A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data.

BACKGROUND: Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods. RESULTS: We developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors. CONCLUSIONS: DMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest che be found at http://dmi.tigem.it

Correlation of physical and cognitive impairment in diabetic and hypertensive frail older adults

Background: Diabetes and hypertension are common in older adults and represent established risk factors for frailty. Frailty is a multidimensional condition due to reserve loss and susceptibility to stressors with a high risk of death, hospitalizations, functional and cognitive impairment. Comorbidities such as diabetes and hypertension play a key role in increasing the risk of mortality, hospitalization, and disability. Moreover, frail patients with diabetes and hypertension are known to have an increased risk of cognitive and physical impairment. Nevertheless, no study assessed the correlation between physical and cognitive impairment in frail older adults with diabetes and hypertension. Methods: We evaluated consecutive frail older patients with diabetes and hypertension who presented at ASL (local health unit of the Italian Ministry of Health) Avellino, Italy, from March 2021 to October 2021. The inclusion criteria were: a previous diagnosis of diabetes and hypertension with no evidence of secondary causes; age > 65 years; a frailty status; Montreal Cognitive Assessment (MoCA) score < 26. Results: 179 patients successfully completed the study. We found a strong and significant correlation between MoCA score and 5-m gait speed test (r: 0.877; p < 0.001). To further verify our results, we performed a linear multivariate analysis adjusting for potential confounding factors, with MoCA score as dependent variable, which confirmed the significant association with glycemia (p < 0.001). Conclusions: This is the first study showing a significant correlation between 5-m gait speed test and MoCA score in frail diabetic and hypertensive older adults

Sex differences in blood pro-oxidant status and platelet activation in children admitted with respiratory syncytial virus bronchiolitis. a pilot study

Background: Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis in the pediatric population worldwide and an important cause of death in developing countries. It has been demonstrated that the balance between oxidant and antioxidant systems is disrupted in children with bronchiolitis and that oxidative stress contributes to the pathogenesis of this disease. Platelets play an important role in antimicrobial host defenses and contribute to pulmonary vascular repair being either targets or source of reactive oxidizing species. The main purpose of this study was to assessing sex differences in clinical characteristics and platelets activation during RSV bronchiolitis in infancy. Methods: In this retrospective study a total of 203 patients (112 boys and 91 girls) with bronchiolitis, aged 12 months or less, admitted to the Bambino Gesù Pediatric Hospital of Rome (Italy) in the period from January to December 2017, were enrolled. Moreover, in a select group of patients (15 boys and 12 girls) with diagnosis of moderate bronchiolitis from RSV, a pilot study on oxidative stress and platelet characteristics was carried out by electron paramagnetic resonance and flow cytometry respectively. Age-matched healthy control subjects (10 boys and 10 girls) were chosen as controls. Data were analyzed using Student’ T test, Chi Squared test and one-way ANOVA test. Results: This study highlights the influence of sex in the clinical course of bronchiolitis. In particular we found: i) a higher incidence of bronchiolitis in boys than in girls (55% vs 45%); ii) higher C reactive protein values in girls than boys (1.11 mg/dL vs 0.92 mg/dL respectively; p < 0.05); iii) a different degree of thrombocytosis during hospitalization (mild in the girls and severe in the boys). Moreover, in selected patients we found that compared to girls with bronchiolitis, boys showed: i) higher percentage of activated platelets (8% vs 2% respectively; p < 0.05) and iii) higher number of platelets forming homotypic aggregates (2.36% vs 0.84% respectively, p < 0.05)

Sex-based electroclinical differences and prognostic factors in epilepsy with eyelid myoclonia

Although a striking female preponderance has been consistently reported in epilepsy with eyelid myoclonia (EEM), no study has specifically explored the variability of clinical presentation according to sex in this syndrome. Here, we aimed to investigate sex-specific electroclinical differences and prognostic determinants in EEM. Data from 267 EEM patients were retrospectively analyzed by the EEM Study Group, and a dedicated multivariable logistic regression analysis was developed separately for each sex. We found that females with EEM showed a significantly higher rate of persistence of photosensitivity and eye closure sensitivity at the last visit, along with a higher prevalence of migraine with/without aura, whereas males with EEM presented a higher rate of borderline intellectual functioning/intellectual disability. In female patients, multivariable logistic regression analysis revealed age at epilepsy onset, eyelid myoclonia status epilepticus, psychiatric comorbidities, and catamenial seizures as significant predictors of drug resistance. In male patients, a history of febrile seizures was the only predictor of drug resistance. Hence, our study reveals sex-specific differences in terms of both electroclinical features and prognostic factors. Our findings support the importance of a sex-based personalized approach in epilepsy care and research, especially in genetic generalized epilepsies

The spectrum of epilepsy with eyelid myoclonia: delineation of disease subtypes from a large multicenter study

