Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease.

BACKGROUND: The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific. OBJECTIVE: Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature. METHODS: Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls. RESULTS: The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4). CONCLUSIONS: This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.

RPPA: Origins, Transition to a Validated Clinical Research Tool, and Next Generations of the Technology.

RPPA technology has graduated from a research tool to an essential component of clinical drug discovery research and personalized medicine. Next generations of RPPA technology will be a single clinical instrument that integrates all the steps of the workflow.

Combining the "Sibling Technologies" of Laser Capture Microdissection and Reverse Phase Protein Microarrays.

Reverse phase protein microarrays (RPPA) and laser capture microdissection (LCM) are "sibling" technologies that originated from the same laboratory to overcome the challenge of quantifying low-abundance proteins in heterogeneous tissues. Combining both technologies provides both unique opportunities and unique challenges. Enabling the unprecedented resolution of the activation state of labile biomarkers, such as phosphorylated cell signaling proteins, has had a substantial impact on our understanding of diseases and is playing a significant role in clinical trials. At the same time, quantifying proteins at this sensitivity in very small amounts of material requires cognizance of pre-analytical variability and the limits of downstream detection technologies. Here, we discuss both the potential that the combination of both technologies presents and the potential pitfalls that must be navigated.

Proteome microarray technology and application: higher, wider, and deeper.

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.

Where are we going with gene screening for male infertility?

Male infertility is a heterogenous disease process requiring the proper functioning and interaction of thousands of genes. Given the number of genes involved, it is thought that genetic causes contribute to most cases of infertility. Identifying these causes, however, is challenging. Infertility is associated with negative health outcomes, such as cancer, highlighting the need to further understand the genetic underpinnings of this condition. This paper describes the genetic and genomic tests currently available to identify the etiology of male infertility and then will discuss emerging technologies that may facilitate diagnosis and treatment of in the future.

A decade of Nucleic Acid Programmable Protein Arrays (NAPPA) availability: News, actors, progress, prospects and access.

Understanding the dynamic of the proteome is a critical challenge because it requires high sensitive methodologies in high-throughput formats in order to decipher its modifications and complexity. While molecular biology provides relevant information about cell physiology that may be reflected in post-translational changes, High-Throughput (HT) experimental proteomic techniques are essential to provide valuable functional information of the proteins, peptides and the interconnections between them. Hence, many methodological developments and innovations have been reported during the last decade. To study more dynamic protein networks and fine interactions, Nucleic Acid Programmable Protein Arrays (NAPPA) was introduced a decade ago. The tool is rapidly maturing and serving as a gateway to characterize biological systems and diseases thanks primarily to its accuracy, reproducibility, throughput and flexibility. Currently, NAPPA technology has proved successful in several research areas adding valuable information towards innovative diagnostic and therapeutic applications. Here, the basic and latest advances within this modern technology in basic, translational research are reviewed, in addition to presenting its exciting new directions. Our final goal is to encourage more scientists/researchers to incorporate this method, which can help to remove bottlenecks in their particular research or biomedical projects. SIGNIFICANCE: Nucleic Acid Programmable Protein Arrays (NAPPA) is becoming an essential tool for functional proteomics and protein-protein interaction studies. The technology impacts decisively on projects aiming massive screenings and the latest innovations like the multiplexing capability or printing consistency make this a promising method to be integrated in novel and combinatorial proteomic approaches.

Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data.

BACKGROUND: Rotator cuff tear (RCT) is a common shoulder disorder in the elderly. Muscle atrophy, denervation and fatty infiltration exert secondary injuries on torn rotator cuff muscles. It has been reported that satellite cells (SCs) play roles in pathogenic process and regenerative capacity of human RCT via regulating of target genes. This study aims to complement the differentially expressed genes (DEGs) of SCs that regulated between the torn supraspinatus (SSP) samples and intact subscapularis (SSC) samples, identify their functions and molecular pathways. METHODS: The gene expression profile GSE93661 was downloaded and bioinformatics analysis was made. RESULTS: Five hundred fifty one DEGs totally were identified. Among them, 272 DEGs were overexpressed, and the remaining 279 DEGs were underexpressed. Gene ontology (GO) and pathway enrichment analysis of target genes were performed. We furthermore identified some relevant core genes using gene-gene interaction network analysis such as GNG13, GCG, NOTCH1, BCL2, NMUR2, PMCH, FFAR1, AVPR2, GNA14, and KALRN, that may contribute to the understanding of the molecular mechanisms of secondary injuries in RCT. We also discovered that GNG13/calcium signaling pathway is highly correlated with the denervation atrophy pathological process of RCT. CONCLUSION: These genes and pathways provide a new perspective for revealing the underlying pathological mechanisms and therapy strategy of RCT.

