Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers

MicroRNAs as Regulators, Biomarkers, and Therapeutic Targets in Autism Spectrum Disorder

The pathogenesis of autism spectrum disorder (ASD) is complex and is mainly influenced by genetic and environmental factors. Some research has indicated that environmental aspects may interplay with genetic aspects to enhance the risk, and microRNAs (miRNAs) are probably factors in explaining this link between heredity and the environment. MiRNAs are single-stranded noncoding RNAs that can regulate gene expression at the posttranscriptional level. Some research has indicated that miRNAs are closely linked to neurological diseases. Many aberrantly expressed miRNAs have been observed in autism, and these dysregulated miRNAs are expected to be potential biomarkers and provide new strategies for the treatment of this disease. This article reviews the research progress of miRNAs in autism, including their biosynthesis and function. It is found that some miRNAs show aberrant expression patterns in brain tissue and peripheral blood of autistic patients, which may serve as biomarkers of the disease. In addition, the article explores the novel role of exosomes as carriers of miRNAs with the ability to cross the blood-brain barrier and unique expression profiles, offering new possibilities for diagnostic and therapeutic interventions in ASD. The potential of miRNAs in exosomes as diagnostic markers for ASD is specifically highlighted, as well as the prospect of using engineered exosome-encapsulated miRNAs for targeted therapies.

Modelling cell type-specific lncRNA regulatory network in autism with Cycle

BACKGROUND

Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types.

RESULTS

Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation.

CONCLUSION

Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.

Exploring key genes and pathways associated with sex differences in autism spectrum disorder: integrated bioinformatic analysis

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder marked by functional abnormalities in brain that causes social and linguistic difficulties. The incidence of ASD is more prevalent in males compared to females, but the underlying mechanism, as well as molecular indications for identifying sex-specific differences in ASD symptoms remain unknown. Thus, impacting the development of personalized strategy towards pharmacotherapy of ASD. The current study employs an integrated bioinformatic approach to investigate the genes and pathways uniquely associated with sex specific differences in autistic individuals. Based on microarray dataset (GSE6575) extracted from the gene expression omnibus, the dysregulated genes between the autistic and the neurotypical individuals for both sexes were identified. Gene set enrichment analysis was performed to ascertain biological activities linked to the dysregulated genes. Protein-protein interaction network analysis was carried out to identify hub genes. The identified hub genes were examined to determine their functions and involvement in the associated pathways using Enrichr. Additionally, hub genes were validated from autism-associated databases and the potential small molecules targeting the hub genes were identified. The present study utilized whole blood transcriptomic gene expression analysis data and identified 2211 and 958 differentially expressed unique genes in males and females respectively. The functional enrichment analysis revealed that male hub genes were functionally associated with RNA polymerase II mediated transcriptional regulation whereas female hub genes were involved in intracellular signal transduction and cell migration. The top male hub genes exhibited functional enrichment in tyrosine kinase signalling pathway. The pathway enrichment analysis of male hub genes indicates the enrichment of papillomavirus infection. Female hub genes were enriched in androgen receptor signalling pathway and functionally enriched in focal adhesion specific excision repair. Identified drug like candidates targeting these genes may serve as a potential sex specific therapeutics. Wortmannin for males, 5-Fluorouracil for females had the highest scores. Targeted and sex-specific pharmacotherapies may be created for the management of ASD. The current investigation identifies sex-specific molecular signatures derived from whole blood which may serve as a potential peripheral sex-specific biomarkers for ASD. The study also uncovers the possible pharmacological interventions against the selected genes/pathway, providing support in development of therapeutic strategies to mitigate ASD. However, experimental proofs on biological systems are warranted.

Fetal Brain-Derived Exosomal miRNAs from Maternal Blood: Potential Diagnostic Biomarkers for Fetal Alcohol Spectrum Disorders (FASDs)

