Identification of molecular signatures and pathways common to blood cells and brain tissue of amyotrophic lateral sclerosis patients

Epigenetic age acceleration is associated with occupational exposures, sex, and survival in amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is linked to ageing and genetic and environmental risk factors, yet underlying mechanisms are incompletely understood. We aimed to evaluate epigenetic age acceleration (EAA), i.e., DNA methylation (DNAm) age acceleration, and its association with ALS case status and survival.

METHODS

In this study, we included 428 ALS and 288 control samples collected between 2011 and 2021. We calculated EAA using the GrimAge residual method from ALS and control blood samples and grouped participants with ALS into three ageing groups (fast, normal, slow). We associated EAA with ALS case status and survival, stratified by sex, and correlated it with environmental and biological factors through occupational exposure assessments, immune cell proportions, and transcriptome changes.

FINDINGS

Participants with ALS had higher average EAA by 1.80 ± 0.30 years (p < 0.0001) versus controls. Participants with ALS in the fast ageing group had a hazard ratio of 1.52 (95% confidence interval 1.16-2.00, p = 0.0028) referenced to the normal ageing group. In males, this hazard ratio was 1.55 (95% confidence interval 1.11-2.17, p = 0.010), and EAA was positively correlated with high-risk occupational exposures including particulate matter (adj.p < 0.0001) and metals (adj.p = 0.0087). Also, in male participants with ALS, EAA was positively correlated with neutrophil proportions and was negatively correlated with CD4+ T cell proportions. Pathways dysregulated in participants with ALS with fast ageing included spliceosome, nucleocytoplasmic transport, axon guidance, and interferons.

INTERPRETATION

EAA was associated with ALS case status and, at least in males, with shorter survival after diagnosis. The effect of EAA on ALS was partially explained by occupational exposures and immune cell proportions in a sex-dependent manner. These findings highlight the complex interactions of ageing and exposures in ALS.

FUNDING

NIH, CDC/National ALS Registry, ALS Association, Dr. Randall Whitcomb Fund for ALS Genetics, Peter Clark Fund for ALS Research, Sinai Medical Staff Foundation, Scott L. Pranger ALS Clinic Fund, NeuroNetwork Therapeutic Discovery Fund, NeuroNetwork for Emerging Therapies.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (DUSP1, NKX2-5, GLI1, KLF4, SCNN1B, NPHS1, SGK2, PITX2, S100A4, and MSX1) were identified from protein-protein interaction (PPI) network which were mostly expressed as down-regulated. Following that, transcription factors (TFs)-DEGs interactions (FOXC1, GATA2, etc), TF-microRNA (miRNA) interactions (hsa-miR-340, hsa-miR-34a, etc), protein-drug interactions, and disorders associated with DEGs were predicted. Furthermore, we used gene set enrichment analysis (GSEA) to emphasize relevant gene ontology terms (eg, TF activity, sequence-specific DNA binding) linked to DEGs in HD. Disease interactions revealed the diseases that are linked to HD, and the prospective small drug molecules like cytarabine and arsenite was predicted against HD. This study reveals molecular biomarkers at the RNA and protein levels that may be beneficial to improve the understanding of molecular mechanisms, early diagnosis, as well as prospective pharmacologic targets for designing beneficial HD treatment.

Flounder fish (Paralichthys sp.) collagen a new tissue regeneration: genotoxicity, cytotoxicity and physical-chemistry characterization

Collagen dressings have been widely used as effective treatments for chronic wounds acting as barrier, protecting the area from infections and participating in the healing process. Collagen from fish skin is biocompatible, presents low immunogenicity and is able of stimulating wound healing. In this scenario, skin of flounder fish (Paralichthys sp.) may constitute a promising source for collagen. Then, our hypothesis is that fish collagen is able of increasing cell proliferation, with no cytotoxicity. In this context, the aim of the present study was to investigate the physicochemical and morphological properties of collagen using scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDS), mass loss and pH. Moreover, the cytotoxicity and genotoxicity of collagen were studied using in vitro studies (cell viability, comet assay and micronucleus assay). Fish collagen showed no variation of pH and mass weight, with characteristic peaks of collagen in FTIR. Furthermore, all the extracts presented cell viability at least over 50% and no cytotoxicity was observed. Regarding genotoxicity data, the results showed that only the extract of 100% showed higher values in comparison with negative control group for CHO-K1 cell line as depicted by comet and micronucleus assays. Based on the results, it is suggested that fish collagen is biocompatible and present non-cytotoxicity in the in vitro studies, being considered a suitable material for tissue engineering proposals.

