Context base editing for splice correction of IVSI-110 ß-thalassemia.

ß-Thalassemia is brought about by defective ß-globin (HBB [hemoglobin subunit ß]) formation and, in severe cases, requires regular blood transfusion and iron chelation for survival. Genome editing of hematopoietic stem cells allows correction of underlying mutations as curative therapy. As potentially safer alternatives to double-strand-break-based editors, base editors (BEs) catalyze base transitions for precision editing of DNA target sites, prompting us to reclone and evaluate two recently published adenine BEs (ABEs; SpRY and SpG) with relaxed protospacer adjacent motif requirements for their ability to correct the common HBBIVSI-110(G>A) splice mutation. Nucleofection of ABE components as RNA into patient-derived CD34+ cells achieved up to 90% editing of upstream sequence elements critical for aberrant splicing, allowing full characterization of the on-target base-editing profile of each ABE and the detection of differences in on-target insertions and deletions. In addition, this study identifies opposing effects on splice correction for two neighboring context bases, establishes the frequency distribution of multiple BE editing events in the editing window, and shows high-efficiency functional correction of HBBIVSI-110(G>A) for our ABEs, including at the levels of RNA, protein, and erythroid differentiation.

RFC1 Repeat Distribution in the Cypriot Population: Study of a Large Cohort of Patients With Undiagnosed Ataxia and Non-Disease Controls.

Background and Objectives: The intronic biallelic AAGGG expansion in the replication factor C subunit 1 (RFC1) gene was recently associated with a phenotype combining cerebellar ataxia, neuropathy, and vestibular areflexia syndrome, as well as with late-onset ataxia. Following this discovery, studies in multiple populations extended the phenotypic and genotypic spectrum of this locus. Multiple benign and additional pathogenic configurations are currently known. Our main objectives were to study the prevalence of the pathogenic AAGGG expansion in the Cypriot population, to further characterize the RFC1 repeat locus allele distribution, and to search for possible novel repeat configurations. Methods: Cypriot undiagnosed patients, in the majority presenting at least with cerebellar ataxia and non-neurologic disease controls, were included in this study. A combination of conventional methods was used, including standard PCR flanking the repeat region, repeat-primed PCR, long-range PCR, and Sanger sequencing. Bioinformatics analysis of already available in-house short-read whole-genome sequencing data was also performed. Results: A large group of undiagnosed patients (n = 194), mainly presenting with pure ataxia or with ataxia accompanied by neuropathy or additional symptoms, as well as a group of non-disease controls (n = 100), were investigated in the current study. Our findings include the diagnosis of 10 patients homozygous for the pathogenic AAGGG expansion and a high percentage of heterozygous AAGGG carriers in both groups. The benign AAAAGn, AAAGGn, and AAGAGn configurations were also identified in our cohorts. We also report and discuss the identification of 2 recently reported novel and possibly benign repeat configurations, AAAGGGn and AAGACn, thus confirming their existence in another distinct population, and we highlight an increased frequency of the AAAGGGn in the patient group, including a single case of homozygosity. Discussion: Our findings indicate the existence of genetic heterogeneity regarding the RFC1 repeat configurations and that the AAGGG pathogenic expansion is a frequent cause of ataxia in the Cypriot population.

A novel large intragenic DPYD deletion causing dihydropyrimidine dehydrogenase deficiency: a case report.

