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Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.
Seven years passed since the discovery of lin−4’s unique properties and then, around the turn of the millennium, the research floodgates opened. This chapter tracks the nascent field of microRNA research, the frenetic race to discover and catalogue new microRNAs and find the organisms in which they were made. MicroRNAs held a prominent position in evolution, their number and diversity expanding at key transitions to more complex life, including for our own species, Homo sapiens. MicroRNAs, it would become clear, are the genome’s solution to how to control the natural fluctuations, randomness and noise in gene expression. The chapter also covers the pivotal experiments that laid the ground rules for how microRNAs work and revealed their effects on gene expression. Along the way, a selection of the scientific toolkit gets special attention, including some of the models used to find microRNAs and the technologies that would prove that microRNAs, despite their small size and limited number in genomes, controlled the vast majority of gene activity in our cells.
Seed germination is a pivotal period of plant growth and development. This process can be divided into four major stages, swelling absorption, seed coat dehiscence, radicle emergence and radicle elongation. Cupressus gigantea, a tree native to Tibet, China, is characterized by its resistance to stresses such as cold, and drought, and has a high economic and ecological value. Nevertheless, given its unique geographic location, its seeds are difficult to germinate. Therefore, it is crucial to explore the mechanisms involved in seed germination in this species to improve the germination efficiency of its seeds, thereby protecting this high-quality resource. Here, our findings indicate that seed germination was enhanced when exposed to a 6-h/8-h light/dark photoperiod, coupled with a temperature of 20°C. Furthermore, the application of exogenous GA3 (1 mg/ml, about 2.9 mM) stimulated the germination of C. gigantea seeds. Subsequently, proteomics was used to detect changes in protein expression during the four stages of seed germination. We identified 34 differentially expressed proteins (DEPs), including 13 at the radicle pre-emergence stage, and 17 at the radicle elongation stage. These DEPs were classified into eight functional groups, cytoskeletal proteins, energy metabolism, membrane transport, stress response, molecular chaperones, amino acid metabolism, antioxidant system and ABA signalling pathway. Most of them were found to be closely associated with amino acid metabolism. Combined, these findings indicate that, along with temperature and light, exogenous GA3 can increase the germination efficiency of C. gigantea seeds. Our study also offers insights into the changes in protein expression patterns in C. gigantea seeds during germination.
The aim of this experiment was to investigate the differential proteomic characteristics of milk from high- and low-yielding Guanzhong dairy goats during the peak lactation period under the same feeding conditions. Nine Guanzhong dairy goats with high yield (H: 3.5 ± 0.17 kg/d) and nine with low yield (L:1.2 ± 0.25 kg/d) were selected for milk proteomic analysis using tandem mass tag technology. A total of 78 differentially expressed proteins were identified. Compared with L, 50 proteins including HK3, HSPB1 and ANXA2 were significantly upregulated in H milk, while 28 proteins including LALBA and XDH were significantly downregulated. Bioinformatics analysis of the differentially expressed proteins showed that galactose metabolism, purine metabolism, glycolysis/gluconeogenesis, MAPK signaling pathway, regulation of actin cytoskeleton and other pathways were closely related to milk yield. HK3, HSPB1, ANXA2, LALBA and XDH were important candidate proteins associated with the milk production characteristics of Guanzhong dairy goats. Our data provide relevant biomarkers and a theoretical basis for improving milk production in Guanzhong dairy goats.
Precision oncology is a rapidly evolving concept that holds great promise in cancer treatment. However, a cancer complexity attributed to genomic and acquired tumour heterogeneity limits treatment effectiveness and increases toxicity. These limitations refer to both systemic therapies and radiotherapy, which are two mainstays of non-invasive cancer treatment. By understanding cancer heterogeneity and utilising advanced tools to personalise treatment strategies, precision oncology has the potential to revolutionise cancer care. In this article, we review the current status of precision oncology in solid tumours, specifically focusing on the impact of tumour heterogeneity and genomic patient features on systemic therapies and radiation. We also discuss the implementation of novel tools, such as next-generation sequencing and liquid biopsies, to overcome this problem.