Fetal alcohol spectrum disorders (FASDs) are leading causes of neurodevelopmental disability but cannot be diagnosed early in utero. Because several microRNAs (miRNAs) are implicated in other neurological and neurodevelopmental disorders, the effects of EtOH exposure on the expression of these miRNAs and their target genes and pathways were assessed. In women who drank alcohol (EtOH) during pregnancy and non-drinking controls, matched individually for fetal sex and gestational age, the levels of miRNAs in fetal brain-derived exosomes (FB-Es) isolated from the mothers' serum correlated well with the contents of the corresponding fetal brain tissues obtained after voluntary pregnancy termination. In six EtOH-exposed cases and six matched controls, the levels of fetal brain and maternal serum miRNAs were quantified on the array by qRT-PCR. In FB-Es from 10 EtOH-exposed cases and 10 controls, selected miRNAs were quantified by ddPCR. Protein levels were quantified by ELISA. There were significant EtOH-associated reductions in the expression of several miRNAs, including miR-9 and its downstream neuronal targets BDNF, REST, Synapsin, and Sonic hedgehog. In 20 paired cases, reductions in FB-E miR-9 levels correlated strongly with reductions in fetal eye diameter, a prominent feature of FASDs. Thus, FB-E miR-9 levels might serve as a biomarker to predict FASDs in at-risk fetuses.

Spatiotemporal expression and coexpression patterns of SRPK1 in the human brain: A neurodevelopmental perspective

BACKGROUND

SRPK1 is a splicing-related protein that plays an important role in the development and function of the human brain. This article presents evidence that SRPK1 has distinct spatiotemporal expression patterns enriched in processes related to neurodevelopmental disorders across development.

MATERIAL AND METHOD

We used the BrainSpan growing mammalian brain transcriptome to evaluate the distribution of SRPK1 throughout the entire brain. RNA-sequencing data were gathered from 524 tissue samples recovered from 41 postmortem brains of physiologically normal individuals spanning early developing fetus (8 postconception weeks, PCW) to later life (40 years of age). Using the Allen Human Brain Atlas (AHBA) dataset, we analyzed the spatial gene expression of 15 adult human brains. Using Toppgene, we identified genes that exhibit significant coexpression with SRPK1.

RESULTS

We found evidence that analyzing the spatiotemporal gene expression profile and identifying coexpressed genes reveals that SRPK1 expression is involved in various neurodevelopmental and somatic events throughout the lifetime.

CONCLUSION

Our findings highlight the importance of detailed maps of gene expression in the human brain for improved human-to-human translation and illustrate differences in SRPK1 expression across anatomical areas and developmental stages in healthy human brain tissue.

Biomarker prediction in autism spectrum disorder using a network-based approach

BACKGROUND

Autism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery.

METHODS

The first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein-protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA-miRNA network, consisting of the selected genes and corresponding miRNA regulators.

RESULTS

In the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein-protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA-miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis.

CONCLUSION

This study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.

Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder

Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.

iPSC-Derived Macrophages: The Differentiation Protocol Affects Cell Immune Characteristics and Differentiation Trajectories

The generation of human macrophages from induced pluripotent stem cells (iMacs) is a rapidly developing approach used to create disease models, screen drugs, study macrophage-pathogen interactions and develop macrophage-based cell therapy. To generate iMacs, different types of protocols have been suggested, all thought to result in the generation of similar iMac populations. However, direct comparison of iMacs generated using different protocols has not been performed. We have compared the productivity, the differentiation trajectories and the characteristics of iMacs generated using two widely used protocols: one based on the formation of embryoid bodies and the induction of myeloid differentiation by only two cytokines, interleukin-3 and macrophage colony-stimulating factor, and the other utilizing multiple exogenous factors for iMac generation. We report inter-protocol differences in the following: (i) protocol productivity; (ii) dynamic changes in the expression of genes related to inflammation and lipid homeostasis following iMac differentiation and (iii) the transcriptomic profiles of terminally differentiated iMacs, including the expression of genes involved in inflammatory response, antigen presentation and lipid homeostasis. The results document the dependence of fine iMac characteristics on the type of differentiation protocol, which is important for further development of the field, including the development of iMac-based cell therapy.

Potential of Salivary Biomarkers in Autism Research: A Systematic Review

The diagnostic process for autism spectrum disorders (ASD) is based on a behavioral analysis of the suspected individual. Despite intensive research, no specific and valid biomarker has been identified for ASD, but saliva, with its advantages such as non-invasive collection, could serve as a suitable alternative to other body fluids. As a source of nucleic acid of both human and microbial origin, protein and non-protein molecules, saliva offers a complex view on the current state of the organism. Additionally, the use of salivary markers seems to be less complicated not only for ASD screening but also for revealing the etiopathogenesis of ASD, since enrolling neurotypical counterparts willing to participate in studies may be more feasible. The aim of the presented review is to provide an overview of the current research performed on saliva in relation to ASD, mutual complementing, and discrepancies that result in difficulties applying the observed markers in clinical practice. We emphasize the methodological limitations of saliva collection and processing as well as the lack of information regarding ASD diagnosis, which is critically discussed.

Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning

As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose individuals with ASD merely based on clinical observations. Without additional support of biochemical markers, the difficulty of diagnosis could impact therapeutic decisions and, therefore, lead to delayed treatments. Recently, accumulating evidence have shown that both genetic abnormalities and chemical toxicants play important roles in the onset of ASD. In this work, a new multilabel classification (MLC) model is proposed to identify the autistic risk genes and toxic chemicals on a large-scale data set. We first construct the feature matrices and partially labeled networks for autistic risk genes and toxic chemicals from multiple heterogeneous biological databases. Based on both global and local measure metrics, the simulation experiments demonstrate that the proposed model achieves superior classification performance in comparison with the other state-of-the-art MLC methods. Through manual validation with existing studies, 60% and 50% out of the top-20 predicted risk genes are confirmed to have associations with ASD and autistic disorder, respectively. To the best of our knowledge, this is the first computational tool to identify ASD-related risk genes and toxic chemicals, which could lead to better therapeutic decisions of ASD.

Cataloging recent advances in epigenetic alterations in major mental disorders and autism

During the last two decades, diverse epigenetic modifications including DNA methylation, histone modifications, RNA editing and miRNA dysregulation have been associated with psychiatric disorders. A few years ago, in a review we outlined the most common epigenetic alterations in major psychiatric disorders (e.g., aberrant DNA methylation of DTNBP1, HTR2A, RELN, MB-COMT and PPP3CC, and increased expression of miR-34a and miR-181b). Recent follow-up studies have uncovered other DNA methylation aberrations affecting several genes in mental disorders, in addition to dysregulation of many miRNAs. Here, we provide an update on new epigenetic findings and highlight potential origin of the diversity and inconsistencies, focusing on drug effects, tissue/cell specificity of epigenetic landscape and discuss shortcomings of the current diagnostic criteria in mental disorders.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

A white paper on a neurodevelopmental framework for drug discovery in autism and other neurodevelopmental disorders

In the last decade there has been a revolution in terms of genetic findings in neurodevelopmental disorders (NDDs), with many discoveries critical for understanding their aetiology and pathophysiology. Clinical trials in single-gene disorders such as fragile X syndrome highlight the challenges of investigating new drug targets in NDDs. Incorporating a developmental perspective into the process of drug development for NDDs could help to overcome some of the current difficulties in identifying and testing new treatments. This paper provides a summary of the proceedings of the 'New Frontiers Meeting' on neurodevelopmental disorders organised by the European College of Neuropsychopharmacology in conjunction with the Innovative Medicines Initiative-sponsored AIMS-2-TRIALS consortium. It brought together experts in developmental genetics, autism, NDDs, and clinical trials from academia and industry, regulators, patient and family associations, and other stakeholders. The meeting sought to provide a platform for focused communication on scientific insights, challenges, and methodologies that might be applicable to the development of CNS treatments from a neurodevelopmental perspective. Multidisciplinary translational consortia to develop basic and clinical research in parallel could be pivotal to advance knowledge in the field. Although implementation of clinical trials for NDDs in paediatric populations is widely acknowledged as essential, safety concerns should guide each aspect of their design. Industry and academia should join forces to improve knowledge of the biology of brain development, identify the optimal timing of interventions, and translate these findings into new drugs, allowing for the needs of users and families, with support from regulatory agencies.

ExoBCD: a comprehensive database for exosomal biomarker discovery in breast cancer

Effective and safe implementation of precision oncology for breast cancer is a vital strategy to improve patient outcomes, which relies on the application of reliable biomarkers. As 'liquid biopsy' and novel resource for biomarkers, exosomes provide a promising avenue for the diagnosis and treatment of breast cancer. Although several exosome-related databases have been developed, there is still lacking of an integrated database for exosome-based biomarker discovery. To this end, a comprehensive database ExoBCD (https://exobcd.liumwei.org) was constructed with the combination of robust analysis of four high-throughput datasets, transcriptome validation of 1191 TCGA cases and manual mining of 950 studies. In ExoBCD, approximately 20 900 annotation entries were integrated from 25 external sources and 306 exosomal molecules (49 potential biomarkers and 257 biologically interesting molecules). The latter could be divided into 3 molecule types, including 121 mRNAs, 172 miRNAs and 13 lncRNAs. Thus, the well-linked information about molecular characters, experimental biology, gene expression patterns, overall survival, functional evidence, tumour stage and clinical use were fully integrated. As a data-driven and literature-based paradigm proposed of biomarker discovery, this study also demonstrated the corroborative analysis and identified 36 promising molecules, as well as the most promising prognostic biomarkers, IGF1R and FRS2. Taken together, ExoBCD is the first well-corroborated knowledge base for exosomal studies of breast cancer. It not only lays a foundation for subsequent studies but also strengthens the studies of probing molecular mechanisms, discovering biomarkers and developing meaningful clinical use.