BACKGROUND: Dihydropyrimidine dehydrogenase (DPD), is the initial and rate-limiting enzyme in the catabolic pathway of pyrimidines. Deleterious variants in the DPYD gene cause DPD deficiency, a rare autosomal recessive disorder. The clinical spectrum of affected individuals is wide ranging from asymptomatic to severely affected patients presenting with intellectual disability, motor retardation, developmental delay and seizures. DPD is also important as the main enzyme in the catabolism of 5-fluorouracil (5-FU) which is extensively used as a chemotherapeutic agent. Even in the absence of clinical symptoms, individuals with either complete or partial DPD deficiency face a high risk of severe and even fatal fluoropyrimidine-associated toxicity. The identification of causative genetic variants in DPYD is therefore gaining increasing attention due to their potential use as predictive markers of fluoropyrimidine toxicity. METHODS: A male infant patient displaying biochemical features of DPD deficiency was investigated by clinical exome sequencing. Bioinformatics tools were used for data analysis and results were confirmed by MLPA and Sanger sequencing. RESULTS: A novel intragenic deletion of 71.2 kb in the DPYD gene was identified in homozygosity. The deletion, DPYD(NM_000110.4):c.850 + 23455_1128 + 8811del, eliminates exons 9 and 10 and may have resulted from a non-homologous end-joining event, as suggested by in silico analysis. CONCLUSIONS: The study expands the spectrum of DPYD variants associated with DPD deficiency. Furthermore, it raises the concern that patients at risk for fluoropyrimidine toxicity due to DPYD deletions could be missed during pre-treatment genetic testing for the currently recommended single nucleotide polymorphisms.

Oct4 is a gatekeeper of epithelial identity by regulating cytoskeletal organization in skin keratinocytes.

Oct4 is a pioneer transcription factor regulating pluripotency. However, it is not well known whether Oct4 has an impact on epidermal cells. We generated OCT4 knockout clonal cell lines using immortalized human skin keratinocytes to identify a functional role for the protein. Here, we report that Oct4-deficient cells transitioned into a mesenchymal-like phenotype with enlarged size and shape, exhibited accelerated migratory behavior, decreased adhesion, and appeared arrested at the G2/M cell cycle checkpoint. Oct4 absence had a profound impact on cortical actin organization, with loss of microfilaments from the cell membrane, increased puncta deposition in the cytoplasm, and stress fiber formation. E-cadherin, ß-catenin, and ZO1 were almost absent from cell-cell contacts, while fibronectin deposition was markedly increased in the extracellular matrix (ECM). Mapping of the transcriptional and chromatin profiles of Oct4-deficient cells revealed that Oct4 controls the levels of cytoskeletal, ECM, and differentiation-related genes, whereas epithelial identity is preserved through transcriptional and non-transcriptional mechanisms.

List2Net: Linking multiple lists of biological data in a network context.

Motivation: A common task in scientific research is the comparison of lists or sets of diverse biological entities such as biomolecules, ontologies, sequences and expression profiles. Such comparisons rely, one way or another, on calculating a measure of similarity either by means of vector correlation metrics, set operations such as union and intersection, or specific measures to capture, for example, sequence homology. Subsequently, depending on the data type, the results are often visualized using heatmaps, Venn, Euler, or Alluvial diagrams. While most of the abovementioned representations offer simplicity and interpretability, their effectiveness holds only for a limited number of lists and specific data types. Conversely, network representations provide a more versatile approach where data lists are viewed as interconnected nodes, with edges representing pairwise commonality, correlation, or any other similarity metric. Networks can represent an arbitrary number of lists of any data type, offering a holistic perspective and most importantly, enabling analytics for characterizing and discovering novel insights in terms of centralities, clusters and motifs that can exist in such networks. While several tools that implement the translation of lists to the various commonly used diagrams, such as Venn and Euler, have been developed, a similar tool that can parse, analyze the commonalities and generate networks from an arbitrary number of lists of the same or heterogenous content does not exist. Results: To address this gap, we introduce List2Net, a web-based tool that can rapidly process and represent lists in a network context, either in a single-layer or multi-layer mode, facilitating network analysis on multi-source/multi-layer data. Specifically, List2Net can seamlessly handle lists encompassing a wide variety of biological data types, such as named entities or ontologies (e.g., lists containing gene symbols), sequences (e.g., protein/peptide sequences), and numeric data types (e.g., omics-based expression or abundance profiles). Once the data is imported, the tool then (i) calculates the commonalities or correlations (edges) between the lists (nodes) of interest, (ii) generates and renders the network for visualization and analysis and (iii) provides a range of exporting options, including vector, raster format visualization but also the calculated edge lists and metrics in tabular format for further analysis in other tools. List2Net is a fast, lightweight, yet informative application that provides network-based holistic insights into the conditions represented by the lists of interest (e.g., disease-to-disease, gene-to-phenotype, drug-to-disease, etc.). As a case study, we demonstrate the utility of this tool applied on publicly available datasets related to Multiple Sclerosis (MS). Using the tool, we showcase the translation of various ontologies characterizing this specific condition on disease-to-disease subnetworks of neurodegenerative, autoimmune and infectious diseases generated from various levels of information such as genetic variation, genes, proteins, metabolites and phenotypic terms.