Edited by
Xiuzhen Huang, Cedars-Sinai Medical Center, Los Angeles,Jason H. Moore, Cedars-Sinai Medical Center, Los Angeles,Yu Zhang, Trinity University, Texas
Ideal healthcare should provide prevention and treatment strategies in the context of individual variability. The promise of genomics and big data for understanding the complex disease etiology and development of treatment strategies for translating research findings in a laboratory setting to the bedside requires a paradigm shift in how we conduct biomedical research. The take-home message from the Human Genome Sequencing Project is the need for a bold vision, even in the absence of a clear path. The No-Boundary Thinking (NBT) approach that advocates a scientific dialogue among individuals with varying expertise in a “discipline-free” manner at the problem definition stage is a pragmatic approach to leverage big data for precision medicine. Genomics big data as it pertains to understanding the molecular function of genes and proteins is discussed in this chapter. We also discuss the challenges in the adoption of NBT to genomics research.
In the years following FDA approval of direct-to-consumer, genetic-health-risk/DTCGHR testing, millions of people in the US have sent their DNA to companies to receive personal genome health risk information without physician or other learned medical professional involvement. In Personal Genome Medicine, Michael J. Malinowski examines the ethical, legal, and social implications of this development. Drawing from the past and present of medicine in the US, Malinowski applies law, policy, public and private sector practices, and governing norms to analyze the commercial personal genome sequencing and testing sectors and to assess their impact on the future of US medicine. Written in relatable and accessible language, the book also proposes regulatory reforms for government and medical professionals that will enable technological advancements while maintaining personal and public health standards.
In the years following FDA approval of direct-to-consumer, genetic-health-risk/DTCGHR testing, millions of people in the US have sent their DNA to companies to receive personal genome health risk information without physician or other learned medical professional involvement. In Personal Genome Medicine, Michael J. Malinowski examines the ethical, legal, and social implications of this development. Drawing from the past and present of medicine in the US, Malinowski applies law, policy, public and private sector practices, and governing norms to analyze the commercial personal genome sequencing and testing sectors and to assess their impact on the future of US medicine. Written in relatable and accessible language, the book also proposes regulatory reforms for government and medical professionals that will enable technological advancements while maintaining personal and public health standards.
In the years following FDA approval of direct-to-consumer, genetic-health-risk/DTCGHR testing, millions of people in the US have sent their DNA to companies to receive personal genome health risk information without physician or other learned medical professional involvement. In Personal Genome Medicine, Michael J. Malinowski examines the ethical, legal, and social implications of this development. Drawing from the past and present of medicine in the US, Malinowski applies law, policy, public and private sector practices, and governing norms to analyze the commercial personal genome sequencing and testing sectors and to assess their impact on the future of US medicine. Written in relatable and accessible language, the book also proposes regulatory reforms for government and medical professionals that will enable technological advancements while maintaining personal and public health standards.
In the years following FDA approval of direct-to-consumer, genetic-health-risk/DTCGHR testing, millions of people in the US have sent their DNA to companies to receive personal genome health risk information without physician or other learned medical professional involvement. In Personal Genome Medicine, Michael J. Malinowski examines the ethical, legal, and social implications of this development. Drawing from the past and present of medicine in the US, Malinowski applies law, policy, public and private sector practices, and governing norms to analyze the commercial personal genome sequencing and testing sectors and to assess their impact on the future of US medicine. Written in relatable and accessible language, the book also proposes regulatory reforms for government and medical professionals that will enable technological advancements while maintaining personal and public health standards.
We studied the genetic polymorphism of beta-lactoglobulin (β-Lg) whey protein in Gangatiri zebu cows for this Research Communication. The polymorphic nature of milk protein fractions and their association with milk production traits, composition and quality has attracted several efforts in evaluating the allelic distribution of protein locus as a potential dairy trait marker. Genetic variants of β-Lg have highly significant effects on casein number (B > A) and protein recovery (B > A) and also determine the yield of cheese dry matter (B > A). Molecular techniques of polyacrylamide gel electrophoresis and high-resolution accurate mass-spectroscopy were applied to characterize the β-Lg protein obtained from the Gangatiri breed milk. Sequence analysis of β-Lg showed the presence of variant B having UniProt database accession number P02754, coded on the PAEP gene. Our study can provide reference and guidance for the selection of superior milk (having β-LgB) from this indigenous breed that could potentially give a good yield of β-Lg for industrial applications.
