Review Articles

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http://hdl.handle.net/2003/36302

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    Leibniz Research Centre for Working Environment and Human Factors
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    Pesticide toxicity
    (2018-11-08) Lushchak, Volodymyr I.; Matviishyn, Tetiana M.; Husak, Viktor V.; Storey, Janet M.; Storey, Kenneth B.
    Pesticides are known for their high persistence and pervasiveness in the environment, and along with products of their biotransformation, they may remain in and interact with the environment and living organisms in multiple ways, according to their nature and chemical structure, dose and targets. In this review, the classifications of pesticides based on their nature, use, physical state, pathophysiological effects, and sources are discussed. The effects of these xenobiotics on the environment, their biotransformation in terms of bioaccumulation are highlighted with special focus on the molecular mechanisms deciphered to date. Basing on targeted organisms, most pesticides are classified as herbicides, fungicides, and insecticides. Herbicides are known as growth regulators, seedling growth inhibitors, photosynthesis inhibitors, inhibitors of amino acid and lipid biosynthesis, cell membrane disrupters, and pigment biosynthesis inhibitors, whereas fungicides include inhibitors of ergosterol biosynthesis, protein biosynthesis, and mitochondrial respiration. Insecticides mainly affect nerves and muscle, growth and development, and energy production. Studying the impact of pesticides and other related chemicals is of great interest to animal and human health risk assessment processes since potentially everyone can be exposed to these compounds which may cause many diseases, including metabolic syndrome, malnutrition, atherosclerosis, inflammation, pathogen invasion, nerve injury, and susceptibility to infectious diseases. Future studies should be directed to investigate influence of long term effects of low pesticide doses and to minimize or eliminate influence of pesticides on non-target living organisms, produce more specific pesticides and using modern technologies to decrease contamination of food and other goods by pesticides.
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    lncRNA involvement in hepatocellular carcinoma metastasis and prognosis
    (2018-09-04) Abbastabar, Maryam; Sarfi, Mohammad; Golestani, Abolfazl; Khalili, Ehsan
    Eukaryotic lncRNAs are RNA molecules defined to be greater than 200 bp in length that are not translated to a protein and operate through several mechanisms, including participating in chromatin remodeling and methylation, influencing the integrity and stability of proteins and complexes, or acting as a sponge for miRNA inhibition. A number of recent studies have concentrated on the relationship between long non-coding RNAs (lncRNAs) and cancer. Hepatocellular carcinoma (HCC) is the most prevalent histological type of liver tumors, accounting for about 80 % of the cases worldwide. Lack of proper molecular markers for diagnosis of HCC and treatment evaluation is a significant problem. Dysregulated expression of HCC-related lncRNAs such as MEG-3, MALAT1, HULC, HOTAIR, and H19 have been identified and closely related with tumorigenesis, metastasis, prognosis and diagnosis. In this review, we summarized recent highlighted functions and molecular mechanisms of the most extensively studied lncRNAs in the pathophysiology of hepatocellular carcinoma and their potential for serving as probable therapeutic targets.
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    Unraveling the bioactivity of anticancer peptides as deduced from machine learning
    (2018-07-25) Shoombuatong, Watshara; Schaduangrat, Nalini; Nantasenamt, Chanin
    Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.
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    Is autophagy associated with diabetes mellitus and its complications?
    (2018-07-24) Bhattacharya, Debalina; Mukhopadhyay, Mainak; Bhattacharyya, Maitree; Karmakar, Parimal
    Diabetes mellitus (DM) is an endocrine disorder. In coming decades it will be one of the leading causes of death globally. The key factors in the pathogenesis of diabetes are cellular injuries and disorders of energy metabolism leading to severe diabetic complications. Recent studies have confirmed that autophagy plays a pivotal role in diabetes and its complications. It has been observed that autophagy regulates the normal function of pancreatic β cells and insulin-target tissues, such as skeletal muscle, liver, and adipose tissue. This review will summarize the regulation of autophagy in diabetes and its complications, and explore how this process would emerge as a potential therapeutic target for diabetes treatment.
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    Towards understanding aromatase inhibitory activity via QSAR modeling
    (2018-07-20) Shoombuatong, Watshara; Schaduangrat, Nalini; Nantasenamat, Chanin
    Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.
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    Natural products as reservoirs of novel therapeutic agents
    (2018-05-04) Mushtaq, Sadaf; Abbasi, Bilal Haider; Uzair, Bushra; Abbasi, Rashda
    Since ancient times, natural products from plants, animals, microbial and marine sources have been exploited for treatment of several diseases. The knowledge of our ancestors is the base of modern drug discovery process. However, due to the presence of extensive biodiversity in natural sources, the percentage of secondary metabolites screened for bioactivity is low. This review aims to provide a brief overview of historically significant natural therapeutic agents along with some current potential drug candidates. It will also provide an insight into pros and cons of natural product discovery and how development of recent approaches has answered the challenges associated with it.