Selective Delivery to Cardiac Muscle Cells Using Cell-Specific Aptamers.

In vivo SELEX is an advanced adaptation of Systematic Evolution of Ligands by Exponential Enrichment (SELEX) that allows the development of aptamers capable of recognizing targets directly within their natural microenvironment. While this methodology ensures a higher translation potential for the selected aptamer, it does not select for aptamers that recognize specific cell types within a tissue. Such aptamers could potentially improve the development of drugs for several diseases, including neuromuscular disorders, by targeting solely the proteins involved in their pathogenesis. Here, we describe our attempt to utilize in vivo SELEX with a modification in the methodology that drives the selection of intravenously injected aptamers towards a specific cell type of interest. Our data suggest that the incorporation of a cell enrichment step can direct the in vivo localization of RNA aptamers into cardiomyocytes, the cardiac muscle cells, more readily over other cardiac cells. Given the crucial role of cardiomyocytes in the disease pathology in DMD cardiomyopathy and therapy, these aptamers hold great potential as drug delivery vehicles with cardiomyocyte selectivity.

Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures.

Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.

Vir2Drug: a drug repurposing framework based on protein similarities between pathogens.

We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.

Circulating small RNA signatures differentiate accurately the subtypes of muscular dystrophies: small-RNA next-generation sequencing analytics and functional insights.

Muscular dystrophies are a group of rare and severe inherited disorders mainly affecting the muscle tissue. Duchene Muscular Dystrophy, Myotonic Dystrophy types 1 and 2, Limb Girdle Muscular Dystrophy and Facioscapulohumeral Muscular Dystrophy are some of the members of this family of disorders. In addition to the current diagnostic tools, there is an increasing interest for the development of novel non-invasive biomarkers for the diagnosis and monitoring of these diseases. miRNAs are small RNA molecules characterized by high stability in blood thus making them ideal biomarker candidates for various diseases. In this study, we present the first genome-wide next-generation small RNA sequencing in serum samples of five different types of muscular dystrophy patients and healthy individuals. We identified many small RNAs including miRNAs, lncRNAs, tRNAs, snoRNAs and snRNAs, that differentially discriminate the muscular dystrophy patients from the healthy individuals. Further analysis of the identified miRNAs showed that some miRNAs can distinguish the muscular dystrophy patients from controls and other miRNAs are specific to the type of muscular dystrophy. Bioinformatics analysis of the target genes for the most significant miRNAs and the biological role of these genes revealed different pathways that the dysregulated miRNAs are involved in each type of muscular dystrophy investigated. In conclusion, this study shows unique signatures of small RNAs circulating in five types of muscular dystrophy patients and provides a useful resource for future studies for the development of miRNA biomarkers in muscular dystrophies and for their involvement in the pathogenesis of the disorders.

Serum miRNAs as biomarkers for the rare types of muscular dystrophy.

Muscular dystrophies are a group of disorders that cause progressive muscle weakness. There is an increasing interest for the development of biomarkers for these disorders and specifically for Duchene Muscular Dystrophy. Limited research however, has been performed on the biomarkers' development for the most rare muscular dystrophies, like the Facioscapulohumeral Muscular Dystrophy, Limb-Girdle Muscular Dystrophy and Myotonic Dystrophy type 2. Here, we aimed to identify novel serum-based miRNA biomarkers for these rare muscular dystrophies, through high-throughput next-generation RNA sequencing. We identified many miRNAs that associate with muscular dystrophy patients compared to controls. Based on a series of selection criteria, the two best candidate miRNAs for each of these disorders were chosen and validated in a larger number of patients. Our results showed that miR-223-3p and miR-206 are promising serum-based biomarkers for Facioscapulohumeral Muscular Dystrophy type 1, miR-143-3p and miR-486-3p for Limb-Girdle Muscular Dystrophy type 2A whereas miR-363-3p and miR-25-3p associate with Myotonic Dystrophy type 2. Some of the identified miRNAs were significantly elevated in the serum of the patients compared to controls, whereas some others were lower. In conclusion, we provide new evidence that certain circulating miRNAs may be used as biomarkers for three types of rare muscular dystrophies.