Difficulties in the diagnosis of bipolar disorder (BD) are associated with a lack of understanding of the mechanisms of its pathogenesis. Identification of proteins involved in the pathogenesis of BD will bring us closer to an understanding of its mechanisms and can help in diagnosis.
Objectives
The search of proteomic biomarkers of bipolar disorder.
Methods
We performed a proteomic analysis of the serum of 16 healthy people and 33 patients with BD. Patients were hospitalized in an acute state of the depressive phase, and they did not receive therapy for more than 6 months. Blood was collected before the start of therapy. Serum was purified from major proteins by affinity chromatographyandseparatedby1D-electrophoresis. After trypsinolysis, the proteins were identified by HPLC/mass spectrometry. The ELISA kit was used to determine the amount of zNMDAR1.
Results
We identified a protein that does not occur in healthy people: a subunit of the glutamate NMDA receptor zeta-1 (zNMDAR1). As a result, we found a statistically significant (p = 0.037) almost fivefold increase in the concentration of this protein in the serum of patients with bipolar disorder (0.64 [0.18; 0.78] ng/ml) compared with healthy individuals.
Conclusions
Thus, in bipolar disorder NMDAR is damaged, which can lead appearance of their subunits in the serum, and which indicated a violation of glutamatergic neurotransmission. Then this protein claims the role of markers of bipolar disorder. Mass spectrometric analysis was carried out of the “Human Proteome” Core Facility of the Institute of Biomedical Chemistry Moscow. RSW project, state registration number AAAA-A19-119020690013-2.
The relevance of this study is determined by the need to search for biological markers of schizophrenia. The detection and validation of such molecules can become the basis for the creation of additional paraclinical diagnostic methods or contribute to the creation of targets for individual pharmacotherapy, which is an important task of modern fundamental medicine.
Objectives
Comparative proteomic analysis of serum in schizophrenic patients with positive and negative symptoms.
Methods
The study includes 10 healthy donors and 27 patients with schizophrenia. Samples preparation included: serum purification from major proteins via affinity chromatography, 1D-PAGE proteins separation, in-gel tryptic hydrolysis, LC-MS/MS mass-spectrometry (Orbitrap Q-exactive HF mass spectrometer, Agilent Technologies). Identification of proteins was carried out using Mascot software Ver. 2.1 («Matrix Science», USA). Proteins for quantitative analysis were selected in view of the DISGENET database. Quantitative LC-MS-SRM analysis of selected protein was performed on QQQ TSQ Vantage (Thermo Scientific) with labeled peptide standards.
Results
Receptor-interacting serine/threonine-protein kinase 1 was selected for quantitative assessment. Significant differences were revealed in the RIPK1 concentrations in the serum of schizophrenic patients with negative and positive symptoms (p=0.02). The serum concentration of RIPK1 in patients with negative symptoms is tenfold in patients with positive symptoms.
Conclusions
Receptor-interacting serine/threonine-protein kinase 1 can be considered a biomarker of negative symptoms of schizophrenia based on a significant increase in serum concentration. Mass spectrometric analysis was carried out of the “Human Proteome” Core Facility of the Institute of Biomedical Chemistry Moscow. Support by Grant of RSF № 18-15-00053P.
Preterm birth (PTB) is one of the leading causes of deaths in infants under the age of five. Known risk factors of PTB include genetic factors, lifestyle choices or infection. Identification of omic biomarkers associated with PTB could aid clinical management of women at high risk of early labour and thereby reduce neonatal morbidity. This systematic literature review aimed to identify and summarise maternal omic and multi-omic (genomics, transcriptomics, proteomics and metabolites) biomarker studies of PTB. Original research articles were retrieved from three databases: PubMed, Web of Science and Science Direct, using specified search terms for each omic discipline. PTB studies investigating genomics, transcriptomics, proteomics or metabolomics biomarkers prior to onset of labour were included. Data were collected and reviewed independently. Pathway analyses were completed on the biomarkers from non-targeted omic studies using Reactome pathway analysis tool. A total of 149 omic studies were identified; most of the literature investigated proteomic biomarkers. Pathway analysis identified several cellular processes associated with the omic biomarkers reported in the literature. Study heterogeneity was observed across the research articles, including the use of different gestation cut-offs to define PTB. Infection/inflammatory biomarkers were identified across majority of papers using a range of targeted and non-targeted approaches.