The fourth scientific discovery paradigm for precision medicine and healthcare: Challenges ahead

With the progression of modern information techniques, such as next generation sequencing (NGS), Internet of Everything (IoE) based smart sensors, and artificial intelligence algorithms, data-intensive research and applications are emerging as the fourth paradigm for scientific discovery. However, we face many challenges to practical application of this paradigm. In this article, 10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.

Neuroprotective Effect of α-Mangostin in the Ameliorating Propionic Acid-Induced Experimental Model of Autism in Wistar Rats

Several studies have documented the role of hyper-activation of extracellular signal-regulated kinases (ERK) in Autism pathogenesis. Alpha-mangostin (AMG) is a phytoconstituents with anti-oxidants, anti-inflammatory, and ERK inhibition properties in many diseases. Our research aims to investigate the neuroprotective effect of AMG in the rat model of intracerebroventricular-propionic acid (ICV-PPA) induced autism with a confirmation of its effect on the ERK signaling. Autism was induced in Wistar rats (total 36 rats; 18 male/18 female) by multiple doses of PPA through ICV injection for 11 days. Actophotometer and beam walking tasks were used to evaluate animals' motor abilities, and the Morris water maze task was utilized to confirm the cognition and memory in animals. Long term administration of AMG 100 mg/kg and AMG 200mg/kg continued from day 12 to day 44 of the experiment. Before that, animals were sacrificed, brains isolated, morphological, gross pathological studies were performed, and neurochemical analysis was performed in the brain homogenates. Cellular and molecular markers, including ERK, myelin basic protein, apoptotic markers including caspase-3, Bax, Bcl-2, neuroinflammatory markers, neurotransmitters, and oxidative stress markers, have been tested throughout the brain. Thus, AMG reduces the overactivation of the ERK signaling and also restored autism-like behavioral and neurochemical alterations.

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.

Detection of disease-associated microRNAs - application for autism spectrum disorders

Autism spectrum disorders (ASD) diagnostic procedure still lacks a uniform biological marker. This review gathers the information on microRNAs (miRNAs) specifically as a possible source of biomarkers of ASD. Extracellular vesicles, and their subset of exosomes, are believed to be a tool of cell-to-cell communication, and they are increasingly considered to be carriers of such a marker. The interest in studying miRNAs in extracellular vesicles grows in all fields of study and therefore should not be omitted in the field of neurodevelopmental disorders. The summary of miRNAs associated with brain cells and ASD either studied directly in the tissue or biofluids are gathered in this review. The heterogeneity in findings from different studies points out the fact that unified methods should be established, beginning with the determination of the accurate patient and control groups, through to sample collection, processing, and storage conditions. This review, based on the available literature, proposes the standardized approach to obtain the results that would not be affected by technical factors. Nowadays, the method of high-throughput sequencing seems to be the most optimal to analyze miRNAs. This should be followed by the uniformed bioinformatics procedure to avoid misvalidation. At the end, the proper validation of the obtained results is needed. With such an approach as is described in this review, it would be possible to obtain a reliable biomarker that would characterize the presence of ASD.

iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data

The latest advances in the next generation sequencing technology have greatly facilitated the extensive research of genomics and transcriptomics, thereby promoting the decoding of carcinogenesis with unprecedented resolution. Considering the contribution of analyzing high-throughput multi-omics data to the exploration of cancer molecular mechanisms, an integrated tool for heterogeneous multi-omics data analysis (iODA) is proposed for the systems-level interpretation of multi-omics data, i.e., transcriptomic profiles (mRNA or miRNA expression data) and protein-DNA interactions (ChIP-Seq data). Considering the data heterogeneity, iODA can compare six statistical algorithms in differential analysis for the selected sample data and assist users in choosing the globally optimal one for dysfunctional mRNA or miRNA identification. Since molecular signatures are more consistent at the pathway level than at the gene level, the tool is able to enrich the identified dysfunctional molecules onto the KEGG pathways and extracted the consistent items as key components for further pathogenesis investigation. Compared with other tools, iODA is multi-functional for the systematic analysis of different level of omics data, and its analytical power was demonstrated through case studies of single and cross-level prostate cancer omics data. iODA is open source under GNU GPL and can be downloaded from http://www.sysbio.org.cn/iODA.

An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD. Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many genes and many other factors are associated with ASD, various bioinformatics methods have also been developed for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants and gene expression data, and (ii) methods using biological networks, which include gene co-expression networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate on the computational techniques used. We also briefly review other approaches and discuss possible future directions and strategies for developing bioinformatics approaches to analyze ASD.

Identification of microRNA-451a as a Novel Circulating Biomarker for Colorectal Cancer Diagnosis

Background

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Successful treatment of CRC relies on accurate early diagnosis, which is currently a challenge due to its complexity and personalized pathologies. Thus, novel molecular biomarkers are needed for early CRC detection.

Methods

Gene and microRNA microarray profiling of CRC tissues and miRNA-seq data were analyzed. Candidate microRNA biomarkers were predicted using both CRC-specific network and miRNA-BD tool. Validation analyses were carried out to interrogate the identified candidate CRC biomarkers.

Results

We identified miR-451a as a potential early CRC biomarker circulating in patient's serum. The dysregulation of miR-451a was revealed both in primary tumors and in patients' sera. Downstream analysis validated the tumor suppressor role of miR-451a and high sensitivity of miR-451a in CRC patients, further confirming its potential role as CRC circulation biomarker.

Conclusion

The miR-451a is a potential circulating biomarker for early CRC diagnosis.

Identification of miRNA-mRNA Network in Autism Spectrum Disorder Using a Bioinformatics Method

Autism spectrum disorder (ASD) includes a heterogeneous group of disorders with different contributing genetics and epigenetics factors. Aberrant expression of miRNAs has been detected in ASD children compared with normally developed children. Due to the heterogeneity of this disorder, there is no consensus on ASD-associated miRNAs; thus, it is necessary to develop a model for comprehensive assessment of the role of miRNAs in ASD. We interrogated the PubMed, Google Scholar, and Web of Science databases until the end of 2019 to identify ASD-associated miRNAs. In addition, mRNA-coding genes that contribute to the pathogenesis of ASD were downloaded from the SFARI GENE ( https://gene.sfari.org/ ). The obtained 201 miRNAs and 478 target mRNAs were imported into the Cytoscape software suite to construct a miRNA-mRNA network. A protein-protein interaction network was constructed for target mRNAs using the CluPedia program in Cytoscape. Using this approach, we detected five modules that were associated with neurexins and neuroligins, glutamatergic synapse, cell adhesion molecules, NOTCH, MECP2 and circadian clock pathways, L1CAM interactions, and neurotransmitter release cycle. Taken together, functional analysis of these genes led to determination of critical pathways related to CNS disorders. Thus, the suggested approach in the current study resulted in the identification of the most relevant pathways in the pathogenesis of ASD that can be used as biomarkers or therapeutic targets.

Serum proteins differentially expressed in early- and late-onset preeclampsia assessed using iTRAQ proteomics and bioinformatics analyses

BACKGROUND

Preeclampsia remains a serious disorder that puts at risk the lives of perinatal mothers and infants worldwide. This study assessed potential pathogenic mechanisms underlying preeclampsia by investigating differentially expressed proteins (DEPs) in the serum of patients with early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) compared with healthy pregnant women.

METHODS

Blood samples were collected from four women with EOPE, four women with LOPE, and eight women with normal pregnancies, with four women providing control samples for each preeclampsia group. Serum proteins were identified by isobaric tags for relative and absolute quantitation combined with liquid chromatography-tandem mass spectrometry. Serum proteins with differences in their levels compared with control groups of at least 1.2 fold-changes and that were also statistically significantly different between the groups at P < 0.05 were further analyzed. Bioinformatics analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses, were used to determine the key proteins and signaling pathways associated with the development of PE and to determine those DEPs that differed between women with EOPE and those with LOPE. Key protein identified by mass spectrometry was verified by enzyme linked immunosorbent assay (ELISA).

RESULTS

Compared with serum samples from healthy pregnant women, those from women with EOPE displayed 70 proteins that were differentially expressed with significance. Among them, 51 proteins were significantly upregulated and 19 proteins were significantly downregulated. In serum samples from women with LOPE, 24 DEPs were identified , with 10 proteins significantly upregulated and 14 proteins significantly downregulated compared with healthy pregnant women. Bioinformatics analyses indicated that DEPs in both the EOPE and LOPE groups were associated with abnormalities in the activation of the coagulation cascade and complement system as well as with lipid metabolism. In addition, 19 DEPs in the EOPE group were closely related to placental development or invasion of tumor cells. Downregulationof pregnancy-specific beta-1-glycoprotein 9 (PSG9) in the LOPE group was confirmed by ELISA.

CONCLUSION

The pathogenesis of EOPE and LOPE appeared to be associated with coagulation cascade activation, lipid metabolism, and complement activation. However, the pathogenesis of EOPE also involved processes associated with greater placental injury. This study provided several new proteins in the serum which may be valuable for clinical diagnosis of EOPE and LOPE, and offered potential mechanisms underpinning the development of these disorders.

Recent Progress on Relevant microRNAs in Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose pathogenesis is unclear and is affected by both genetic and environmental factors. The microRNAs (miRNAs) are a kind of single-stranded non-coding RNA with 20-22 nucleotides, which normally inhibit their target mRNAs at a post-transcriptional level. miRNAs are involved in almost all biological processes and are closely related to ASD and many other diseases. In this review, we summarize relevant miRNAs in ASD, and analyze dysregulated miRNAs in brain tissues and body fluids of ASD patients, which may contribute to the pathogenesis and diagnosis of ASD.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

Systems biology comprehensive analysis on breast cancer for identification of key gene modules and genes associated with TNM-based clinical stages

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein-protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan-Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan-Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Neuroprotective potential of solanesol in intracerebroventricular propionic acid induced experimental model of autism: Insights from behavioral and biochemical evidence

Autism is the category used within the newest edition of the diagnostic and statistical manual of neurodevelopmental disorders. Autism is a spectrum of disorder where a variety of behavioural patterns observed in autistic patients, such as stereotypes and repetitive behavior, hyperexcitability, depression-like symptoms, and memory and cognitive dysfunctions. Neuropathological hallmarks that associated with autism are mitochondrial dysfunction, oxidative stress, neuroinflammation, Neuro-excitation, abnormal synapse formation, overexpression of glial cells in specific brain regions like cerebellum, cerebral cortex, amygdala, and hippocampus. ICV injection of propionic acid (PPA) (4 μl/0.26 M) mimics autistic-like behavioral and biochemical alterations in rats. Literature findings reveal that there is a link between autism neuronal mitochondrial coenzyme-Q10 (CoQ10) and ETC-complexes dysfunctions are the keys pathogenic events for autism. Therefore, in the current study, we explore the neuroprotective interventions of Solanesol (SNL) 40 and 60 mg/kg alone and in combination with standard drugs Aripiprazole (ARP) 5 mg/kg, Citalopram (CTP) 10 mg/kg, Memantine (MEM) 5 mg/kg and Donepezil (DNP) 3 mg/kg to overcome behavioral and biochemical alterations in PPA induced experimental model of Autism. Chronic treatment with SNL 60 mg/kg in combination with standard drug shows a marked improvement in locomotion, muscle coordination, long-term memory and the decrease in depressive behavior. While, chronic treatment of SNL alone and in combination with standard drug aripiprazole, citalopram, donepezil, and memantine shows the Neuroprotective potential by enhancing the cognitive deficits, biochemical alterations along with reducing the level of inflammatory mediators and oxidative stress.

Network vulnerability-based and knowledge-guided identification of microRNA biomarkers indicating platinum resistance in high-grade serous ovarian cancer

Background

High-grade serous ovarian cancer (HGSC), the most common ovarian carcinoma type, is associated with the highest mortality rate among all gynecological malignancies. As chemoresistance has been demonstrated as the major challenge in improving the prognosis of HGSC patients, we here aimed to identify microRNA (miRNA) biomarkers for predicting platinum resistance and further explore their functions in HGSC.