Methylation status of hypothalamic Mkrn3 promoter across puberty.

Makorin RING finger protein 3 (MKRN3) is an important factor located on chromosome 15 in the imprinting region associated with Prader-Willi syndrome. Imprinted MKRN3 is expressed in hypothalamic regions essential for the onset of puberty and mutations in the gene have been found in patients with central precocious puberty. The pubertal process is largely controlled by epigenetic mechanisms that include, among other things, DNA methylation at CpG dinucleotides of puberty-related genes. In the present study, we investigated the methylation status of the Mkrn3 promoter in the hypothalamus of the female mouse before, during and after puberty. Initially, we mapped the 32 CpG dinucleotides in the promoter, the 5'UTR and the first 50 nucleotides of the coding region of the Mkrn3 gene. Moreover, we identified a short CpG island region (CpG islet) located within the promoter. Methylation analysis using bisulfite sequencing revealed that CpG dinucleotides were methylated regardless of developmental stage, with the lowest levels of methylation being found within the CpG islet region. In addition, the CpG islet region showed significantly lower methylation levels at the pre-pubertal stage when compared with the pubertal or post-pubertal stage. Finally, in silico analysis of transcription factor binding sites on the Mkrn3 CpG islet identified the recruitment of 29 transcriptional regulators of which 14 were transcriptional repressors. Our findings demonstrate the characterization and differential methylation of the CpG dinucleotides located in the Mkrn3 promoter that could influence the transcriptional activity in pre-pubertal compared to pubertal or post-pubertal period. Further studies are needed to clarify the possible mechanisms and effects of differential methylation of the Mkrn3 promoter.

Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective.

The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter ("Die" score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.

In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches.

BACKGROUND: This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS: We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. RESULTS: We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. CONCLUSIONS: Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.

miR-223-3p and miR-24-3p as novel serum-based biomarkers for myotonic dystrophy type 1.

Myotonic dystrophy type 1 (DM1) is the most common adult-onset muscular dystrophy, primarily characterized by muscle wasting and weakness. Many biomarkers already exist in the rapidly developing biomarker research field that aim to improve patients' care. Limited work, however, has been performed on rare diseases, including DM1. We have previously shown that specific microRNAs (miRNAs) can be used as potential biomarkers for DM1 progression. In this report, we aimed to identify novel serum-based biomarkers for DM1 through high-throughput next-generation sequencing. A number of miRNAs were identified that are able to distinguish DM1 patients from healthy individuals. Two miRNAs were selected, and their association with the disease was validated in a larger panel of patients. Further investigation of miR-223-3p, miR-24-3p, and the four previously identified miRNAs, miR-1-3p, miR-133a-3p, miR-133b-3p, and miR-206-3p, showed elevated levels in a DM1 mouse model for all six miRNAs circulating in the serum compared to healthy controls. Importantly, the levels of miR-223-3p, but not the other five miRNAs, were found to be significantly downregulated in five skeletal muscles and heart tissues of DM1 mice compared to controls. This result provides significant evidence for its involvement in disease manifestation.

Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.

Dysbiosis and Enhanced Beta-Defensin Production in Hair Follicles of Patients with Lichen Planopilaris and Frontal Fibrosing Alopecia.