Technical aspects of microphysiological systems (MPS) as a promising wet human-in-vivo simulator.

Microphysiological systems (MPS) are currently attracting a lot of interest from pharmaceutical companies worldwide. In the United States and European Union, several large government projects related to MPS have been initiated, and, in Japan, pharmaceutical companies interested in MPS are watching the recent trends and developments in the field. In July 2017, the Japan Agency for Medical Research and Development initiated a research program to develop chip-based MPS. In this review, we examine the technical aspects of commercializing chip-based MPS.

Role of Proteomics in the Development of Personalized Medicine.

Advances in proteomic technologies have made import contribution to the development of personalized medicine by facilitating detection of protein biomarkers, proteomics-based molecular diagnostics, as well as protein biochips and pharmacoproteomics. Application of nanobiotechnology in proteomics, nanoproteomics, has further enhanced applications in personalized medicine. Proteomics-based molecular diagnostics will have an important role in the diagnosis of certain conditions and understanding the pathomechanism of disease. Proteomics will be a good bridge between diagnostics and therapeutics; the integration of these will be important for advancing personalized medicine. Use of proteomic biomarkers and combination of pharmacoproteomics with pharmacogenomics will enable stratification of clinical trials and improve monitoring of patients for development of personalized therapies. Proteomics is an important component of several interacting technologies used for development of personalized medicine, which is depicted graphically. Finally, cancer is a good example of applications of proteomic technologies for personalized management of cancer.

Application of iChip to Grow "Uncultivable" Microorganisms and its Impact on Antibiotic Discovery.

PURPOSE: Antibiotics have revolutionized modern medicine, allowing significant progress in healthcare and improvement in life expectancy. Development of antibiotic resistance by pathogenic bacteria is a natural phenomenon; however, the rate of antibiotic resistance emergence is increasing at an alarming rate, due to indiscriminate use of antibiotics in healthcare, agriculture and even everyday products. Traditionally, antibiotic discovery has been conducted by screening extracts of microorganisms for antimicrobial activity. However, this conventional source has been over-used to such an extent that it poses the risk of "running out" of new antibiotics. Aiming to increase access to a greater diversity of microorganisms, a new cultivation method with an in situ approach called iChip has been designed. The iChip has already isolated many novel organisms, as well as Teixobactin, a novel antibiotic with significant potency against gram-positive bacteria.

An ultralow background substrate for protein microarray technology.

We herein report an ultralow background substrate for protein microarrays. Conventional protein microarray substrates often suffer from non-specific protein adsorption and inhomogeneous spot morphology. Consequently, surface treatment and a suitable printing solution are required to improve the microarray performance. In the current work, we improved the situation by developing a new microarray substrate based on a fluorinated ethylene propylene (FEP) membrane. A polydopamine microspot array was fabricated on the FEP membrane, with proteins conjugated to the FEP surface through polydopamine. Uniform microspots were obtained on FEP without the application of a special printing solution. The modified FEP membrane demonstrated ultralow background signal and was applied in protein and peptide microarray analysis.

Prenatal genetic care: debates and considerations of the past, present and future.

After karyotyping invasively obtained fetal material for decades, the field of prenatal genetic care has changed tremendously since the turn of the century. The introduction of novel technologies and strategies went along with concerns and debates, in which key issues were costs, the finding of variants of unknown or uncertain clinical relevance, commercialization and ethical and social issues. At present, there is an explosion of new genomic technologies, which need critical assessment prior to implementation, especially in the prenatal field. The key issues of the debates we had in the past will again play a major role in guiding us toward careful implementation of these new techniques in future.

Self-assembled two-dimensional protein arrays in bionanotechnology: from S-layers to designed lattices.

Although the crystalline S-layer arrays that form the exoskeleton of many archaea and bacteria have been studied for decades, a long-awaited crystal structure coupled with a growing understanding of the S-layer assembly process are injecting new excitement in the field. The trend is amplified by computational strategies that allow for in silico design of protein building blocks capable of self-assembling into 2D lattices and other prescribed quaternary structures. We review these and other recent developments toward achieving unparalleled control over the geometry, chemistry and function of protein-based 2D objects from the nanoscale to the mesoscale.