Resveratrol, Endocrine Disrupting Chemicals, Neurodegenerative Diseases and Depression: Genes, Transcription Factors, microRNAs, and Sponges Involved

We aimed to examine the molecular basis of the positive effect of resveratrol against amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), cognitive impairment (CI), and depression induced by a mixture of bisphenol A (BPA), BPS, and BPF. The CTD, GeneMania, Metascape, SwissADME, Cytoscape, MIENTURNET, miRNAsong, and Autodock Vina were the fundamental tools for analysis. Resveratrol exerts its protective effects on selected diseases induced by a mixture of BPA, BPS, and BPF through the following genes: PTGS2 and GSR for ALS; INS, IL6, BDNF, and SOD1 for PD; BDNF, CASP3, TNF, INS, IGF1, IL1B for CI; and BDNF, PTGS2, and IL6 for depression. Detoxification was noted as the most important for ALS, dopamine metabolism for PD, apoptosis for CI, and the selenium micronutrient network for depression. hsa-miR-377-3p, hsa-miR-1-3p, hsa-miR-128-3p, and hsa-miR-204-5p were highlighted. We created and tested in silico sponges that inhibited these miRNAs. NFE2L2, BACH1, PPARG, and NR4A3 were listed as the key transcription factors implicated in resveratrol's protective effect against harmful studied chemicals. Furthermore, resveratrol's physicochemical properties and pharmacokinetics are consistent with its therapeutic benefits in ALS, PD, CI, and depression, owing to its high gastrointestinal absorption, drug-likeness, non-P-glycoprotein substrate, and capacity to penetrate the blood-brain barrier.

Identification of dysregulated canonical pathways associated with pathogenesis and progression of Amyotrophic Lateral Sclerosis-An integrated bioinformatics approach

The mechanisms responsible for the pathogenesis and progression of Amyotrophic Lateral Sclerosis (ALS) remain poorly understood, making the diagnosis of ALS challenging. We aimed to find the novel gene biomarkers via computationally analyzing microarray expression studies, in three different cell lineages, namely myotube cells, astrocyte cells and oligodendrocyte cells. Microarray gene expression profiles were obtained and analyzed for three cell types: myotube cell lineage (GSE122261), astrocyte, and oligodendrocyte cell lineage (GSE87385). A comprehensive computational pipeline, tailored explicitly for microarray gene expression profiling studies, was devised to analyze the sample groups, wherein the myotube sample group comprised of six control (GSM3462697, GSM3462698, GSM3462699, GSM3462700, GSM3462701, GSM3462702) & six diseased (GSM3462691, GSM3462692, GSM3462693, GSM3462694, GSM3462695, GSM3462696) samples were considered. Similarly, for the astrocyte sample group two samples each for the control (GSM2330040, GSM2330042) and the diseased (GSM2330039, GSM2330041), and for the oligodendrocyte sample group, 2 control (GSM2330043, GSM2330045) samples and two diseased (GSM2330044, GSM2330046) samples were considered for the current study. The in-depth interaction of these DEGs was studied using MCODE and subjected to preliminary functional analysis using ClueGO/CluePedia plug-in. Qiagen's IPA software was employed for enrichment analysis, which generated the key canonical pathways and a list of potential biomarker molecules specific to each sample group. The preliminary analysis yielded 512 DEGs across all 3-sample groups, wherein 139 DEGs belonged to the myotube sample group, 216 DEGs for the astrocyte sample group, and 157 DEGs for the oligodendrocytes sample group. The data suggests growth hormone signaling and its activity, ErbB signaling pathway, and JAK/STAT signaling pathway are some of the pathways that are significantly dysregulated and play a crucial role in the development and progression of ALS. KISS1R and CSHL1 are potential genes that could act as diagnostic biomarkers in myotube cell types. Also, KRAS, TGFB2, JUN, and SMAD6 genes may be used as prognostic biomarkers to differentiate between early and late-stage ALS-diseased patients.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Fish collagen for skin wound healing: a systematic review in experimental animal studies