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    Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy
    (2018-03-26) Thippakorn, Chadinee; Schaduangrat, Nalini; Nantasenamat, Chanin
    Diabetes is associated with numerous metabolic and vascular risk factors that contribute to a high rate of microvascular and macro-vascular disorders leading to mortality and morbidity from diabetic complications. In this case, the major cause of death in overall diabetic patients results from diabetic nephropathy (DN) or renal failure. The risk factors and mechanisms that correspond to the development of DN are not fully understood and so far, no specific and sufficient diagnostic biomarkers are currently available other than micro- or macroalbuminuria. Therefore, this review describes current and novel protein biomarkers in the context of DN as well as probable proteins biomarkers associated with pathological processes for the early stage of DN via proteomics data together with bioinformatics. In addition, the mechanisms involved in early development of diabetic vascular disorders and complications resulting from glucose induced oxidative stress will also be explored.
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    Somatopause, weaknesses of the therapeutic approaches and the cautious optimism based on experimental ageing studies with soy isoflavones
    (2018-03-21) Ajdžanovic, Vladimir; Trifunović, Svetlana; Miljić, Dragana; Šošić-Jurjević, Branka; Filipović, Branko; Miler, Marko; Ristić, Nataša; Manojlović-Stojanoski, Milica; Milošević, Verica
    The pathological phenomenon of somatopause, noticeable in hypogonadal ageing subjects, is based on the growth hormone (GH) production and secretion decrease along with the fall in GH binding protein and insulin-like growth factor 1 (IGF-1) levels, causing different musculoskeletal, metabolic and mental issues. From the perspective of safety and efficacy, GH treatment is considered to be highly controversial, while some other therapeutic approaches (application of IGF-1, GH secretagogues, gonadal steroids, cholinesterase-inhibitors or various combinations) exhibit more or less pronounced weaknesses in this respect. Soy isoflavones, phytochemicals that have already demonstrated the health benefits in treated elderly, at least experimentally reveal their potential for the somatopausal symptoms remediation. Namely, genistein enhanced GHRH-stimulated cAMP accumulation and GH release in rat anterior pituitary cells; refreshed and stimulated the somatotropic system (hypothalamic nuclei and pituitary GH cells) function in a rat model of the mild andropause, and stimulated the GH output in ovariectomized ewes as well as the amplitude of GH pulses in the rams. Daidzein, on the other hand, increased body mass, trabecular bone mass and decreased bone turnover in the animal model of severe andropause, while both isoflavones demonstrated blood cholesterol-lowering effect in the same model. These data, which necessarily need to be preclinically and clinically filtered, hint some cautious optimism and call for further innovative designing of balanced soy isoflavone-based therapeutics.
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    Microsatellite instability in colorectal cancer
    (2018-01-22) Nojadeh, Jafar Nouri; Sharif, Shahin Behrouz; Sakhinia, Ebrahim
    Colorectal cancer (CRC) is a heterogeneous disease that is caused by the interaction of genetic and environmental factors. Although it is one of the most common cancers worldwide, CRC would be one of the most curable cancers if it is detected in the early stages. Molecular changes that occur in colorectal cancer may be categorized into three main groups: 1) Chromosomal Instability (CIN), 2) Microsatellite Instability (MSI), and 3) CpG Island Methylator phenotype (CIMP). Microsatellites, also known as Short Tandem Repeats (STRs) are small (1-6 base pairs) repeating stretches of DNA scattered throughout the entire genome and account for approximately 3 % of the human genome. Due to their repeated structure, microsatellites are prone to high mutation rate. Microsatellite instability (MSI) is a unique molecular alteration and hyper-mutable phenotype, which is the result of a defective DNA mismatch repair (MMR) system, and can be defined as the presence of alternate sized repetitive DNA sequences which are not present in the corresponding germ line DNA. The presence of MSI is found in sporadic colon, gastric, sporadic endometrial and the majority of other cancers. Approximately, 15-20 % of colorectal cancers display MSI. Determination of MSI status in CRC has prognostic and therapeutic implications. As well, detecting MSI is used diagnostically for tumor detection and classification. For these reasons, microsatellite instability analysis is becoming more and more important in colorectal cancer patients. The objective of this review is to provide the comprehensive summary of the update knowledge of colorectal cancer classification and diagnostic features of microsatellite instability.
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    Data mining for the identification of metabolic syndrome status
    (2018-01-10) Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin
    Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/ understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.