may be invalidated by an error caused by heterogeneity of the material. The risk of tissue cross contamination is especially high in oncological studies, where it is often difficult to extract the sample. Therefore, there is a need of developing a method detecting tissue contamination in a post-experimental phase. We propose Microarray Inspector: customizable, user-friendly software that enables easy detection of samples containing mixed tissue types. The advantage of the tool is that it uses raw expression data files and analyses each array independently. In addition, the system allows the user to adjust the criteria of the analysis to conform to individual needs and research requirements. The final output of the program contains comfortable to read reports about tissue contamination assessment with detailed information about the test parameters and results. Microarray Inspector provides a list of contaminant biomarkers needed in the analysis of adipose tissue contamination. Using real data (datasets from public repositories) and our tool, we confirmed high specificity of the software in detecting contamination. The results indicated the presence of adipose tissue admixture in a range from approximately 4% to 13% in several tested surgical samples.