Objective Epilepsy with eyelid myoclonia (EEM) has been associated with marked clinical heterogeneity. Early epilepsy onset has been recently linked to lower chances of achieving sustained remission and to a less favorable neuropsychiatric outcome. However, much work is still needed to better delineate this epilepsy syndrome. Methods In this multicenter retrospective cohort study, we included 267 EEM patients from nine countries. Data on electroclinical and demographic features, intellectual functioning, migraine with or without aura, family history of epilepsy, and epilepsy syndromes in relatives were collected in each patient. The impact of age at epilepsy onset (AEO) on EEM clinical features was investigated, along with the distinctive clinical characteristics of patients showing sporadic myoclonia involving body regions other than eyelids (body-MYO). Results Kernel density estimation revealed a trimodal distribution of AEO, and Fisher-Jenks optimization disclosed three EEM subgroups: early onset (EO-EEM), intermediate onset (IO-EEM), and late onset (LO-EEM). EO-EEM was associated with the highest rate of intellectual disability, antiseizure medication refractoriness, and psychiatric comorbidities and with the lowest rate of family history of epilepsy. LO-EEM was associated with the highest proportion of body-MYO and generalized tonic-clonic seizures (GTCS), whereas IO-EEM had the lowest observed rate of additional findings. A family history of EEM was significantly more frequent in IO-EEM and LO-EEM compared with EO-EEM. In the subset of patients with body-MYO (58/267), we observed a significantly higher rate of migraine and GTCS but no relevant differences in other electroclinical features and seizure outcome. Significance Based on AEO, we identified consistent EEM subtypes characterized by distinct electroclinical and familial features. Our observations shed new light on the spectrum of clinical features of this generalized epilepsy syndrome and may help clinicians toward a more accurate classification and prognostic profiling of EEM patients

Identification of transcriptional and post-translational regulatory networks from gene expression profile: an information-theoretic approach.

The reconstruction of the regulatory interactions among DNA, RNAs and proteins in a cell is probably the most important and key challenge in molecular biology. In the last decade, the introductions of new high-throughput technologies, such as microarrays and, more recently, next generation sequencing (NGS) have facilitated this task. Different Systems Biology approaches have been proposed to reconstruct the transcriptional, post-transcriptional and the post-translational regulatory networks of a cell starting from genomics data. The two aims of the research here described are: (1) the development and the application of a computational method for the identification of tissue-specific, or more broadly, condition-specific pathways; (2) the development and the application of a computational approach for the identification of post-translational modulators of transcription factor activity from gene expression profiles. In Chapter 1, I provide a brief overview of the different molecular networks known to exist in a living cell. Chapter 2 illustrates a comparative study of the different approaches to reverse-engineering gene networks from gene expression profiles (GEPs) and their limitations. Current state-of-the-art reverse-engineering approaches model gene networks as static processes, i.e. regulatory interactions among genes in the network (such as direct physical interactions or indirect functional interactions) do not change across different conditions or tissue types. However, different cell-types, or the same cell-type but in different conditions, may carry out very different functions, thus it is expected that their regulatory networks may reflect these differences. In Chapter 3 and 4, I describe the development of a novel approach named DINA (Differential Network Analysis) for the identification of differentially co-regulated pathways. DINA is based on the hypothesis that genes belonging to a condition-specific pathway are actively co-regulated only when the pathway is active, independently of their absolute level of expression. I first reverse-engineered 30 tissue-specific networks from a collection of about 3000 GEPs. I then applied DINA to these networks in order to identify tissue-specific pathways starting from a list of 110 KEGG-annotated pathways. As expected, DINA predicted many metabolic pathways to be tissue-specific and prevalently active in liver and kidney. I then built a simplified model of hepatocellular carcinoma (HCC) to mimic the HCC progression using three condition-specific regulatory networks obtained from three different cell-lines: (i) primary hepatocyte, (ii) HepG2 and (iii) Huh7. Using these three cell-type specific networks, I demonstrated that DINA can be used to make hypotheses on dysregulated pathways during disease progression. DINA is also able to predict which Transcription Factors (TFs) may be responsible for the pathway condition-specific co-regulation. I tested this approach to identify regulators of tissue-specific metabolic pathways, and I correctly identified Nuclear Receptors as their main regulators. With this method, I was also able to identify a new putative tissue-specific negative regulator of hepatocyte metabolism: Yeats2. In Chapter 5, 6 and 7, I propose a generalized method that I called Differential Multi-Information (DMI) to identify post-translational modulators M of a transcription factor TF by observing the changes in co-regulation (measured by Multi-Information) among a set of n target genes G_1⋯G_n in the presence or absence of the modulator M. My working hypothesis is that the set of target genes will be strongly co-regulated only when the modulator M is present, since the modulator will active the TF. The DMI algorithm requires in input a set of known target genes regulated by a common TF, and it returns in output a ranked list of predicted post-translational modulators of the TF. I first validated the approach using an “in-silico” datasets consisting of 100 GEPs and 760 genes. Next, I tested DMI performance on a real gene expression profile dataset, by identifying the post-translational modulators of 7 transcription factors for which I was able to collect a list of high-confident targets. This set of transcription factors included transcription factors such as P53, MYC and STAT3. Finally, as a case of study, I tested the DMI method on a transcription factor TFEB recently identified as a master regulator of lysosomal biogenesis and autophagy. By comparing the results of DMI with a High Content Screening (HCS) using siRNA oligo libraries against all the known phosphatases, I was able to show that DMI can achieve a very high precision. All these results confirm that DMI could be instrumental in identifying post-translational regulatory interactions in an efficient and cost-effective manner

Predicting drug response from single-cell expression profiles of tumours

Abstract Background Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention. Results Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. Conclusions DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP