The Database of Biomarkers: MicroRNA Biomarkers – Psychiatric Disorders

The Database of Biomarkers: MicroRNA Biomarkers – Psychiatric Disorders

•    Associated diseases/conditions:
Mild Cognitive Impairment (MCI), age-related brain changes

Potential Biomarkers:
Brain-enriched plasma the “miR-132 family” (miR-128/miR-491-5p, miR-132/miR-491-5p and mir-874/miR-491-5p) and the “miR-134 family” (miR-134/miR-370, miR-323-3p/miR-370 and miR-382/miR-370)

Potential Applications:
Early diagnostic biomarkers (“at asymptomatic stage 1-5 years prior to clinical diagnosis”)

References:
Sheinerman KS, Tsivinsky VG, Crawford F, Mullan MJ, Abdullah L, Umansky SR. Plasma microRNA biomarkers for detection of mild cognitive impairment. Aging (Albany NY). 2012 Sep;4(9):590-605. (http://www.ncbi.nlm.nih.gov/pubmed/23001356)

•    Associated diseases/conditions:
Schizophrenia

Potential Biomarkers:
Peripheral blood hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652

Potential Applications:
Diagnostic biomarkers

References:
Lai CY, Yu SL, Hsieh MH, Chen CH, Chen HY, Wen CC, Huang YH, Hsiao PC, Hsiao
CK, Liu CM, Yang PC, Hwu HG, Chen WJ. MicroRNA expression aberration as potential peripheral blood biomarkers for schizophrenia. PLoS One. 2011;6(6):e21635. doi: 10.1371/journal.pone.0021635. (http://www.ncbi.nlm.nih.gov/pubmed/21738743)

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The Database of Biomarkers: MicroRNA Biomarkers – Cardiovascular Diseases

The Database of Biomarkers: MicroRNA Biomarkers – Cardiovascular Diseases

•    Associated diseases/conditions:
Atherosclerosis and stroke

Potential Biomarkers:
Serum miR-21 and miR-221

Potential Applications:
Predictive biomarkers

References:
Tsai PC, Liao YC, Wang YS, Lin HF, Lin RT, Juo SH. Serum microRNA-21 and microRNA-221 as potential biomarkers for cerebrovascular disease. J Vasc Res. 2013;50(4):346-54. doi: 10.1159/000351767. (http://www.ncbi.nlm.nih.gov/pubmed/23860376)

•    Associated diseases/conditions:
Myocardial injury in open-heart surgeries with cardiopulmonary bypass (CPB)

Potential Biomarkers:
Serum and urine miR-1

Potential Applications:
Diagnostic biomarker

References:
Zhou X, Mao A, Wang X, Duan X, Yao Y, Zhang C. Urine and serum microRNA-1 as novel biomarkers for myocardial injury in open-heart surgeries with cardiopulmonary bypass. PLoS One. 2013 Apr 22;8(4):e62245. doi: 10.1371/journal.pone.0062245. (http://www.ncbi.nlm.nih.gov/pubmed/23630629)

•    Associated diseases/conditions:
Acute myocardial infarction (AMI)

Potential Biomarkers:
Peripheral blood miR-1291 and miR-663b (disease markers); miR-30c and miR-145 (markers of infarct sizes)

Potential Applications:
Diagnostic biomarkers

References:
Meder B, Keller A, Vogel B, Haas J, Sedaghat-Hamedani F, Kayvanpour E, Just S, Borries A, Rudloff J, Leidinger P, Meese E, Katus HA, Rottbauer W. MicroRNA signatures in total peripheral blood as novel biomarkers for acute myocardial infarction. Basic Res Cardiol. 2011 Jan;106(1):13-23. doi: 10.1007/s00395-010-0123-2. (http://www.ncbi.nlm.nih.gov/pubmed/20886220)

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The Database of Biomarkers: MicroRNA Biomarkers – Cancers

  • Associated diseases/conditions:

Breast cancer in the Mexican population

 

Potential Biomarkers:

Serum circulating miR-145, miR-155 and miR-382

 

Potential Applications:

Diagnostic biomarkers

 

References:

Mar-Aguilar F, Mendoza-Ramírez JA, Malagón-Santiago I, Espino-Silva PK, Santuario-Facio SK, Ruiz-Flores P, Rodríguez-Padilla C, Reséndez-Pérez D. Serum circulating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers. 2013;34(3):163-9. doi: 10.3233/DMA-120957. (http://www.ncbi.nlm.nih.gov/pubmed/23334650)

 

  • Associated diseases/conditions:

Stage III colorectal cancer (CRC)

 

Potential Biomarkers:

Serum circulating miR-18a and miR-29a

 

Potential Applications:

Screening and monitoring biomarkers

 

References:

Vega AB, Pericay C, Moya I, Ferrer A, Dotor E, Pisa A, Casalots À, Serra-Aracil X, Oliva JC, Ruiz A, Saigí E. microRNA expression profile in stage III colorectal cancer: circulating miR-18a and miR-29a as promising biomarkers. Oncol Rep. 2013 Jul;30(1):320-6. doi: 10.3892/or.2013.2475. (http://www.ncbi.nlm.nih.gov/pubmed/23673725)

 

  • Associated diseases/conditions:

Endometrioid endometrial cancer (EEC)

 

Potential Biomarkers:

Serum miR-222, miR-223, miR-186 and miR-204

 

Potential Applications:

Diagnostic biomarkers

 

References:

Jia W, Wu Y, Zhang Q, Gao G, Zhang C, Xiang Y. Identification of four serum microRNAs from a genome-wide serum microRNA expression profile as potential non-invasive biomarkers for endometrioid endometrial cancer. Oncol Lett. 2013 Jul;6(1):261-267. (http://www.ncbi.nlm.nih.gov/pubmed/23946815)

 

  • Associated diseases/conditions:

Classical Hodgkin Lymphoma (cHL)

 

Potential Biomarkers:

miR-494, miR-1973 and miR-21 (up-regulation)

 

Potential Applications:

Disease response biomarkers

 

References:

Jones KL, Nourse JP, Keane C, Bhatnagar A, Gandhi M. Plasma microRNA are disease response biomarkers in classical Hodgkin lymphoma. Clin Cancer Res. 2013  Nov 12. (http://www.ncbi.nlm.nih.gov/pubmed/24222179)


 

  • Associated diseases/conditions:

Early larynx carcinoma

 

Potential Biomarkers:

“30 up-regulated (e.g., hsa-miR-657), 17 down-regulated miRNA ( e.g., hsa-miR-1287)”

 

Potential Applications:

Predictive biomarkers for early diagnosis

 

References:

Wang Y, Chen M, Tao Z, Hua Q, Chen S, Xiao B. Identification of predictive biomarkers for early diagnosis of larynx carcinoma based on microRNA expression data. Cancer Genet. 2013 Oct 2. doi:pii: S2210-7762(13)00138-5. 10.1016/j.cancergen.2013.09.005. (http://www.ncbi.nlm.nih.gov/pubmed/24238754)

 

  • Associated diseases/conditions:

Lung cancer

 

Potential Biomarkers:

Plasma miR-205, -19a, -19b, -30b, and -20a

 

Potential Applications:

Diagnosis biomarkers

 

References:

Aushev VN, Zborovskaya IB, Laktionov KK, Girard N, Cros MP, Herceg Z, Krutovskikh V. Comparisons of microRNA patterns in plasma before and after tumor removal reveal new biomarkers of lung squamous cell carcinoma. PLoS One. 2013 Oct 9;8(10):e78649. doi: 10.1371/journal.pone.0078649. (http://www.ncbi.nlm.nih.gov/pubmed/24130905)

 

  • Associated diseases/conditions:

Non-small cell lung cancer (NSCLC)

 

Potential Biomarkers:

Serum miR-15b and miR-27b

 

Potential Applications:

Early diagnostic biomarkers

 

References:

Hennessey PT, Sanford T, Choudhary A, Mydlarz WW, Brown D, Adai AT, Ochs MF, Ahrendt SA, Mambo E, Califano JA. Serum microRNA biomarkers for detection of non-small cell lung cancer. PLoS One. 2012;7(2):e32307. doi:10.1371/journal.pone.0032307. (http://www.ncbi.nlm.nih.gov/pubmed/22389695)

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The Database of Biomarkers: Proteomic and Pathway Biomarkers

  • Associated diseases/conditions:

Depression

 

Potential Biomarkers:

“Tumor Necrosis Factor (TNF) and its targets in the inflammatory cytokine pathway”

 

Detections:

Protein expressions

 

Potential Applications:

“Predictive biomarkers for treatment response (to the selective serotonin reuptake inhibitor (SSRI) antidepressant escitalopram)”

 

References:

Powell TR, Schalkwyk LC, Heffernan AL, Breen G, Lawrence T, Price T, Farmer AE, Aitchison KJ, Craig IW, Danese A, Lewis C, McGuffin P, Uher R, Tansey KE, D’Souza UM. Tumor Necrosis Factor and its targets in the inflammatory cytokine pathway are identified as putative transcriptomic biomarkers for escitalopram response. Eur Neuropsychopharmacol. 2013 Sep;23(9):1105-14. doi: 10.1016/j.euroneuro.2012.09.009. (http://www.ncbi.nlm.nih.gov/pubmed/23142150)

 

  • Associated diseases/conditions:

Multiple sclerosis (MS)

 

Potential Biomarkers:

c-Jun N-terminal kinase (JNK) -dependent apoptosis pathway

 

Detections:

Gene expression, peripheral blood mononuclear cells (PBMCs)

 

Potential Applications:

“Diagnostic biomarkers in the active phase of relapsing-remitting MS (RRMS)”

 

References:

Ferrandi C, Richard F, Tavano P, Hauben E, Barbié V, Gotteland JP, Greco B, Fortunato M, Mariani MF, Furlan R, Comi G, Martino G, Zaratin PF. Characterization of immune cell subsets during the active phase of multiple sclerosis reveals disease and c-Jun N-terminal kinase pathway biomarkers. Mult Scler. 2011 Jan;17(1):43-56. doi: 10.1177/1352458510381258. (http://www.ncbi.nlm.nih.gov/pubmed/20855355)

 

  • Associated diseases/conditions:

Cardiovascular disease (CVD)

 

Potential Biomarkers:

Osteoprotegerin (OPG) pathway

 

Detections:

Protein serum concentrations

 

Potential Applications:

“Predictive biomarkers for CVD risk factor burden and mortality”

 

References:

Lieb W, Gona P, Larson MG, Massaro JM, Lipinska I, Keaney JF Jr, Rong J, Corey D, Hoffmann U, Fox CS, Vasan RS, Benjamin EJ, O’Donnell CJ, Kathiresan S. Biomarkers of the osteoprotegerin pathway: clinical correlates, subclinical disease, incident cardiovascular disease, and mortality. Arterioscler Thromb Vasc Biol. 2010 Sep;30(9):1849-54. doi: 10.1161/ATVBAHA.109.199661. (http://www.ncbi.nlm.nih.gov/pubmed/20448212)

 

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The Database of Biomarkers: Proteomic and Pathway Biomarkers – Cancers

  • Associated diseases/conditions:

Non-small cell lung cancer (NSCLC)

 

Potential Biomarkers:

Insulin-like growth factor (IGF) pathway

 

Detections:

Protein expressions, serum levels

 

Potential Applications:

Predictive biomarkers (tumor progression and patient outcomes)

 

References:

Shersher DD, Vercillo MS, Fhied C, Basu S, Rouhi O, Mahon B, Coon JS, Warren WH, Faber LP, Hong E, Bonomi P, Liptay MJ, Borgia JA. Biomarkers of the insulin-like growth factor pathway predict progression and outcome in lung cancer. Ann Thorac Surg. 2011 Nov;92(5):1805-11; discussion 1811. doi: 10.1016/j.athoracsur.2011.06.058. (http://www.ncbi.nlm.nih.gov/pubmed/21945224)

 

  • Associated diseases/conditions:

Non-small cell lung cancer (NSCLC)

 

Potential Biomarkers:

Epidermal growth factor receptor (EGFR) pathway

 

Detections:

Mutations, lymph nodes

 

Potential Applications:

“Diagnostic biomarkers for tumor heterogeneity (between primary tumors and lymph node metastasis)”

 

References:

Park S, Holmes-Tisch AJ, Cho EY, Shim YM, Kim J, Kim HS, Lee J, Park YH, Ahn JS, Park K, Jänne PA, Ahn MJ. Discordance of molecular biomarkers associated with epidermal growth factor receptor pathway between primary tumors and lymph node metastasis in non-small cell lung cancer. J Thorac Oncol. 2009 Jul;4(7):809-15. doi: 10.1097/JTO.0b013e3181a94af4. (http://www.ncbi.nlm.nih.gov/pubmed/19487967)

 

  • Associated diseases/conditions:

Colorectal cancer

 

Potential Biomarkers:

APC/β-catenin signaling pathway

 

Detections:

Expressions in mucosa from the rectum and colon

 

Potential Applications:

“Treatable, preneoplastic biomarkers of risk for colorectal neoplasms”

 

References:

Ahearn TU, Shaukat A, Flanders WD, Seabrook ME, Bostick RM. Markers of the APC/β-catenin signaling pathway as potential treatable, preneoplastic biomarkers of risk for colorectal neoplasms. Cancer Epidemiol Biomarkers Prev. 2012 Jun;21(6):969-79. doi: 10.1158/1055-9965.EPI-12-0126. (http://www.ncbi.nlm.nih.gov/pubmed/22539608)

 

  • Associated diseases/conditions:

Head and neck squamous cell carcinoma (HNSCC)

 

Potential Biomarkers:

Phosphoinositide 3-kinase (PI3K) pathway

 

Detections:

Frequent mutations in tumors

 

Potential Applications:

“Predictive biomarkers for treatment selection (mTOR/PI3K inhibitor (BEZ-235))”

 

References:

Lui VW, Hedberg ML, Li H, Vangara BS, Pendleton K, Zeng Y, Lu Y, Zhang Q, Du Y, Gilbert BR, Freilino M, Sauerwein S, Peyser ND, Xiao D, Diergaarde B, Wang L, Chiosea S, Seethala R, Johnson JT, Kim S, Duvvuri U, Ferris RL, Romkes M, Nukui T, Kwok-Shing Ng P, Garraway LA, Hammerman PS, Mills GB, Grandis JR. Frequent mutation of the PI3K pathway in head and neck cancer defines predictive biomarkers. Cancer Discov. 2013 Jul;3(7):761-9. doi: 10.1158/2159-8290.CD-13-0103.  (http://www.ncbi.nlm.nih.gov/pubmed/23619167)

 

  • Associated diseases/conditions:

Squamous carcinoma

 

Potential Biomarkers:

Interferon (IFN)/STAT1 and neuregulin signaling pathways

 

Detections:

mRNAs expressions, tumor cell-line models

 

Potential Applications:

“Predictive biomarkers for treatment response (to cetuximab (Erbitux®))”

 

References:

Oliveras-Ferraros C, Vazquez-Martin A, Queralt B, Adrados M, Ortiz R, Cufí S,  Hernández-Yagüe X, Guardeño R, Báez L, Martin-Castillo B, Pérez-Martínez MC, Lopez-Bonet E, De Llorens R, Bernadó L, Brunet J, Menendez JA. Interferon/STAT1 and neuregulin signaling pathways are exploratory biomarkers of cetuximab (Erbitux®) efficacy in KRAS wild-type squamous carcinomas: a pathway-based analysis of whole human-genome microarray data from cetuximab-adapted tumor cell-line models. Int J Oncol. 2011 Dec;39(6):1455-79. doi: 10.3892/ijo.2011.1155. (http://www.ncbi.nlm.nih.gov/pubmed/21833472)

 

  • Associated diseases/conditions:

Colorectal cancer

 

Potential Biomarkers:

Wnt signaling pathway

 

Detections:

Tag single nucleotide polymorphisms (tSNPs) in adenomatous polyposis coli

 

Potential Applications:

Prognostic biomarkers for outcome prediction

 

References:

Ting WC, Chen LM, Pao JB, Yang YP, You BJ, Chang TY, Lan YH, Lee HZ, Bao BY. Common genetic variants in Wnt signaling pathway genes as potential prognostic biomarkers for colorectal cancer. PLoS One. 2013;8(2):e56196. doi: 10.1371/journal.pone.0056196. (http://www.ncbi.nlm.nih.gov/pubmed/23405266)

 

  • Associated diseases/conditions:

IBD-associated colorectal carcinogenesis

 

Potential Biomarkers:

Wnt-pathway activation

 

Detections:

Protein expressions, tissue microarray with colonic samples

 

Potential Applications:

Early diagnostic biomarkers

 

References:

Claessen MM, Schipper ME, Oldenburg B, Siersema PD, Offerhaus GJ, Vleggaar FP. WNT-pathway activation in IBD-associated colorectal carcinogenesis: potential biomarkers for colonic surveillance. Cell Oncol. 2010 Jan 1;32(4):303-10. doi: 10.3233/CLO-2009-0503. (http://www.ncbi.nlm.nih.gov/pubmed/20208143)

 

  • Associated diseases/conditions:

Epithelial ovarian cancer

 

Potential Biomarkers:

Wnt pathway

 

Detections:

DNA methylation (epigenetic regulations) at promoter CpG islands (CGI), from epithelial ovarian tumors

 

Potential Applications:

“Predictive biomarkers of patient progression-free survival (PFS)”

 

References:

Dai W, Teodoridis JM, Zeller C, Graham J, Hersey J, Flanagan JM, Stronach E, Millan DW, Siddiqui N, Paul J, Brown R. Systematic CpG islands methylation profiling of genes in the wnt pathway in epithelial ovarian cancer identifies biomarkers of progression-free survival. Clin Cancer Res. 2011 Jun 15;17(12):4052-62. doi: 10.1158/1078-0432.CCR-10-3021. (http://www.ncbi.nlm.nih.gov/pubmed/21459799)

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Bioinformatics Databases and Tools for the Discovery of Proteomic and Pathway Biomarkers

Feature: “For the visualization and analysis of network graphs”

Feature: “Network visualization”

Feature: Computational models

Feature: “comparison of 2DE patterns in the context of proteome sequence queries”

Feature: “Standardizing the representation of gene and gene product attributes across species and databases.”

Feature: “Gene map annotator and pathway profiler”

Feature: A molecular interaction database.

Feature: “An integrated pathway gene relationship database for model organisms and important pathogens”

Feature: “A collection of manually drawn pathway maps”

Feature: “Mammalian Protein-Protein Interaction Database”

Feature: “A pathway and gene-set enrichment database”

Feature: “Classification System”

Feature: “A multi-organism phenotype-genotype database”

Feature: A pathway database.

Feature: “To simulate cellular and biochemical processes”

Feature: “Small Molecule Pathway Database”

Feature: “Transcriptional regulatory elements”

Feature: “Virtual Cell Modeling & Analysis Software”

 

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Bioinformatics Resources for MicroRNA (miRNA) Studies and Biomarker Discovery

Feature: “MicroRNA / Transcription Factor Regulatory Circuits in Human and Mouse”

Feature: “For annotating and discovering small and long ncRNAs”

Feature: “The miRNA expression profile”

Feature: “microRNA target prediction”

Feature: “microRNA expression and sequence analysis database”

Feature: “microRNA target sites within human promoter sequences

Feature: “microRNA targets and expression”

Feature: “A searchable database of published miRNA sequences”

Feature: “miRNA target prediction and functional annotations”

Feature: “Disease-associated SNPs and microRNA target sites on 3’UTRs of human genes”

Feature: “For animal miRNA-target interactions”

Feature: “microRNA genomic information and regulation”

Feature: “Genomic maps of microRNAs”

Feature: “Animal, plant and virus microRNA data”

Feature: “microRNA-target interactions database”

Feature: “microRNA target site predictions”

Feature: “Polymorphism in microRNAs and their Target Sites”

Feature: “A database of miRNA target predictions”

Feature: “microRNA target detection”

Feature: “Immunological effects of RNA interference and microRNA reagents”

Feature: “Patterns of miRNA expression in various human sarcoma types”

Feature: “Somatic mutations impacting microRNA targeting in cancer genomes”

Feature: “microRNA interaction networks”

Feature: “Predicts biological targets of miRNAsin mammals”

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Journals Focusing on Biomarkers

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The dynamic brain networks, MRI, and neuroimaging biomarkers

Processes in the cerebral cortex have the nonlinear and dynamic features. Accurate temporal coordination is necessary for the establishment of functional networks and assemblies. The brain is a complex and self-organized system with serial operations involved in highly interconnected networks. The cortical networks have a high-dimensional state space with non-stationary time series that are challenging to analyze (Singer, 2013).

Connectome refers to the connectivity among brain regions. The combination of such connectivity may lead to a complete picture of the entire brain networks. Magnetic resonance imaging (MRI), including structural MRI and diffusion MRI may provide the neuroimaging technique for the analysis of the connectome (Clayden, 2013). Such analyses may be useful for both clinical and non-clinical studies in neuroscience.

For example, mild cognitive impairment (MCI) has been considered a precursor for Alzheimer’s disease and a predictor for other neurodegenerative diseases. However, it is still difficult to have accurate and efficient diagnosis for MCI. Neuroimaging biomarkers have not been very well validated, compared to conventional biomarkers such as amyloid plaques, tau protein expression, and brain tissue atrophy. To solve the problem, a connectomes-scale analysis of structural and functional connectivity in MCI based on multimodal diffusion tensor imaging (DTI)/ functional magnetic resonance imaging (fMRI) datasets has been found useful (Zhu et al., 2013).

The analyses included DTI-derived structural profiles, the whole-brain functional connectivity, and a set of functional connectomes called “connectome signatures.” Most of the “connectome signatures” were obtained from the functional interactive networks such as the cognition-perception and cognition-action domains (Zhu et al., 2013). Specifically, the “connectome signatures” showed high MCI-vs-controls categorization accuracy. Such study indicated that functional “connectome signatures” may serve as neuroimaging biomarkers of MCI.

References:

Clayden JD. Imaging connectivity: MRI and the structural networks of the brain. Funct Neurol. 2013 Jul/sep;28(3):197-203. doi: 10.11138/FNeur/2013.28.3.197.

Singer W. Cortical dynamics revisited. Trends Cogn Sci. 2013 Oct 16. doi:pii: S1364-6613(13)00210-6. 10.1016/j.tics.2013.09.006.

Zhu D, Li K, Terry DP, Puente AN, Wang L, Shen D, Miller LS, Liu T. Connectome-scale assessments of structural and functional connectivity in MCI. Hum Brain Mapp. 2013 Sep 30. doi: 10.1002/hbm.22373.

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The integration of various “omics” technologies in systems biology for better diagnosis and treatment

With the advancements in the “omics” technologies and the information technology, systems biology has become an important approach for biomarker discovery. These technologies allow faster and cheaper analyses of gene expression, protein interactions, signaling pathways, and metabolic mechanisms. In this era of functional genomics, thousands of molecular signals can be measured for further analysis and meaningful interpretations in clinical medicine. Various fields can be integrated in the systems biology studies.

Specifically, studies in genomics can be used to detect genetic differences between individuals. Studies in proteomics allow the assessment of proteins at a large-scale. Studies in metabolomics enable the profiling of small molecules for the better understanding of cellular processes. Studies in transcriptomics allow the quantitative measurement of mRNAs in different cells and tissues for the detection of gene expression patterns during various biological and pathological states of a tissue. Studies in epigenetics are helpful for understanding DNA methylation and the regulation of genetic activities. These basic scientific areas can be integrated into the systems biology approach for further translational studies to be applied in clinical medicine.

In the case of epilepsy, better antiepileptic drugs (AEDs) are still needed for the prevention and control of the disease. The unbalanced reductionist approaches have been found to ignore the complexity of the disease itself as well as the mechanisms of the drug resistance (Margineanu, 2013). Integrative and systems biology strategies are necessary for understanding the interactive networks in physiological, pathological, and pharmacological processes.

In another example, more accurate diagnostic and prognostic biomarkers are needed for better clinical care of sepsis. However, traditional markers have been found inadequate to meet the goal. Systems biology-based approaches that integrate various “omics” technologies have been suggested to provide better ways for the identification of biomarkers in sepsis, including disease pathways (Skibsted et al., 2013). Such systems-based biomarkers may support the discovery of better treatment targets.

References:

Margineanu DG. Systems biology, complexity, and the impact on antiepileptic drug discovery. Epilepsy Behav. 2013 Sep 30. doi:pii: S1525-5050(13)00434-4.10.1016/j.yebeh.2013.08.029.

Skibsted S, Bhasin MK, Aird WC, Shapiro NI. Bench-to-bedside review: Future novel diagnostics for sepsis – a systems biology approach. Crit Care. 2013 Oct 4;17(5):231.

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Systems biology approaches for the pharmacokinetic/pharmacodynamic (PK/PD) modeling in drug discovery and development

Systems biology-enabled computational modeling can be useful in almost every step of drug discovery and development, including drug target identification and clinical outcome analysis. Various modeling strategies can be used at different stages.

For example, during the basic scientific research stage, systems biology approaches and data mining techniques are useful for analyzing laboratory data. Such approaches would allow the development of computational models for the studies of cellular mechanisms including metabolic processes, signaling pathways, transcription factors, and protein interactions (Melas et al., 2013). Such models can be applied for drug target identification, analysis of the drug’s mode of action (MOA), and drug efficacy evaluations.

During the drug development stage, computational analysis and systems biology methods can assist pharmacokinetic/pharmacodynamic (PK/PD) modeling for safety analysis and dosage testing. Such modeling would be useful for the studies of the associations among drug input, plasma concentration, as well as PD responses (Melas et al., 2013).

The development of predictive preclinical PK/PD models during the early-stage of clinical trials would help overcome obstacles in the drug discovery processes (Zhou and Gallo, 2011). For example, physiologically based (PB) PK modeling methods such as the whole-body PBPK model have been found especially useful.

Based on the systems biology approaches, a systems pharmacology platform can be established to facilitate the development of predictive PK/PD models. Such models would benefit the processes in both drug discovery and drug development.

References:

Melas IN, Kretsos K, Alexopoulos LG. Leveraging systems biology approaches in clinical pharmacology. Biopharm Drug Dispos. 2013 Aug 23. doi: 10.1002/bdd.1859.

Zhou Q, Gallo JM. The pharmacokinetic/pharmacodynamic pipeline: translating anticancer drug pharmacology to the clinic. AAPS J. 2011 Mar;13(1):111-20. doi: 10.1208/s12248-011-9253-1.

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Systems biology strategies are useful for biomarker identification in traumatic brain injury (TBI)

The identification of biomarkers may have a significant influence on patient care. Biomarkers may be used for better diagnosis, disease profiling, research design, and drug target discovery. For instance, biomarkers have been considered to be critical in the management of traumatic brain injury (TBI) (Feala et al., 2013; Papa et al., 2008).

Complex connections have been observed between TBI and other neurological problems. The multifactorial feature of TBI has made it difficult to identify useful biomarkers for diagnosis, prognosis, and effective treatment. Systems biology approaches may be helpful for analyzing the complex molecular networks for the understanding of the secondary cellular responses.

Publicly available resources including KEGG, Reactome, and Gene Ontology can be used for the genetic and pathway annotation. For example, by using pathway analysis, a list of 32 proteins has been identified as candidate biomarkers (Feala et al., 2013). The analysis showed that proteins from the immune system may have direct interactions with proteins related to Alzheimer’s disease and apoptosis. These immune proteins may also interact indirectly with biomarkers including SPTAN1 (spectrin, alpha, non-erythrocytic 1 (alpha-fodrin)) and GFAP (glial fibrillary acidic protein). Understanding of these mechanisms may be useful for further analysis in a network context.

References:

Feala JD, Abdulhameed MD, Yu C, Dutta B, Yu X, Schmid K, Dave J, Tortella F, Reifman J. Systems biology approaches for discovering biomarkers for traumatic brain injury. J Neurotrauma. 2013 Jul 1;30(13):1101-16. doi: 10.1089/neu.2012.2631.

Papa L, Robinson G, Oli M, Pineda J, Demery J, Brophy G, Robicsek SA, Gabrielli A, Robertson CS, Wang KK, Hayes RL. Use of biomarkers for diagnosis and management of traumatic brain injury patients. Expert Opin Med Diagn. 2008 Aug;2(8):937-45. doi: 10.1517/17530059.2.8.937.

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Systems biology and “omics” technologies for better drug discovery and outcome studies in cancer treatment

The multiple platforms using the “omics” technologies and the integration of systems biology approaches including both experimental and computational methods would support both drug target discovery and clinical outcome studies, especially in cancer treatment.

For instance, immunotherapies have become an important treatment strategy for cancer. Complex cellular and molecular networks are involved in both immune cells and tumor cells, as well as the interactions between the immune system and tumors, especially in the regulation of tumor growth by the immune system.

For better understanding of tumor immunosurveillance, it is necessary to apply “omics” and high-throughput molecular profiling technologies for the studies of lymphocyte infiltration, tumor destruction, and epitopes (Guhathakurta et al., 2013). These technologies include DNA and protein microarrays, biospecimen platforms, next-generation sequencing, and mass spectrometry.

For example, mRNA profiling can be applied to examine the extent of tumor infiltration of lymphocytes (Guhathakurta et al., 2013). Protein or peptide microarrays may be useful to evaluate the diversity of antibody responses. Next-generation sequencing may be applied to test the variety of T cell clones.

In addition, systems biology and computational methods such as data mining and algorithmic predictions can be useful for understanding immunogenicity and cancer immunotherapies. The data integration of across multiple platforms would allow the identification of antigens, biomarkers, and therapeutic targets. These methods would also enable faster and more efficient drug discovery process (Kraljevic et al., 2007).

References:

Guhathakurta D, Sheikh NA, Meagher TC, Letarte S, Trager JB. Applications of systems biology in cancer immunotherapy: from target discovery to biomarkers of clinical outcome. Expert Rev Clin Pharmacol. 2013 Jul;6(4):387-401. doi:10.1586/17512433.2013.811814.

 

Kraljevic Pavelic S, Saban N. Evolving ‘-omics’ technologies in the drug development process. Expert Opin Drug Discov. 2007 Apr;2(4):431-6. doi:10.1517/17460441.2.4.431. PubMed PMID: 23484753.

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Molecular diagnostics technologies and systems medicine for individualized treatment

The advancement of genomic and other “omics” technologies has made significant contribution to the development of molecular medicine, including the understanding of cell structure and function, as well as epigenetic variability and functional changes. Such understanding would be useful for improving diagnosis, prevention, and disease treatment. On the basis of systems biology analysis, systems medicine can be developed for more integrative and holistic health care, and move from the reactive to the proactive practice.

For example, the elucidation of the alterations in cellular processes in traumatic or degenerative musculoskeletal conditions would enable better tools and approaches for the diagnosis and therapies of musculoskeletal diseases (Mashayekhi et al., 2013). The comparison of the crosstalk among cellular pathways between normal and disease conditions would allow personalized treatment targeting specific cells.

With such understanding, the conventional “one-size-fits-all” medicine would be replaced by personalized medicine that would lead to better clinical outcomes and reduced health care costs, especially for cancer therapy. Biomarkers would play a critical role in personalized medicine. Various biomarkers need to be identified and validated, including those from serum, tissues, or imaging. On the basis of such biomarkers for prediction, prognosis, and early responses, personalized oncology would have a significant impact on cancer prevention and treatment (Kalia, 2013).

For example, imaging biomarkers using techniques including Computed Tomography (CT) and Positron Emitted Tomography (PET) would allow the early detection and treatment among cancer patients (Kalia, 2013). Further development in molecular imaging would also contribute to integrated diagnostics and theranostics. Molecular diagnostics technologies such as gene expression and proteomic tests would allow personalized and targeted chemotherapies for those cancer patients who may have more positive responses. The application of companion molecular diagnostics may also enable more efficient treatment with reduced costs.

References:

Kalia M. Personalized oncology: recent advances and future challenges. Metabolism. 2013 Jan;62 Suppl 1:S11-4. doi: 10.1016/j.metabol.2012.08.016.

Mashayekhi K, O’Brien M, Zugun-Eloae F, Labusca L. Novel approaches for treating musculoskeletal diseases: molecular orthopedics and systems medicine. Open Orthop J. 2013 May 3;7:144-51. doi: 10.2174/1874325001307010144.

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Systems medicine, P4 medicine, and an integrative “Human Model”

Conventional practice in Western medicine focuses on doctors’ “reactive” interventions in response to the occurrence of diseases. With the advancement of genomics, proteomics, and various high efficient “omics” technologies, the multidimensional networks of the human body can be investigated for the better understanding of health and complex diseases.

Systems biology studies can help elucidate the multiple feedback mechanisms in the high complex systems of the human body. Conceptual and functional connections can be established among genes, proteins, cells, organs, systems, and the whole organism. An integrative “Human Model” can be developed to associate the functions of the systems at various levels, and to connect scientific mechanisms with clinical outcomes (Hester et al., 2011).

Based on such understanding, systems medicine can be developed to supply a conceptual and theoretical framework that translates the rapid developments in the basic biomedical science to better clinical practice. An “anticipatory” medicine with more personalized, predictive, preventive and participatory (P4) healthcare would be available on the basis of such framework (Sobradillo et al., 2011; Vandamme et al., 2013). Multidisciplinary collaborations among various fields including biology, physiology, and clinical medicine would also be needed to achieve the P4 medicine.

 

References:

 

Hester RL, Iliescu R, Summers R, Coleman TG. Systems biology and integrative physiological modelling. J Physiol. 2011 Mar 1;589(Pt 5):1053-60. doi: 10.1113/jphysiol.2010.201558.

Sobradillo P, Pozo F, Agustí A. P4 medicine: the future around the corner. Arch Bronconeumol. 2011 Jan;47(1):35-40. doi: 10.1016/j.arbres.2010.09.009.

Vandamme D, Fitzmaurice W, Kholodenko B, Kolch W. Systems medicine: helping us understand the complexity of disease. QJM. 2013 Oct;106(10):891-5. doi:

10.1093/qjmed/hct163.

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Individualized systems medicine (ISM): applications in the treatment of acute myeloid leukemia (AML)

Approaches in individualized systems medicine (ISM) or personalized systems medicine would be useful for achieving optimal therapies, especially in cancer treatment. Strategies in ISM have been suggested by Pemovska et al., by focusing on the establishment of molecular profiling (Pemovska et al. 2013).

To do this, the researchers tested ex vivo drug sensitivity and resistance of acute myeloid leukemia (AML) patients’ cancer cells in response to 187 oncology drugs (Pemovska et al. 2013).  The consecutive patient samples were examined to investigate the mechanisms of the drug resistance.

Based on the profiles, the tests revealed 5 drug response subtypes (Pemovska et al. 2013). Some of the subtypes showed unique genomic properties, such as gene fusions and mutations. Different clinical responses were observed based on the profiling. These changes include the clonal evolution of the AML cells, genomic alterations that may contribute to the drug resistance, and new sensibilities to drugs that were not effective previously (Pemovska et al. 2013).

Such strategies may also be used for further studies of the clonal evolution of AML cells. For example, systems studies of the FLT3-dependent and FLT3-independent pathways would be helpful for finding potential targets (Leung et al., 2013).

Overall, the ISM strategies may be helpful to predict the effectiveness of the clinical applications of drug therapies. Such approaches can also be applied as the guidelines for more effective therapies.

 

References:

Leung AY, Man CH, Kwong YL. FLT3 inhibition: a moving and evolving target in acute myeloid leukaemia. Leukemia. 2013 Feb;27(2):260-8. doi: 10.1038/leu.2012.195.

Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, Almusa H, Bespalov MM, Ellonen P, Elonen E, Gjertsen BT, Karjalainen R, Kulesskiy E, Lagström S, Lehto A, Lepistö M, Lundán T, Majumder MM, Lopez Marti JM, Mattila P, Murumägi A, Mustjoki S, Palva A, Parsons A, Pirttinen T, Rämet ME, Suvela M, Turunen L, Västrik I, Wolf M, Knowles J, Aittokallio T, Heckman CA, Porkka K, Kallioniemi O, Wennerberg K. Individualized Systems Medicine (ISM) strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 2013 Sep 20.

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Review of Systems Biology Approaches: Detecting Signaling Pathways in Neurons

Temporal dynamics and spatial features are important factors in cell responses including synaptic plasticity. The complex network and interactions among neuronal signaling pathways result in emergent dynamical behaviors associated with synaptic plasticity. Signaling pathways can be activated by transmembrane receptors and result in transcription in the nucleus. For example, ionic channels and G protein coupled receptors may activate signaling pathway molecules and lead to electrical activities at various spatial and time scales (Blackwell, 2013). Functions of ionic channels can be altered by calcium at small temporal and spatial scales. Furthermore, functions of ionic channels can be altered by different kinases and phosphatases at larger temporal and spatial scales and result in synaptic plasticity.

Both experimental and computational methods for modeling neuronal signaling pathways have been suggested useful (Blackwell and Jedrzejewska-Szmek, 2013). For example, spatial models of signaling pathways have been related to extracellular signal-related kinase (ERK) activation among hippocampal neurons. Spatial models have also shown that anchoring proteins in synaptic plasticity play critical roles in putting molecules close to their activators. In addition, differences have been found between potentiation and depression among the spatial distribution of synaptic plasticity.

To better understand the functions of synaptic plasticity, it is needed to elucidate the roles of transmembrane receptors in stimulating ERK in neurons, as well as the targets of kinases. Challenges in the development of spatial models for synaptic plasticity include the estimation of parameter and the analyses of sensitivity (Blackwell and Jedrzejewska-Szmek, 2013). It is also necessary to simulate different reaction and diffusion events within various temporal and spatial scales (Blackwell, 2013). Systems biology approaches with interdisciplinary strategies would be helpful for meeting these challenges.

References:

Blackwell KT. (2013) Approaches and tools for modeling signaling pathways and calcium dynamics in neurons. J Neurosci Methods. 2013 Jun 3.

Blackwell KT, Jedrzejewska-Szmek J. (2013) Molecular mechanisms underlying neuronal synaptic plasticity: systems biology meets computational neuroscience in the wilds of synaptic plasticity. Wiley Interdiscip Rev Syst Biol Med.

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Psychosocial Factors and Cancer Progression: Psychoneuroimmunology (PNI) Mechanisms

Psychosocial factors have been found to play important roles in the development of various diseases, including cancer progression. Such associations are mediated via the biobehavioral pathways in various patient populations, including the hematopoietic stem cell transplant (HCT) patients (Knight et al., 2013). The exploration of these biopsychosocial pathways that connect psychosocial factors with clinical outcomes may ultimately result in better clinical outcomes for those patient groups that are psychologically and immunologically vulnerable.

For example, studies focusing on psychoneuroimmunology (PNI) and developmental neuroscience have identified the influences on the regulation of cancer progression by catecholamine hormones including norepinephrine and epinephrine, as well as their receptors, the β-adrenergic receptors (β-ARs) (Yang and Eubank, 2013). Specifically, psychological stress may have effects on different types of tumor progression from proliferation to angiogenesis, as well as metastasis. Such effects can be mediated by molecular factors such as the catecholamines norepinephrine and epinephrine. The β-adrenergic receptors (β-ARs) are also critical in these effects. The understanding of such mechanisms may be helpful for the development of β-blockers for the purpose of adjuvant cancer therapy.

For the hematopoietic stem cell transplant (HCT) patients, studies on the relationships among psychosocial, endocrine and immune factors have revealed pathways that may mediate the connections between psychosocial factors and disease progressions. These pathways involve catecholamines, glucocorticoids, and vascular endothelial growth factor (VEGF) (Knight  et al., 2013). Mechanisms of inflammation, immune reconstitution, as well as infectious susceptibility have also been found important. Based on these mechanisms, immunomodulating and psychosocial interventions may be applied as potential strategies.

 

References:

 

Knight JM, Lyness JM, Sahler OJ, Liesveld JL, Moynihan JA. Psychosocial factors and hematopoietic stem cell transplantation: Potential biobehavioral pathways. Psychoneuroendocrinology. 2013 Jul 8. doi:pii: S0306-4530(13)00233-3. 10.1016/j.psyneuen.2013.06.016.

 

Yang EV, Eubank TD. The impact of adrenergic signaling in skin cancer progression: Possible repurposing of β-blockers for treatment of skin cancer. Cancer Biomark. 2013 Jan 1;13(3):155-60. doi: 10.3233/CBM-130325.

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Psychoneuroimmunology (PNI) and Human Social Genomics

Psychoneuroimmunology (PNI) studies the relationships between behaviors and health, especially the role of immunological mechanisms underlying how the mind and body interact. That is, the interactions between the behavior and immunity, as well as the nervous system. The human adaptive responses are a complicated network composed of many different parts, including the responses of the nervous system and the immunoregulatory processes.

 

Various topics have been studied in psychoneuroimmunology, such as the interactions between the nervous system and the immune system including the hypothalamic-pituitary-adrenal (HPA) axis. These topics also include the roles of the nervous system and immune responses in behavioral changes, stress responses, and physical disorders such as infectious diseases.

 

In addition to the biological factors, external social conditions together with the subjective perceptions of these situations have been found to affect the internal biological processes at various levels, including the gene expressions (Slavich and Cole). The field studying such interactions is called “human social genomics.”

 

Genetic factors have been found to be involved in the social-environmental regulation. Gene expressions can be influenced by the neural and molecular changes result from social processes. Genetic polymorphisms may play a role in the individual responses to the social context. The interactions between social and genetic factors may affect complex behavioral and disease phenotypes. The systems studies of these interactions may contribute to the understanding of health and diseases.

 

Reference:

 

Slavich GM, Cole SW. The Emerging Field of Human Social Genomics. Clin Psychol Sci. 2013 Jul;1(3):331-348.

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Mindfulness and Psychotherapy: How It can Help

Psychotherapies containing mindfulness elements have increased to more than 40% (Simon, 2007). Mindfulness is becoming a “transtheoretical” construct and a major perceptual process in psychotherapies. Scientific studies of meditation are transforming meditation into the main stream. The focus of the studies has been turning from relaxation and concentration based meditation into mindfulness meditation. The ancient Buddhist psychological tradition is playing an important role in such transition.

 

Brain imaging and neuroplasticity studies have found that long-term meditation practice is related to changes in the brain areas, such as higher activities in the left prefrontal cortex, the area connected with the feelings of well-being and immune responses (Davidson et al., 2003). Such scientific findings have confirmed that the training of the mind can change the brain and improve the brain functions (Begley, 2007).

 

Psychotherapies involving mindfulness training based on the moment-by-moment awareness and acceptance can be helpful for various types of people. The concept of “acceptance” can be helpful for those who are self-critical. The practice of keeping the attention at the present moment can be helpful for those who have obsessive problems. The training of “awareness” can be helpful for those with impulsive control disorders including overeating. The awareness that “thoughts are not facts” is called “metacognitive awareness”, which can be helpful for those with depression (Teasdale et al., 2002).

 

Concentration Meditation and Mindfulness Meditation

 

Concentration meditation is to focus the mind on an object such as a mantra or the breath. Concentration meditation is to generate calmness and relaxation responses via the cultivation of concentration. Mindfulness meditation is to cultivate the insight of one’s personal “nature,” the mental conditions. Mindfulness includes the components of moment by moment awareness and acceptance. It is also called “insight meditation.” Concentration practice and mindfulness practice can be used together to reduce suffering.

 

References

 

Begley, S. (2007). Train you mind, change your brain. New York: Ballantine Books.

Davidson, R. J., Kabat-Zinn, J., Schumacher, J., Rosenkranz, M., Muller, D., Santorelli, S., et al. (2003). Alterations in brain and immune function produced by mindfulness meditation. Psychosomatic Medicine, 65(4), 564–570.

Simon, R. (2007). The top ten. Psychotherapy Networker, March/April, pp. 24, 25, 37.

Teasdale, J., Moore, R., Hayhurst, H., Pope, M., Williams, S., & Segal, Z. (2002). Metacognitive awareness and prevention of relapse in depression: Empirical evidence. Journal of Consulting and Clinical Psychology, 70(2), 275–287.

 

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What is Mindfulness: The Concept and Practice

What is Mindfulness: The Concept and Practice

 

Mindfulness refers to the ability of complete and continuous awareness and consciousness. This state is especially helpful during emotional turmoil. Mindfulness is a skill derived from Buddhist psychology that has been practiced for more than 2,500 years. The word “mindfulness” was translated from the Pali word “sati” that means “awareness, attention, and remembering.” Attention means focused awareness, and remembering means remembering to be aware. Although mindfulness is not enough for achieving happiness, it can provide a basis for the other elements (Rapgay and Bystrisky, 2009). Mindfulness can be an important part for changing the mind habits that contribute to suffering and unhappiness, including greed, anger, envy, and arrogance. It can be a useful tool for the observance on how suffering is generated by the mind moment by moment. Such observation can help with the development of wisdom and insight for alleviating suffering.

 

In addition to awareness and attention, clinical and therapeutic mindfulness also include the qualities of non-judgment, acceptance, and compassion. Three elements have been defined in the meaning of therapeutic mindfulness, including “awareness, of present experience, with acceptance.” (Germer et al., 2005) The word “mindfulness” has the opposite meaning of “mindlessness”, as our common mind habits are staying in the past memories or jumping to the future fantasies. Mindfulness is the training to pay attention to the current moment and being present.

 

Mindfulness is not to turn the mind blank. Instead, it is to increase the awareness of the mind. The mental activities, including emotions, will be more vivid rather than emotionless. As our attention turns to the moment-by-moment experience, we are engaged in our life more fully rather than withdrawing or escaping. Mindfulness is not to reject unpleasant experiences, but to have higher capability to bear them.

 

Mindfulness can be practiced in our daily life by paying more attention to the present moment (but without changing the routines). Meditation can also help increase mindfulness, by focusing the attention on a chosen object such as the breath and returning the focus to the object whenever the mind wanders away. There are also meditation retreats with extended periods of meditation training.

 

References

 

Germer, C., Siegel, R., & Fulton, P. (Eds.) (2005). Mindfulness and psychotherapy. New York: Guilford Press.

Rapgay, L. & Bystrisky, A. (2009). Classical mindfulness: An introduction to its theory and practice for clinical application. In Longevity and Optimal Health: Integrating Eastern and Western perspectives. Annals of the New York Academy of Sciences. 1172, 148-162.

 

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Microglia, Polydendrocytes, Ependymal cells, and the Embryonic Neural Tube

Microglia are macrophages in the brain. Macrophages are the immune cells in the blood and other parts of the body. Microglia can be activated upon neuronal damages or infections, to destroy foreign stuff or pathogens, as well as necrotic tissues. They can send signals and request help from other parts of the immune system.

Polydendrocytes are the precursor cells in the central nervous system (CNS). They are involved in regeneration upon injuries and damages. They can be changed to astrocytes, oligodendrocytes, and even neurons.

Ependymal cells are the cells around the ventricles, the fluid spaces in the brain. They can cluster with blood vessels to form choroid plexuses that generate cerebrospinal fluid. Ependymal cells can also generate new neurons to replace old dysfunctional neurons.

The embryonic neural tube has three swellings as brain vesicles. These brain vesicles become the forebrain, midbrain, and hindbrain vesicles. A vesicle is around a chamber filled with fluid. The forebrain vesicle becomes a midline part including the diencephalon and hypothalamus. The lateral part becomes the cerebral vesicle that becomes the cerebral cortex and deep cerebral structures. The midbrain vesicle develops into the first part of the brainstem and four swellings. The hindbrain vesicle develops into the last part of the brainstem and the cerebellum.

The brain structures of other animals are similar to those of humans, except the part of the cerebral cortex. For example, the basic structures of the rat brain and the human brain are similar as in the typical mammalian brain. Because of such similarity, animal models including mouse and rat models are often used for studying disorders such as Parkinson’s disease. However, the human brain has a significantly more developed cerebral cortex with characteristics that other animals don’t have.

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Glia, Astrocytes, Oligodendrocytes, and Schwann Cells

Glia have a close relationship with neurons but this relationship has not been fully understood. Glia are essential partners to neurons and may influence many functions including neuronal and metabolic activities, neurotransmitter kinetics, as well as the blood flow. Glial activities are gradual and their impulses spread slower, compared with the fast signals generated by neurons. Recent studies found that the total number of neurons and the total number of glial cells are about the same.

The astrocytes are the major glial cells. One astrocyte can interact with multiple neurons to regulate the metabolism and neuron excitability, and to supply pre-processed food for the energy needed by neurons. Astrocytes are involved in the regulation of the cellular environment, the blood flow for the energy used by neurons, and the communication and signaling.

Oligodendrocytes are the cells that generate myelin around axons with layers of cell membranes. The gaps in the myelin sheath are called nodes. With the myelin sheaths, action potentials can jump between the nodes and make the signaling faster. Oligodendrocytes can also respond to synaptic events and generate spikes similar to action potentials. Such responses make the functional difference between neurons and glia unclear.

Schwann cells play the same role as the oligodendrocytes outside the central nervous system. They generate myelin around the axons of peripheral nerves. Similar to the conditions of oligodendrocytes in the central nervous system, there are nodes and gaps between the myelin sheath of peripheral nerve axons also. Schwann cells are also involved in the regulation of the extracellular environment and the neurotransmitter activities, especially at the junctions between the neural tissues and muscular tissues.

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Synapses, Gap Junctions, and Glia: Their Structures, Functions, and Features

Synapses are the regions between the terminal of an axon and the receptors on other cells. Neurotransmitters can bring the messages across the synapses. Synapses can be open or enclosed, large or small. Synaptic inputs can affect the neuron activities. The cells releasing neurotransmitters are presynaptic cells, and those binding to the neurotransmitters with membrane receptors are postsynaptic cells. Chemical communications can also happen outside synapses, e.g., receptors in the autonomic nervous system can be activated by substances in the blood.

Synapses have the feature of plasticity, i.e., they can be removed, generated, and changed. Such plasticity allows the functional adaptation and the development of the nervous system, including the functions of learning, memory, and recovery from damages. In fact, plasticity is the feature of the whole nervous system, just with variances in the extent. For example, the cerebral cortex has been found to be more plastic than the spinal cord.

The neurons are connected with each other with the small openings in their membranes, which are called gap junctions. Ions and electric currents can pass between the neurons via the gap junctions and have influences in the alterations of membrane potentials on connected neurons. The gap junction may also be involved in the chemical synaptic communications. Such coordination is critical for the cerebral cortex activities and may be involved in the neuronal basis of attention.

The other type of cells in the nervous system is glia. The glial cells play the role of functional partners of neurons. In addition to the nutritive and protective functions, they can also regulate the activities and communications of neurons. Different subtypes of glia have different functions. Some can protect against chemical alterations, some can change the features of neuronal signaling, and some are involved in immune regulations.

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Neurotransmitters and Their Functions

There are different types of neurotransmitters. For example, glutamate (glu) is involved in the excitation of the central nervous system (CNS). Aspartate (asp) is involved in the excitation of the brain and spinal cord. Gama-aminobutyric acid (GABA) is involved in the inhibition of the CNS. Glycine (gly) is involved in the inhibition in the spinal cord. In addition, acetylcholine (ACh) is involved in attention and autonomic activation. Dopamine (DA) is involved in movement and reward. Serotonin (5-HT) is involved in mood control, sensory processing, and relaxation.

The neurotransmitter noradrenaline (NA) is also known as norepinephrine (NE), and is involved in the control of smooth muscles and arousal. Substance P (SP) is involved in many functions including the signaling of pain. Opioids (Enk) are also involved in the regulation of pain, as well as satiety. Neuropeptide Y (NPY) is involved in the control of appetite. Adenosine triphosphate (ATP) is important for various functions.

The release of neurotransmitters can have ionotropic and/or metabotropic effects on the post-synaptic cell. The ionotropic effect means that upon activation the receptor can let the ions across the membrane. The metabotropic effect means that upon activation the receptor can stimulate biochemical signaling internally. A synapse can have both types of receptors, and some receptors have both functions. Metabotropic receptors may influence the gene expression and protein production, as well as the oscillating cycles of enzyme activities.

Ionotropic receptors associated with sodium channels will enable a neuron to fire. Those receptors associated with chloride channels may inhibit the firing to set the membrane potential back to the resting state. Post-synaptic potentials are the ultimate alterations in the membrane potentials, which can be excitatory (EPSP) or inhibitory (IPSP). Various synapses may have competing effects by reinforcing or opposing each other’s influences. The synaptic influences may be affected by the distance between a synapse and the axon hillock, as well as the thickness and shape of the dendrites.

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