Clinical proteomics: promises, challenges and limitations of affinity arrays

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Gut Microbiome Proteomics in Food Allergies

Food allergies (FA) have dramatically increased in recent years, particularly in developed countries. It is currently well-established that food tolerance requires the strict maintenance of a specific microbial consortium in the gastrointestinal (GI) tract microbiome as alterations in the gut microbiota can lead to dysbiosis, causing inflammation and pathogenic intestinal conditions that result in the development of FA. Although there is currently not enough knowledge to fully understand how the interactions between gut microbiota, host responses and the environment cause food allergies, recent advances in '-omics' technologies (i.e., proteomics, genomics, metabolomics) and in approaches involving systems biology suggest future headways that would finally allow the scientific understanding of the relationship between gut microbiome and FA. This review summarizes the current knowledge in the field of FA and insights into the future advances that will be achieved by applying proteomic techniques to study the GI tract microbiome in the field of FA and their medical treatment. Metaproteomics, a proteomics experimental approach of great interest in the study of GI tract microbiota, aims to analyze and identify all the proteins in complex environmental microbial communities; with shotgun proteomics, which uses liquid chromatography (LC) for separation and tandem mass spectrometry (MS/MS) for analysis, as it is the most promising technique in this field.

Protocol for global proteome, virome, and metaproteome profiling of respiratory specimen (VTM) in COVID-19 patient by LC-MS/MS-based analysis

In this protocol, we describe global proteome profiling for the respiratory specimen of COVID-19 patients, patients suspected with COVID-19, and H1N1 patients. In this protocol, details for identifying host, viral, or bacterial proteome (Meta-proteome) are provided. Major steps of the protocol include virus inactivation, protein quantification and digestion, desalting of peptides, high-resolution mass spectrometry (HRMS)-based analysis, and downstream bioinformatics analysis. For complete details on the use and execution of this profile, please refer to Maras et al. (2021).

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Novel Approaches to an Integrated Route for Trisomy 21 Evaluation

Trisomy 21 (T21) is one of the most commonly occurring genetic disorders, caused by the partial or complete triplication of chromosome 21. Despite the significant progress in the diagnostic tools applied for prenatal screening, commonly used methods are still imprecise and involve invasive diagnostic procedures that are related to a maternal risk of miscarriage. In this case, novel prenatal biomarkers are still being evaluated using highly specialized techniques, which could increase the diagnostic usefulness of biochemical prenatal screening for T21. From the other hand, the T21′s pathogenesis, caused by the improper division of genetic material, disrupting many metabolic pathways, could be further evaluated with the use of omics methods, which could result in bringing relevant insights for the evaluation of potential medical targets. Accordingly, a literature search was undertaken to collect novel information about prenatal screening for Down syndrome with the use of advanced technology, with a particular emphasis on the evaluation of novel screening biomarkers and the discovery of potential medical targets. These meta-analyses are focused on novel approaches designed with the use of omics techniques, representing the most rapidly developing and promising field in research today. Considering the limitations and progress of these methods, the use of omics techniques in evaluating T21 pathogenesis could bring beneficial results in prenatal screening, simultaneously uncovering novel potential medical targets.

Serum biomarker discovery related to pathogenesis in acute coronary syndrome by proteomic approach

Acute coronary syndrome (ACS) results from inadequate supply of blood flow from the coronary arteries to the heart or ischemia. ACS has an extremely high morbidity and mortality. The levels of biomarkers currently used for detection of ACS also increase in response to myocardial necrosis and other diseases and are not elevated immediately after symptoms appear, thus limiting their diagnostic capacity. Therefore, we aimed to discover new ACS diagnostic biomarkers with high sensitivity and specificity that are specifically related to ACS pathogenesis. Sera from 50 patients with ACS and healthy controls (discovery cohort) each were analyzed using mass spectrometry (MS) to identify differentially expressed proteins, and protein candidates were evaluated as ACS biomarkers in 120 people in each group (validation cohort). α-1-acid glycoprotein 1 (AGP1), complement C5 (C5), leucine-rich α-2-glycoprotein (LRG), and vitronectin (VN) were identified as biomarkers whose levels increase and gelsolin (GSN) as a biomarker whose levels decrease in patients with ACS. We concluded that these biomarkers are associated with the pathogenesis of ACS and can predict the onset of ACS prior to the appearance of necrotic biomarkers.