The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms.
Methods
The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA).
Results
Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster.
Conclusions
Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.
Maize is one of the three staple foods in the world. The white variety represents 60% of the maize importation with a world consumption of 1125 million tons in 2019/2020. Currently, new technologies could contribute to the analysis of this seed, supporting quality control and improvement. This study aims to carry out the morphological and proteomic comparison between the hybrid MR2008 and its parental lines LUG03 and CML491 through mass spectrometry and bioinformatics analysis. Herein, we identified that 34.8% of the hybrid proteome differs from the parental proteome. Also, ontological and morphological analyses determined that the hybrid exhibits more characteristics related to CML491 than LUG03, for example, metabolic pathways and enzymes, such as anthocyanidin 3-O-glucosyltransferase (UniProt P16166). This analysis allowed the identification of dominant characters, metabolic pathways and confirms the utility of this methodology in agricultural practices, mainly in processes of selection and quality control of a crop.
Leishmaniasis is an infectious disease in which different clinical manifestations are classified into three primary forms: visceral, cutaneous and mucocutaneous. These disease forms are associated with parasite species of the protozoan genus Leishmania. For instance, Leishmania infantum and Leishmania tropica are typically linked with visceral (VL) and cutaneous (CL) leishmaniasis, respectively; however, these two species can also cause other form to a lesser extent. What is more alarming is this characteristic, which threatens current medical diagnosis and treatment, is started to be acquired by other species. Our purpose was to address this issue; therefore, gel-based and gel-free proteomic analyses were carried out on the species L. infantum to determine the proteins differentiating between the parasites caused VL and CL. In addition, L. tropica parasites representing the typical cases for CL were included. According to our results, electrophoresis gels of parasites caused to VL were distinguishable regarding the repetitive down-regulation on some specific locations. In addition, a distinct spot of an antioxidant enzyme, superoxide dismutase, was shown up only on the gels of CL samples regardless of the species. In the gel-free approach, 37 proteins that were verified with a second database search using a different search engine, were recognized from the comparison between VL and CL samples. Among them, 31 proteins for the CL group and six proteins for the VL group were determined differentially abundant. Two proteins from the gel-based analysis, pyruvate kinase and succinyl-coA:3-ketoacid-coenzyme A transferase analysis were encountered in the protein list of the CL group.
Plasmodium falciparum is the main cause of severe malaria in humans that can lead to death. There is growing evidence of drug-resistance in P. falciparum treatment, and the design of effective vaccines remains an ongoing strategy to control the disease. On the other hand, the recognition of specific diagnostic markers for P. falciparum can accelerate the diagnosis of this parasite in the early stages of infection. Therefore, the identification of novel antigenic proteins especially by proteomic tools is urgent for vaccination and diagnosis of P. falciparum. The proteome diversity of the life cycle stages of P. falciparum, the altered proteome of P. falciparum-infected human sera and altered proteins in P. falciparum-infected erythrocytes could be proposed as appropriate proteins for the aforementioned aims. Accordingly, this review highlights and proposes different proteins identified using proteomic approaches as promising markers in the diagnosis and vaccination of P. falciparum. It seems that most of the candidates identified in this study were able to elicit immune responses in the P. falciparum-infected hosts and they also played major roles in the life cycle, pathogenicity and key pathways of this parasite.
Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of ‘big data’ and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.
Two major outstanding questions in microbiome research ask what microbes are present in a community and how they interact with each other and their hosts. Recent, rapid improvements in nucleic acid (DNA and RNA) sequencing allow us to study the composition and function of microbiomes in unprecedented detail, leading to a step change in our understanding of host–microbe interactions. This chapter gives a broad overview of the basic toolkit available to modern microbiologists and microbial ecologists, exploring their application to key questions about microbiome structure and function. We cover tools based on nucleic acid sequencing (e.g. amplicon sequencing, metagenomics, metatranscriptomics) as well as approaches targeting larger molecules such as metabolomics and proteomics. We discuss the use of microbial culture as a means of measuring functional capacity of individual microbes, or building artificial communities to understand emergent properties of consortia. We emphasise the advantages of combining multiple techniques alongside robust experimental design to garner powerful quantitative estimates of microbiome structure, and how this relates to host–microbe interactions.