Collagen extracted from fishes has been appearing as an alternative for commercial porcine and bovine collagen and it has been considered interesting especially for membrane manufacturing in tissue engineering. Despite the positive in vitro effects of fish collagen membranes, there is still no understanding of all the benefits that this natural biomaterial plays in the wound healing process, due to the lack of compilation of the results obtained in animal studies. In this sense, the purpose of this study was to perform a systematic review of the literature to examine the effects of fish collagen membranes for skin wound healing in experimental models of skin wound. The search was carried out according to the orientations of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and the descriptors of the Medical Subject Headings (MeSH) were defined: "fish," collagen," "skin," and "in vivo". A total of 10 articles were retrieved from the databases PubMed and Scopus. After the elegibility analyses, this review covers the different origins of fish collagen reported in the different papers from the beginning of 2015 through the middle of 2021. The results were based mainly on histological analysis and macroscopic evaluation, and fish skin collagen was responsible for improving the wound healing rate and the process of reepithelization and collagen deposition. In conclusion, fish skin collagen has shown positive results in in vivo studies and may be a potential biomaterial in tissue engineering.

Application of a bioinformatic pipeline to RNA-seq data identifies novel viruslike sequence in human blood

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

Collagen from Marine Sources and Skin Wound Healing in Animal Experimental Studies: a Systematic Review

Collagen (Col) from marine organisms has been emerging as an important alternative for commercial Col and it has been considered highly attractive by the industry. Despite the positive effects of Col from marine origin, there is still limited understanding of the effects of this natural biomaterial in the process of wound healing in animal studies. In this context, the purpose of this study was to perform a systematic review of the literature to examine the effects of Col from different marine species in the process of skin tissue healing using experimental models of skin wound. The search was carried out according to the orientations of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and the descriptors of the Medical Subject Headings (MeSH) were defined: "marine collagen," "spongin," "spongin," "skin," and "wound." A total of 42 articles were retrieved from the databases PubMed and Scopus. After the eligibility analyses, this review covers the different marine sources of Col reported in 10 different papers from the beginning of 2011 through the middle of 2019. The results were based mainly on histological analysis and it demonstrated that Col-based treatment resulted in a higher deposition of granulation tissue, stimulation of re-epitalization and neoangiogenesis and increased amount of Col of the wound, culminating in a more mature morphological aspect. In conclusion, this review demonstrates that marine Col from different species presented positive effects on the process of wound skin healing in experimental models used, demonstrating the huge potential of this biomaterial for tissue engineering proposals.

From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating and fatal neurodegenerative disorder, caused by the degeneration of upper and lower motor neurons for which there is no truly effective cure. The lack of successful treatments can be well explained by the complex and heterogeneous nature of ALS, with patients displaying widely distinct clinical features and progression patterns, and distinct molecular mechanisms underlying the phenotypic heterogeneity. Thus, stratifying ALS patients into consistent and clinically relevant subgroups can be of great value for the development of new precision diagnostics and targeted therapeutics for ALS patients. In the last years, the use and integration of high-throughput "omics" approaches have dramatically changed our thinking about ALS, improving our understanding of the complex molecular architecture of ALS, distinguishing distinct patient subtypes and providing a rational foundation for the discovery of biomarkers and new individualized treatments. In this review, we discuss the most significant contributions of omics technologies in unraveling the biological heterogeneity of ALS, highlighting how these approaches are revealing diagnostic, prognostic and therapeutic targets for future personalized interventions.

A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue

Identification of genes whose regulation of expression is functionally similar in both brain tissue and blood cells could in principle enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of gene expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinson's Disease (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology approaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain tissue and blood cells by examining cell pathway commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may, with clinical validation, enable prediction of neurological dysfunction using blood cell transcript profiling.