Biomarker Discovery of Acute Coronary Syndrome Using Proteomic Approach

Acute coronary syndrome (ACS) is a condition in which the coronary artery supplying blood to the heart is infarcted via formation of a plaque and thrombus, resulting in abnormal blood supply and high mortality and morbidity. Therefore, the prompt and efficient diagnosis of ACS and the need for new ACS diagnostic biomarkers are important. In this study, we aimed to identify new ACS diagnostic biomarkers with high sensitivity and specificity using a proteomic approach. A discovery set with samples from 20 patients with ACS and 20 healthy controls was analyzed using mass spectrometry. Among the proteins identified, those showing a significant difference between each group were selected. Functional analysis of these proteins was conducted to confirm their association with functions in the diseased state. To determine ACS diagnostic biomarkers, standard peptides of the selected protein candidates from the discovery set were quantified, and these protein candidates were validated in a validation set consisting of the sera of 50 patients with ACS and 50 healthy controls. We showed that hemopexin, leucine-rich α-2-glycoprotein, and vitronectin levels were upregulated, whereas fibronectin level was downregulated, in patients with ACS. Thus, the use of these biomarkers may increase the accuracy of ACS diagnosis.

Re-Evaluating the Conventional Wisdom about Binding Assays

Analytical technologies based on binding assays have evolved substantially since their inception nearly 60 years ago, but our conceptual understanding of molecular recognition has not kept pace. Contemporary technologies, such as single-molecule and digital measurements, have challenged, or even rendered obsolete, core concepts behind conventional binding assay design. Here, we explore the fundamental principles underlying molecular recognition systems, which we consider in terms of signals generated through concentration-dependent shifts in equilibrium. We challenge certain orthodoxies related to binding-based detection assays, including the primary importance of a low dissociation constant (K_D) and the extent to which this parameter constrains dynamic range and limit of detection. Lastly, we identify key principles for designing binding assays that are optimally suited for a given detection application.

Novel Autoantibody Signatures in Sera of Patients with Pancreatic Cancer, Chronic Pancreatitis and Autoimmune Pancreatitis: A Protein Microarray Profiling Approach

Identification of disease-associated autoantibodies is of high importance. Their assessment could complement current diagnostic modalities and assist the clinical management of patients. We aimed at developing and validating high-throughput protein microarrays able to screen patients' sera to determine disease-specific autoantibody-signatures for pancreatic cancer (PDAC), chronic pancreatitis (CP), autoimmune pancreatitis and their subtypes (AIP-1 and AIP-2). In-house manufactured microarrays were used for autoantibody-profiling of IgG-enriched preoperative sera from PDAC-, CP-, AIP-1-, AIP-2-, other gastrointestinal disease (GID) patients and healthy controls. As a top-down strategy, three different fluorescence detection-based protein-microarrays were used: large with 6400, intermediate with 345, and small with 36 full-length human recombinant proteins. Large-scale analysis revealed 89 PDAC, 98 CP and 104 AIP immunogenic antigens. Narrowing the selection to 29 autoantigens using pooled sera first and individual sera afterwards allowed a discrimination of CP and AIP from PDAC. For validation, predictive models based on the identified antigens were generated which enabled discrimination between PDAC and AIP-1 or AIP-2 yielded high AUC values of 0.940 and 0.925, respectively. A new repertoire of autoantigens was identified and their assembly as a multiplex test will provide a fast and cost-effective tool for differential diagnosis of pancreatic diseases with high clinical relevance.

Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging

To understand the value and clinical impact of surveying genome-wide disease-causing genes and variants, we used a prospective cohort study design that enrolled volunteers who agreed to have their whole genome sequenced and to participate in deep phenotyping using clinical laboratory tests, metabolomics technologies, and advanced noninvasive imaging. The genomic results are integrated with the phenotype results. Approximately 1 in 6 adult individuals (17.3%) had genetic findings and, when integrated with deep phenotyping data, including family/medical histories with genetic findings, 1 in 9 (11.5%) had genotype and phenotype associations. Genomics and metabolomics association analysis revealed 5.1% of heterozygotes with phenotype manifestations affecting serum metabolite levels. We report observations from our study in which health outcomes and benefits were not measured.

Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

System-based approaches as prognostic tools for glioblastoma

BACKGROUND

The evasion of apoptosis is a hallmark of cancer. Understanding this process holistically and overcoming apoptosis resistance is a goal of many research teams in order to develop better treatment options for cancer patients. Efforts are also ongoing to personalize the treatment of patients. Strategies to confirm the therapeutic efficacy of current treatments or indeed to identify potential novel additional options would be extremely beneficial to both clinicians and patients. In the past few years, system medicine approaches have been developed that model the biochemical pathways of apoptosis. These systems tools incorporate and analyse the complex biological networks involved. For their successful integration into clinical practice, it is mandatory to integrate systems approaches with routine clinical and histopathological practice to deliver personalized care for patients.

RESULTS

We review here the development of system medicine approaches that model apoptosis for the treatment of cancer with a specific emphasis on the aggressive brain cancer, glioblastoma.

CONCLUSIONS

We discuss the current understanding in the field and present new approaches that highlight the potential of system medicine approaches to influence how glioblastoma is diagnosed and treated in the future.

The nuts and bolts of omics for the clinical allergist

OBJECTIVE

Omics, aka multi-omics, is an emerging area of research that is advancing the use of personalized medicine in clinical practice and is therefore relevant for the practicing allergist.

DATA SOURCES

We performed a literature search of a selection of scientific findings in omics and allergy, including variants that may be important to allergy outcomes in the genome, transcriptome, metabolome, microbiome, epigenome, and exposome, among others.

STUDY SELECTIONS

Basic science papers and review articles.

RESULTS

The use of multi-omic data in clinical practice is changing how clinicians treat their patients whereby more personalized approaches are becoming standard in medical practice and has the potential to transform the treatment of allergies.

CONCLUSION

Multi-omic data are relevant and will become increasingly important for the clinical allergist.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

The implementation of omics technologies in cancer microbiome research

Whilst the interplay between host genetics and the environment plays a pivotal role in the aetiopathogenesis of cancer, there are other key contributors of importance as well. One such factor of central and growing interest is the contribution of the microbiota to cancer. Even though the field is only a few years old, investigation of the 'cancer microbiome' has already led to major advances in knowledge of the basic biology of cancer risk and progression, opened novel avenues for biomarkers and diagnostics, and given a better understanding of mechanisms underlying response to therapy. Recent developments in microbial DNA sequencing techniques (and the bioinformatics required for analysis of these datasets) have allowed much more in-depth profiling of the structure of microbial communities than was previously possible. However, for more complete assessment of the functional implications of microbial changes, there is a growing recognition of the importance of the integration of microbial profiling with other omics modalities, with metabonomics (metabolite profiling) and proteomics (protein profiling) both gaining particular recent attention. In this review, we give an overview of some of the key scientific techniques being used to unravel the role of the cancer microbiome. We have aimed to highlight practical aspects related to sample collection and preparation, choice of the modality of analysis, and examples of where different omics technologies have been complementary to each other to highlight the significance of the cancer microbiome.

Application of molecular tools to elucidate the microbiota of seafood

The aim of this review is to present the methodologies currently applied to identify microbiota and pathogens transmitted to humans through seafood consumption, focusing on molecular techniques and pointing out their importance, advantages, disadvantages and applicability. Knowledge of available techniques allows researchers to identify which technique best fits their expectations. With such discernment, it will be possible to infer which disadvantages will be present and, therefore, not interfering with the final result. Two methodologies can be employed for this purpose, dependent and independent cultures. However, the dependent culture has certain limitations that can be solved through the independent cultivation techniques, such as PCR, PFGE and NGS, especially through the sequencing of the 16S rRNA region, providing a complete view of microbial diversity. These have revolutionized microbiological knowledge, mainly because they allow for the identification of uncultivable micro-organisms, which represent a substantial portion of total micro-organisms, making it possible to elucidate not yet described taxa which may display pathogenic potential, besides quantifying microbial communities, microbiota genetics, translated proteins and produced metabolites. In addition, transcriptomic and metabolomic techniques also allow for the evaluation of possible impacts that microbial communities may create in their environment, as well as the determination of potential pathogenicity to humans.