Results

We developed and applied our network vulnerability-based and knowledge-guided bioinformatics model first time for the study of drug-resistance in cancer. Four miRNA biomarkers (miR-454-3p, miR-98-5p, miR-183-5p and miR-22-3p) were identified with potential in stratifying platinum-sensitive and platinum-resistant HGSC patients and predicting prognostic outcome. Among them, miR-454-3p and miR-183-5p were newly discovered to be closely implicated in platinum resistance in HGSC. Functional analyses highlighted crucial roles of the four miRNA biomarkers in platinum resistance through mediating transcriptional regulation, cell proliferation and apoptosis. Moreover, expression patterns of the miRNA biomarkers were validated in both platinum-sensitive and platinum-resistant ovarian cancer cells.

Conclusions

With bioinformatics modeling and analysis, we identified and confirmed four novel putative miRNA biomarkers, miR-454-3p, miR-98-5p, miR-183-5p and miR-22-3p that could serve as indicators of resistance to platinum-based chemotherapy, thereby contributing to the improvement of chemotherapeutic efficiency and optimization of personalized treatments in HGSC.

Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.

MicroRNA-153 promotes brain-derived neurotrophic factor and hippocampal neuron proliferation to alleviate autism symptoms through inhibition of JAK-STAT pathway by LEPR

Autism is known as a severe neurobehavioral syndrome, with males affected more often than females. Previous studies have revealed that microRNAs (miRNAs) play a critical role in the search for novel therapeutic strategies for autism. Therefore, we evaluate the ability of miR-153 to influence brain-derived neurotrophic factor (BDNF) of autism as well as proliferation and apoptosis of hippocampal neuron through the janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway by targeting leptin receptor (LEPR). Firstly, the autistic mice models were established and Morris water maze was employed for the analysis of the learning ability and memory of the mice. Besides, in vitro experiments were conducted with the transfection of different mimic, inhibitor, or siRNA into the hippocampal neuron cells, after which the effect of miR-153 on LEPR and the JAK-STAT signaling pathway-related factors was investigated. Next, 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide assay and flow cytometry assay were conducted to evaluate cell proliferation, cell cycle, and apoptosis respectively following transfection. The results revealed that there was a significant decrease in learning ability and memory in the autistic mice along with a reduction in the positive expression rate of BDNF and serious inflammatory reaction. LEPR was confirmed as a target gene of miR-153 by the dual luciferase reporter gene assay. After transfection of overexpressed miR-153, LEPR and the JAK-STAT signaling pathway were inhibited followed by an increase in BDNF and enhancement of cell proliferation. In conclusion, the high expression of miR-153 can inhibit activation of JAK-STAT signaling pathway by LEPR, thus improving BDNF expression and the proliferative ability of hippocampal neurons.

From bedside to bench and back: Translating ASD models

Autism spectrum disorders (ASD) represent a heterogeneous group of disorders defined by deficits in social interaction/communication and restricted interests, behaviors, or activities. Models of ASD, developed based on clinical data and observations, are used in basic science, the "bench," to better understand the pathophysiology of ASD and provide therapeutic options for patients in the clinic, the "bedside." Translational medicine creates a bridge between the bench and bedside that allows for clinical and basic science discoveries to challenge one another to improve the opportunities to bring novel therapies to patients. From the clinical side, biomarker work is expanding our understanding of possible mechanisms of ASD through measures of behavior, genetics, imaging modalities, and serum markers. These biomarkers could help to subclassify patients with ASD in order to better target treatments to a more homogeneous groups of patients most likely to respond to a candidate therapy. In turn, basic science has been responding to developments in clinical evaluation by improving bench models to mechanistically and phenotypically recapitulate the ASD phenotypes observed in clinic. While genetic models are identifying novel therapeutics targets at the bench, the clinical efforts are making progress by defining better outcome measures that are most representative of meaningful patient responses. In this review, we discuss some of these challenges in translational research in ASD and strategies for the bench and bedside to bridge the gap to achieve better benefits to patients.

CBD: a biomarker database for colorectal cancer

Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.

Database URL: http://sysbio.suda.edu.cn/CBD/

Biomarkers for detection, prognosis and therapeutic assessment of neurological disorders

Neurological disorders have aroused a significant concern among the health scientists globally, as diseases such as Parkinson's, Alzheimer's and dementia lead to disability and people have to live with them throughout the life. Recent evidence suggests that a number of environmental chemicals such as pesticides (paraquat) and metals (lead and aluminum) are also the cause of these diseases and other neurological disorders. Biomarkers can help in detecting the disorder at the preclinical stage, progression of the disease and key metabolomic alterations permitting identification of potential targets for intervention. A number of biomarkers have been proposed for some neurological disorders based on laboratory and clinical studies. In silico approaches have also been used by some investigators. Yet the ideal biomarker, which can help in early detection and follow-up on treatment and identifying the susceptible populations, is not available. An attempt has therefore been made to review the recent advancements of in silico approaches for discovery of biomarkers and their validation. In silico techniques implemented with multi-omics approaches have potential to provide a fast and accurate approach to identify novel biomarkers.

Translational Bioinformatics for Cholangiocarcinoma: Opportunities and Challenges

Translational bioinformatics is becoming a driven force and a new scientific paradigm for cancer research in the era of big data. To promote the cross-disciplinary communication and research, we take cholangiocarcinoma as an example to review the present status and the future perspectives of the bioinformatics models applied in cancer study. We first summarize the present application of computational methods to the study of cholangiocarcinoma ranged from pattern recognition of biological data, knowledge based data annotation to systems biological level modeling and clinical translation. Then the future opportunities and challenges about database or knowledge base building, novel model developing and molecular mechanism exploring as well as the intelligent decision supporting system construction for the precision diagnosis, prognosis and treatment of cholangiocarcinoma are discussed.

MiR-375: A prospective regulator in medullary thyroid cancer based on microarray data and bioinformatics analyses

BACKGROUND

This research aims to investigate the prospective molecular mechanism of miR-375 in Medullary Thyroid Cancer (MTC).

MATERIAL AND METHODS

The expression level of miR-375 in MTC was explored with microarray data from Gene Expression Omnibus (GEO). To gather the putative target genes of miR-375, we selected eligible datasets in GEO, in which antagomir-375 and premir-375 were transfected to provide the miR-375-related genes. Subsequently, we attained the intersection of the results of GEO microarray data and 12 online target genes prediction database as the prospective target genes. Furthermore, we conducted in silico analysis including gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways annotations and Protein-Protein Interactions (PPI) analysis to provide an overview of the function of miR-375 in MTC. Finally, data from The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) were used for a validation.

RESULTS

Up-regulation could be confirmed with the data from GSE40807. GEO dataset GSE67742 provided 10,596 miR-375-related genes, while 12 online prediction databases showed that 3352 target genes appeared no less than four times. Finally, the intersection of the two groups of genes included 1132 prospective targets. In aspect of functional annotation, negative regulation of transcription from RNA polymerase II promoter (P=9.83E-06), golgi membrane (P=9.98E-05) and pathway of protein binding (P=3.63E-07) were highlighted as the most enriched terms with GO analysis. With regards to PPI network, 162 hub genes that interacted with no less than 10 other different genes was visualized, among which PI3K/Akt signaling pathway was the most enriched pathway as assessed by KEGG. Furthermore, two genes (JAK2 and NGFR) in PI3K/Akt signaling pathway showed down-regulated patterns in both mRNA and protein levels.

CONCLUSION

The higher expression level of miR-375 might play a pivotal role in the tumorigenesis of MTC via targeting multiple key pathways, especially PI3K/Akt pathway. However, the exact molecular mechanism of miR-375 needs to be verified with in-depth investigation in the future.

Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis

Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms.

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis

Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application.

Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare

The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.

Systems Health: A Transition from Disease Management Toward Health Promotion

To date, most of the chronic diseases such as cancer, cardiovascular disease, and diabetes, are the leading cause of death. Current strategies toward disease treatment, e.g., risk prediction and target therapy, still have limitations for precision medicine due to the dynamic and complex nature of health. Interactions among genetics, lifestyle, and surrounding environments have nonnegligible effects on disease evolution. Thus a transition in health-care area is urgently needed to address the hysteresis of diagnosis and stabilize the increasing health-care costs. In this chapter, we explored new insights in the field of health promotion and introduced the integration of systems theories with health science and clinical practice. On the basis of systems biology and systems medicine, a novel concept called "systems health" was comprehensively advocated. Two types of bioinformatics models, i.e., causal loop diagram and quantitative model, were selected as examples for further illumination. Translational applications of these models in systems health were sequentially discussed. Moreover, we highlighted the bridging of ancient and modern views toward health and put forward a proposition for citizen science and citizen empowerment in health promotion.