Mitochondrial metabolism and neuroinflammation in the cerebral cortex and cortical synapses of rats: effect of milk intake through DNA methylation.

Brain plasticity and cognitive functions are tightly influenced by foods or nutrients, which determine a metabolic modulation having a long-term effect on health, involving also epigenetic mechanisms. Breast milk or formula based on cow milk is the first food for human beings, who, throughout their lives, are then exposed to different types of milk. We previously demonstrated that rats fed with milk derived from distinct species, with different compositions and nutritional properties, display selective modulation of systemic metabolic and inflammatory profiles through changes of mitochondrial functions and redox state in liver, skeletal and cardiac muscle. Here, in a rat model, we demonstrated that isoenergetic supplementation of milk from cow (CM), donkey (DM) or human (HM) impacts mitochondrial functions and redox state in the brain cortex and cortical synapses, affecting neuroinflammation and synaptic plasticity. Interestingly, we found that the administration of different milk modulates DNA methylation in rat brain cortex and consequently affects gene expression. Our results emphasize the importance of nutrition in brain and synapse physiology, and highlight the key role played in this context by mitochondria, nutrient-sensitive organelles able to orchestrate metabolic and inflammatory responses.

Phylogenomics of Acinetobacter species and analysis of antimicrobial resistance genes.

Introduction: Non-baumannii Acinetobacter species are increasingly isolated in the clinical setting and the environment. The aim of the present study was to analyze a genome database of 837 Acinetobacter spp. isolates, which included 798 non-baumannii Acinetobacter genomes, in order to define the concordance of classification and discriminatory power of 7-gene MLST, 53-gene MLST, and single-nucleotide polymorphism (SNPs) phylogenies. Methods: Phylogenies were performed on Pasteur Multilocus Sequence Typing (MLST) or ribosomal Multilocus Sequence Typing (rMLST) concatenated alleles, or SNPs extracted from core genome alignment. Results: The Pasteur MLST scheme was able to identify and genotype 72 species in the Acinetobacter genus, with classification results concordant with the ribosomal MLST scheme. The discriminatory power and genotyping reliability of the Pasteur MLST scheme were assessed in comparison to genome-wide SNP phylogeny on 535 non-baumannii Acinetobacter genomes assigned to Acinetobacter pittii, Acinetobacter nosocomialis, Acinetobacter seifertii, and Acinetobacter lactucae (heterotypic synonym of Acinetobacter dijkshoorniae), which were the most clinically relevant non-baumannii species of the A. baumannii group. The Pasteur MLST and SNP phylogenies were congruent at Robinson-Fould and Matching cluster tests and grouped genomes into four and three clusters in A. pittii, respectively, and one each in A. seifertii. Furthermore, A. lactucae genomes were grouped into one cluster within A. pittii genomes. The SNP phylogeny of A. nosocomialis genomes showed a heterogeneous population and did not correspond to the Pasteur MLST phylogeny, which identified two recombinant clusters. The antimicrobial resistance genes belonging to at least three different antimicrobial classes were identified in 91 isolates assigned to 17 distinct species in the Acinetobacter genus. Moreover, the presence of a class D oxacillinase, which is a naturally occurring enzyme in several Acinetobacter species, was found in 503 isolates assigned to 35 Acinetobacter species. Conclusion: In conclusion, Pasteur MLST phylogeny of non-baumannii Acinetobacter isolates coupled with in silico detection of antimicrobial resistance makes it important to study the population structure and epidemiology of Acinetobacter spp. isolates.

Accumulation of 8-oxodG within the human mitochondrial genome positively associates with transcription.

Mitochondrial DNA (mtDNA) can be subject to internal and environmental stressors that lead to oxidatively generated damage and the formation of 8-oxo-7,8-dihydro-2'-deoxyguanine (8-oxodG). The accumulation of 8-oxodG has been linked to degenerative diseases and aging, as well as cancer. Despite the well-described implications of 8-oxodG in mtDNA for mitochondrial function, there have been no reports of mapping of 8-oxodG across the mitochondrial genome. To address this, we used OxiDIP-Seq and mapped 8-oxodG levels in the mitochondrial genome of human MCF10A cells. Our findings indicated that, under steady-state conditions, 8-oxodG is non-uniformly distributed along the mitochondrial genome, and that the longer non-coding region appeared to be more protected from 8-oxodG accumulation compared with the coding region. However, when the cells have been exposed to oxidative stress, 8-oxodG preferentially accumulated in the coding region which is highly transcribed as H1 transcript. Our data suggest that 8-oxodG accumulation in the mitochondrial genome is positively associated with mitochondrial transcription.

Guadecitabine plus ipilimumab in unresectable melanoma: five-year follow-up and integrated multi-omic analysis in the phase 1b NIBIT-M4 trial.

Association with hypomethylating agents is a promising strategy to improve the efficacy of immune checkpoint inhibitors-based therapy. The NIBIT-M4 was a phase Ib, dose-escalation trial in patients with advanced melanoma of the hypomethylating agent guadecitabine combined with the anti-CTLA-4 antibody ipilimumab that followed a traditional 3 + 3 design (NCT02608437). Patients received guadecitabine 30, 45 or 60 mg/m2/day subcutaneously on days 1 to 5 every 3 weeks starting on week 0 for a total of four cycles, and ipilimumab 3 mg/kg intravenously starting on day 1 of week 1 every 3 weeks for a total of four cycles. Primary outcomes of safety, tolerability, and maximum tolerated dose of treatment were previously reported. Here we report the 5-year clinical outcome for the secondary endpoints of overall survival, progression free survival, and duration of response, and an exploratory integrated multi-omics analysis on pre- and on-treatment tumor biopsies. With a minimum follow-up of 45 months, the 5-year overall survival rate was 28.9% and the median duration of response was 20.6 months. Re-expression of immuno-modulatory endogenous retroviruses and of other repetitive elements, and a mechanistic signature of guadecitabine are associated with response. Integration of a genetic immunoediting index with an adaptive immunity signature stratifies patients/lesions into four distinct subsets and discriminates 5-year overall survival and progression free survival. These results suggest that coupling genetic immunoediting with activation of adaptive immunity is a relevant requisite for achieving long term clinical benefit by epigenetic immunomodulation in advanced melanoma patients.

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. RESULTS: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. AVAILABILITY AND IMPLEMENTATION: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.

Placental Metabolomics of Fetal Growth Restriction.

Fetal growth restriction is an obstetrical pathological condition that causes high neonatal mortality and morbidity. The mechanisms of its onset are not completely understood. Metabolites were extracted from 493 placentas from non-complicated pregnancies in Hamilton Country, TN (USA), and analyzed by gas chromatography-mass spectrometry (GC-MS). Newborns were classified according to raw fetal weight (low birth weight (LBW; <2500 g) and non-low birth weight (Non-LBW; >2500 g)), and according to the calculated birth weight centile as it relates to gestational age (small for gestational age (SGA), large for gestational age (LGA), and adequate for gestational age (AGA)). Mothers of LBW infants had a lower pre-pregnancy weight (66.2 ± 17.9 kg vs. 73.4 ± 21.3 kg, p < 0.0001), a lower body mass index (BMI) (25.27 ± 6.58 vs. 27.73 ± 7.83, p < 0.001), and a shorter gestation age (246.4 ± 24.0 days vs. 267.2 ± 19.4 days p < 0.001) compared with non-LBW. Marital status, tobacco use, and fetus sex affected birth weight centile classification according to gestational age. Multivariate statistical comparisons of the extracted metabolomes revealed that asparagine, aspartic acid, deoxyribose, erythritol, glycerophosphocholine, tyrosine, isoleucine, serine, and lactic acid were higher in both SGA and LBW placentas, while taurine, ethanolamine, ß-hydroxybutyrate, and glycine were lower in both SGA and LBW. Several metabolic pathways are implicated in fetal growth restriction, including those related to the hypoxia response and amino-acid uptake and metabolism. Inflammatory pathways are also involved, suggesting that fetal growth restriction might share some mechanisms with preeclampsia.

Reprogramming of the gut microbiota following feralization in Sus scrofa.

BACKGROUND: Wild boar has experienced several evolutionary trajectories from which domestic (under artificial selection) and the feral pig (under natural selection) originated. Strong adaptation deeply affects feral population's morphology and physiology, including the microbiota community. The gut microbiota is generally recognized to play a crucial role in maintaining host health and metabolism. To date, it is unclear whether feral populations' phylogeny, development stages or lifestyle have the greatest impact in shaping the gut microbiota, as well as how this can confer adaptability to new environments. Here, in order to deepen this point, we characterized the gut microbiota of feral population discriminating between juvenile and adult samples, and we compared it to the microbiota structure of wild boar and domestic pig as the references. Gut microbiota composition was estimated through the sequencing of the partial 16S rRNA gene by DNA metabarcoding and High Throughput Sequencing on DNA extracted from fecal samples. RESULTS: The comparison of microbiota communities among the three forms showed significant differences. The feral form seems to carry some bacteria of both domestic pigs, derived from its ancestral condition, and wild boars, probably as a sign of a recent re-adaptation strategy to the natural environment. In addition, interestingly, feral pigs show some exclusive bacterial taxa, also suggesting an innovative nature of the evolutionary trajectories and an ecological segregation in feral populations, as already observed for other traits. CONCLUSIONS: The feral pig showed a significant change between juvenile and adult microbiota suggesting an influence of the wild environment in which these populations segregate. However, it is important to underline that we certainly cannot overlook that these variations in the structure of the microbiota also depended on the different development stages of the animal, which in fact influence the composition of the intestinal microbiota. Concluding, the feral pigs represent a new actor living in the same geographical space as the wild boars, in which its gut microbial structure suggests that it is mainly the result of environmental segregation, most different from its closest relative. This gives rise to interesting fields of exploration regarding the changed ecological complexity and the consequent evolutionary destiny of the animal communities involved in this phenomenon.

On the Fidelity Robustness of CHSH-Bell Inequality via Filtered Random States.

The theorem developed by John Bell constituted the starting point of a revolution that translated a philosophical question about the nature of reality into the broad and intense field of research of the quantum information technologies. We focus on a system of two qubits prepared in a random, mixed state, and we study the typical behavior of their nonlocality via the CHSH-Bell inequality. Afterward, motivated by the necessity of accounting for inefficiency in the state preparation, we address to what extent states close enough to one with a high degree of nonclassicality can violate local realism with a previously chosen experimental setup.

A screening test proposal for congenital defects based on maternal serum metabolomics profile.

BACKGROUND: Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology. OBJECTIVE: This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects. STUDY DESIGN: This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort. RESULTS: Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]). CONCLUSION: This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

What is Nonclassical about Uncertainty Relations?

Uncertainty relations express limits on the extent to which the outcomes of distinct measurements on a single state can be made jointly predictable. The existence of nontrivial uncertainty relations in quantum theory is generally considered to be a way in which it entails a departure from the classical worldview. However, this perspective is undermined by the fact that there exist operational theories which exhibit nontrivial uncertainty relations but which are consistent with the classical worldview insofar as they admit of a generalized-noncontextual ontological model. This prompts the question of what aspects of uncertainty relations, if any, cannot be realized in this way and so constitute evidence of genuine nonclassicality. We here consider uncertainty relations describing the tradeoff between the predictability of a pair of binary-outcome measurements (e.g., measurements of Pauli X and Pauli Z observables in quantum theory). We show that, for a class of theories satisfying a particular symmetry property, the functional form of this predictability tradeoff is constrained by noncontextuality to be below a linear curve. Because qubit quantum theory has the relevant symmetry property, the fact that its predictability tradeoff describes a section of a circle is a violation of this noncontextual bound, and therefore constitutes an example of how the functional form of an uncertainty relation can witness contextuality. We also deduce the implications for a selected group of operational foils to quantum theory and consider the generalization to three measurements.

OxiDIP-Seq for Genome-wide Mapping of Damaged DNA Containing 8-Oxo-2'-Deoxyguanosine.

8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is considered to be a premutagenic DNA lesion generated by 2'-deoxyguanosine (dG) oxidation due to reactive oxygen species (ROS). In recent years, the 8-oxodG distribution in human, mouse, and yeast genomes has been underlined using various next-generation sequencing (NGS)-based strategies. The present study reports the OxiDIP-Seq protocol, which combines specific 8-oxodG immuno-precipitation of single-stranded DNA with NGS, and the pipeline analysis that allows the genome-wide 8-oxodG distribution in mammalian cells. The development of this OxiDIP-Seq method increases knowledge on the oxidative DNA damage/repair field, providing a high-resolution map of 8-oxodG in human cells.

A novel workflow for the qualitative analysis of DNA methylation data.

DNA methylation is an epigenetic modification that plays a pivotal role in major biological mechanisms, such as gene regulation, genomic imprinting, and genome stability. Different combinations of methylated cytosines for a given DNA locus generate different epialleles and alterations of these latter have been associated with several pathological conditions. Existing computational methods and statistical tests relevant to DNA methylation analysis are mostly based on the comparison of average CpG sites methylation levels and they often neglect non-CG methylation. Here, we present EpiStatProfiler, an R package that allows the analysis of CpG and non-CpG based epialleles starting from bisulfite sequencing data through a collection of dedicated extraction functions and statistical tests. EpiStatProfiler is provided with a set of useful auxiliary features, such as customizable genomic ranges, strand-specific epialleles analysis, locus annotation and gene set enrichment analysis. We showcase the package functionalities on two public datasets by identifying putative relevant loci in mice harboring the Huntington's disease-causing Htt gene mutation and in Ctcf +/- mice compared to their wild-type counterparts. To our knowledge, EpiStatProfiler is the first package providing functionalities dedicated to the analysis of epialleles composition derived from any kind of bisulfite sequencing experiment.

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery.

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models' classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

Targeted inhibition of ubiquitin signaling reverses metabolic reprogramming and suppresses glioblastoma growth.

Glioblastoma multiforme (GBM) is the most frequent and aggressive form of primary brain tumor in the adult population; its high recurrence rate and resistance to current therapeutics urgently demand a better therapy. Regulation of protein stability by the ubiquitin proteasome system (UPS) represents an important control mechanism of cell growth. UPS deregulation is mechanistically linked to the development and progression of a variety of human cancers, including GBM. Thus, the UPS represents a potentially valuable target for GBM treatment. Using an integrated approach that includes proteomics, transcriptomics and metabolic profiling, we identify praja2, a RING E3 ubiquitin ligase, as the key component of a signaling network that regulates GBM cell growth and metabolism. Praja2 is preferentially expressed in primary GBM lesions expressing the wild-type isocitrate dehydrogenase 1 gene (IDH1). Mechanistically, we found that praja2 ubiquitylates and degrades the kinase suppressor of Ras 2 (KSR2). As a consequence, praja2 restrains the activity of downstream AMP-dependent protein kinase in GBM cells and attenuates the oxidative metabolism. Delivery in the brain of siRNA targeting praja2 by transferrin-targeted self-assembling nanoparticles (SANPs) prevented KSR2 degradation and inhibited GBM growth, reducing the size of the tumor and prolonging the survival rate of treated mice. These data identify praja2 as an essential regulator of cancer cell metabolism, and as a potential therapeutic target to suppress GBM growth.

Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.

Nextcast: A software suite to analyse and model toxicogenomics data.

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

8-oxodG accumulation within super-enhancers marks fragile CTCF-mediated chromatin loops.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG), a major product of the DNA oxidization process, has been proposed to have an epigenetic function in gene regulation and has been associated with genome instability. NGS-based methodologies are contributing to the characterization of the 8-oxodG function in the genome. However, the 8-oxodG epigenetic role at a genomic level and the mechanisms controlling the genomic 8-oxodG accumulation/maintenance have not yet been fully characterized. In this study, we report the identification and characterization of a set of enhancer regions accumulating 8-oxodG in human epithelial cells. We found that these oxidized enhancers are mainly super-enhancers and are associated with bidirectional-transcribed enhancer RNAs and DNA Damage Response activation. Moreover, using ChIA-PET and HiC data, we identified specific CTCF-mediated chromatin loops in which the oxidized enhancer and promoter regions physically associate. Oxidized enhancers and their associated chromatin loops accumulate endogenous double-strand breaks which are in turn repaired by NHEJ pathway through a transcription-dependent mechanism. Our work suggests that 8-oxodG accumulation in enhancers-promoters pairs occurs in a transcription-dependent manner and provides novel mechanistic insights on the intrinsic fragility of chromatin loops containing oxidized enhancers-promoters interactions.

A Metabolomics-Based Screening Proposal for Colorectal Cancer.

Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data.

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

Microarray Data Preprocessing: From Experimental Design to Differential Analysis.

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.