Despite their distinct clinical manifestation, frontal fibrosing alopecia (FFA) and lichen planopilaris (LPP) display similar histopathologic features. Aberrant innate immune responses to endogenous or exogenous triggers have been discussed as factors that could drive inflammatory cascades and the collapse of the stem cell niche. In this exploratory study, we investigate the bacterial composition of scalp skin and plucked hair follicles (HF) of patients with FFA, LPP and alopecia areata circumscripta (AAc), as well as healthy individuals, in relation to cellular infiltrates and the expression of defense mediators. The most abundant genus in lesional and non-lesional HFs of LPP and FFA patients was Staphylococcus, while Lawsonella dominated in healthy individuals and in AAc patients. We observed statistically significant differences in the ratio of Firmicutes to Actinobacteria between healthy scalp, lesional, and non-lesional sites of FFA and LPP patients. This marked dysbiosis in FFA and LPP in compartments close to the bulge was associated with increased HßD1 and HßD2 expression along the HFs from lesional sites, while IL-17A was increased in lesional HF from AAc patients. The data encourage further studies on how exogenous factors and molecular interactions across the HF epithelium could contribute to disease onset and propagation.

Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains.

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington's Disease Status Based on Omics Data.

Huntington's disease is a rare neurodegenerative disease caused by a cytosine-adenine-guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington's disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-ß) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca2+) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca2+, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.

Analyzing Gene Expression Profiles from Ataxia and Spasticity Phenotypes to Reveal Spastic Ataxia Related Pathways.

Spastic ataxia (SA) is a group of rare neurodegenerative diseases, characterized by mixed features of generalized ataxia and spasticity. The pathogenetic mechanisms that drive the development of the majority of these diseases remain unclear, although a number of studies have highlighted the involvement of mitochondrial and lipid metabolism, as well as calcium signaling. Our group has previously published the GBA2 c.1780G > C (p.Asp594His) missense variant as the cause of spastic ataxia in a Cypriot consanguineous family, and more recently the biochemical characterization of this variant in patients' lymphoblastoid cell lines. GBA2 is a crucial enzyme of sphingolipid metabolism. However, it is unknown if GBA2 has additional functions and therefore additional pathways may be involved in the disease development. The current study introduces bioinformatics approaches to better understand the pathogenetic mechanisms of the disease. We analyzed publicly available human gene expression datasets of diseases presented with 'ataxia' or 'spasticity' in their clinical phenotype and we performed pathway analysis in order to: (a) search for candidate perturbed pathways of SA; and (b) evaluate the role of sphingolipid signaling pathway and sphingolipid metabolism in the disease development, through the identification of differentially expressed genes in patients compared to controls. Our results demonstrate consistent differential expression of genes that participate in the sphingolipid pathways and highlight alterations in the pathway level that might be associated with the disease phenotype. Through enrichment analysis, we discuss additional pathways that are connected to sphingolipid pathways, such as PI3K-Akt signaling, MAPK signaling, calcium signaling, and lipid and carbohydrate metabolism as the most enriched for ataxia and spasticity phenotypes.

Intestinal microbiome analysis demonstrates azithromycin post-treatment effects improve when combined with lactulose.

BACKGROUND: Next-generation sequencing has revolutionized our perspective on the gut microbiome composition, revealing the true extent of the adverse effects of antibiotics. The impact of antibiotic treatment on gut microbiota must be considered and researched to provide grounds for establishing new treatment strategies that are less devastating on commensal bacteria. This study investigates the impact on gut microbiome when a commonly used antibiotic, azithromycin is administered, as well as uncovers the benefits induced when it is used in combination with lactulose, a prebiotic known to enhance the proliferation of commensal microbes. METHODS: 16S rRNA gene sequencing analysis of stool samples obtained from 87 children treated with azithromycin in combination with or without lactulose have been determined. Children's gut microbial profile was established at the pre- and post-treatment stage. RESULTS: Azithromycin caused an increase in the relative abundance of opportunistic pathogens such as Streptococcus that was evident 60 days after treatment. While few days after treatment, children who also received lactulose started to show a higher relative abundance of saccharolytic bacteria such as Lactobacillus, Enterococcus, Anaerostipes, Blautia and Roseburia, providing a protective role against opportunistic pathogens. In addition, azithromycin-prebiotic combination was able to provide a phylogenetic profile more similar to the pre-treatment stage. CONCLUSION: It is suggested that during azithromycin treatment, lactulose is able to reinstate the microbiome equilibrium much faster as it promotes saccharolytic microbes and provides a homeostatic effect that minimizes the opportunistic pathogen colonization.