Analysis, biomedicine, collaboration, and determinism challenges and guidance: wish list for biopharmaceuticals on the interface of computing and statistics.

I have personally witnessed processing advance from desk calculators and mainframes, through timesharing and PCs, to supercomputers and cloud computing. I have also witnessed resources grow from too little data into almost too much data, and from theory dominating data into data beginning to dominate theory while needing new theory. Finally, I have witnessed problems advance from simple in a lone discipline into becoming almost too complex in multiple disciplines, as well as approaches evolve from analysis driving solutions into solutions by data mining beginning to drive the analysis itself. How we do all of this has transitioned from competition overcoming collaboration into collaboration starting to overcome competition, as well as what is done being more important than how it is done has transitioned into how it is done becoming as important as what is done. In addition, what or how we do it being more important than what or how we should actually do it has shifted into what or how we should do it becoming just as important as what or how we do it, if not more so. Although we have come a long way in both our methodology and technology, are they sufficient for our current or future complex and multidisciplinary problems with their massive databases? Since the apparent answer is not a resounding yes, we are presented with tremendous challenges and opportunities. This personal perspective adapts my background and experience to be appropriate for biopharmaceuticals. In these times of exploding change, informed perspectives on what challenges should be explored with accompanying guidance may be even more valuable than the far more typical literature reviews in conferences and journals of what has already been accomplished without challenges or guidance. Would we believe that an architect who designs a skyscraper determines the skyscraper's exact exterior, interior and furnishings or only general characteristics? Why not increase dependability of conclusions in genetics and translational medicine by enriching genetic determinism with uncertainty? Uncertainty is our friend if exploited or potential enemy if ignored. Genes design proteins, but they cannot operationally determine all protein characteristics: they begin a long chain of complex events occurring many times via intricate feedbacks plus interactions which are not all determined. Genes influence proteins and diseases by just determining their probability distributions, not by determining them. From any sample of diseased people, we may more successfully infer gene probability distributions than genes themselves, and it poses an issue to resolve. My position is supported by 2-3 articles a week in ScienceDaily, 2011.

Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis.

INTRODUCTION: Sepsis is a complex immunological response to infection characterized by early hyper-inflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on Systemic Inflammatory Response Syndrome (SIRS) differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. METHODS: This was a multi-centre, prospective clinical trial conducted across four tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n = 27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n = 38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n = 20). Each participant had minimally 5 ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. RESULTS: Based on preliminary microarray analyses comparing HC and sepsis groups, a panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. The area under the curve (AUC) receiver operator characteristics (ROC) curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 and 92%. CONCLUSIONS: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients.

Single-walled carbon nanotubes as optical materials for biosensing.

In this review, we summarize recent progress in the development of single-walled carbon nanotubes (SWNTs) as optical materials for biosensing applications. First, as optical labels, we discuss the use of SWNTs in Raman-based protein detection. Strong and simple resonance Raman spectroscopy of SWNTs opens up a method of protein microarray with detection sensitivity down to femtomolar range. Also, tunable isotopic SWNT-Raman signature enables the simultaneous detection of multiple analytes in complex fluids. Second, the photoluminescence properties of SWNTs are also explored. We examine fluorescence biosensors that integrate the quenching property of SWNTs and the recognition property of functional nucleic acids. Particularly, SWNTs are established as an efficient signal transduction substrate in different biosensing systems, including the detection of specific proteins and DNA sequences, regulation of singlet oxygen generation and label-free fluorescence assays, and all have exhibited very high selectivity and sensitivity.

Protein microarrays for systems biology.

Systems biology holds the key for understanding biological systems on a system level. It eventually holds the key for the treatment and cure of complex diseases such as cancer, diabetes, obesity, mental disorders, and many others. The '-omics' technologies, such as genomics, transcriptomics, proteomics, and metabonomics, are among the major driving forces of systems biology. Featured as high-throughput, miniaturized, and capable of parallel analysis, protein microarrays have already become an important technology platform for systems biology. In this review, we will focus on the system level or global analysis of biological systems using protein microarrays. Four major types of protein microarrays will be discussed: proteome microarrays, antibody microarrays, reverse-phase protein arrays, and lectin microarrays. We will also discuss the challenges and future directions of protein microarray technologies and their applications for systems biology. We strongly believe that protein microarrays will soon become an indispensable and invaluable tool for systems biology.