Toward biotechnology in space: High-throughput instruments for in situ biological research beyond Earth

Space biotechnology is a nascent field aimed at applying tools of modern biology to advance our goals in space exploration. These advances rely on our ability to exploit in situ high throughput techniques for amplification and sequencing DNA, and measuring levels of RNA transcripts, proteins and metabolites in a cell. These techniques, collectively known as "omics" techniques have already revolutionized terrestrial biology. A number of on-going efforts are aimed at developing instruments to carry out "omics" research in space, in particular on board the International Space Station and small satellites. For space applications these instruments require substantial and creative reengineering that includes automation, miniaturization and ensuring that the device is resistant to conditions in space and works independently of the direction of the gravity vector. Different paths taken to meet these requirements for different "omics" instruments are the subjects of this review. The advantages and disadvantages of these instruments and technological solutions and their level of readiness for deployment in space are discussed. Considering that effects of space environments on terrestrial organisms appear to be global, it is argued that high throughput instruments are essential to advance (1) biomedical and physiological studies to control and reduce space-related stressors on living systems, (2) application of biology to life support and in situ resource utilization, (3) planetary protection, and (4) basic research about the limits on life in space. It is also argued that carrying out measurements in situ provides considerable advantages over the traditional space biology paradigm that relies on post-flight data analysis.

Advancing the global proteome survey platform by using an oriented single chain antibody fragment immobilization approach

Increasing the understanding of a proteome and how its protein composition is affected by for example different diseases, such as cancer, has the potential to improve strategies for early diagnosis and therapeutics. The Global Proteome Survey or GPS is a method that combines mass spectrometry and affinity enrichment with the use of antibodies. The technology enables profiling of complex proteomes in a species independent manner. The sensitivity of GPS, and other methods relying on affinity enrichment, is largely affected by the activity of the exploited affinity reagent. We here present an improvement of the GPS platform by utilizing an antibody immobilization approach which ensures a controlled immobilization process of the antibody to the magnetic bead support. More specifically, we make use of an antibody format that enables site-directed biotinylation and use this in combination with streptavidin coated magnetic beads. The performance of the expanded GPS platform was evaluated by profiling yeast proteome samples. We demonstrate that the oriented antibody immobilization strategy increases the ability of the GPS platform and results in larger fraction of functional antibodies. Additionally, we show that this new antibody format enabled in-solution capture, i.e. immobilization of the antibodies after sample incubation. A workflow has been established that permit the use of an oriented immobilization strategy for the GPS platform.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

The intricacy of biomarker complexity-the identification of a genuine proteomic biomarker is more complicated than believed

Several reasons have been put forward to explain the irreproducibility of proteomic biomarker search. However, these reasons pertain to almost every part of biomarker search across the entire analytical workflow but are entirely experimental or methodological. However, in this article we point out that there is a further cause of such irreproducibility. This is not an additional methodological or experimental cause but arises directly from the biology of protein expression. It arises from the fact that disease changes the diversity within protein families. This cause of irreproducibility has been very little studied in relation to proteomic biomarker search. Gene expression is highly variable even in healthy people. Therefore, multiple proteoforms are also to be expected when gene expression is disrupted by disease, proteoforms that may be differently altered by pathology. In consequence, it is illogical to expect that the whole protein family produces a reliably usable biomarker. It is more reasonable to expect that a specific proteoform fulfills this role. Appropriate sample pre-fractionation methods and data analyses could help to identify this version, carrying the modification or the epitope required.

Protein Profiling Gastric Cancer and Neighboring Control Tissues Using High-Content Antibody Microarrays

In this study, protein profiling was performed on gastric cancer tissue samples in order to identify proteins that could be utilized for an effective diagnosis of this highly heterogeneous disease and as targets for therapeutic approaches. To this end, 16 pairs of postoperative gastric adenocarcinomas and adjacent non-cancerous control tissues were analyzed on microarrays that contain 813 antibodies targeting 724 proteins. Only 17 proteins were found to be differentially regulated, with much fewer molecules than the numbers usually identified in studies comparing tumor to healthy control tissues. Insulin-like growth factor-binding protein 7 (IGFBP7), S100 calcium binding protein A9 (S100A9), interleukin-10 (IL‐10) and mucin 6 (MUC6) exhibited the most profound variations. For an evaluation of the proteins’ capacity for discriminating gastric cancer, a Receiver Operating Characteristic curve analysis was performed, yielding an accuracy (area under the curve) value of 89.2% for distinguishing tumor from non-tumorous tissue. For confirmation, immunohistological analyses were done on tissue slices prepared from another cohort of patients with gastric cancer. The utility of the 17 marker proteins, and particularly the four molecules with the highest specificity for gastric adenocarcinoma, is discussed for them to act as candidates for diagnosis, even in serum, and targets for therapeutic approaches.