Genomics, transcriptomics, proteomics, and numbers

Should AI-Powered Whole-Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology—A Scoping Review

In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole-genome sequencing (WGS) analysis, with a specific focus on its implications in oncology. Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI’s emergence as a potent ally in processing and analyzing the voluminous genomic data generated. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the importance of ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more successful personalized and targeted treatments.

Integrative transcriptomics and proteomics analysis reveal the protection of Astragaloside IV against myocardial fibrosis by regulating senescence

Myocardial fibrosis (MF) is a pivotal pathological process implicated in various cardiovascular diseases, particularly heart failure. Astragaloside IV (AS-IV), a natural compound derived from Astragalus membranaceus, possesses potent cardioprotective properties. However, the precise molecular mechanisms underlying its anti-MF effects, particularly in relation to senescence, remain elusive. Thus, this study aimed to investigate the therapeutic potential and underlying molecular mechanisms of AS-IV in treating ISO-induced MF in mice, employing transcriptomics, proteomics, in vitro, and in vivo experiments. We assessed the positive effects of AS-IV on ISO-induced MF using HE staining, Masson staining, ELISA, immunohistochemical staining, transthoracic echocardiography, transmission electron microscopy, and DHE fluorescence staining. Additionally, we elucidated the regulatory role of AS-IV in MF through comprehensive transcriptomics and proteomics analyses, complemented by Western blotting and RT-qPCR validation of pertinent molecular pathways. Our findings demonstrated that AS-IV treatment markedly attenuated ISO-induced myocardial injury and oxidative stress, concomitantly inhibiting the release of SASPs. Furthermore, integrated transcriptomics and proteomics analyses revealed that the anti-MF mechanism of AS-IV was associated with regulating cellular senescence and the p53 signaling pathway. These results highlight AS-IV exerts its anti-MF effects not only by inhibiting oxidative stress but also by modulating senescence through the p53 signaling pathway.

Forty Years of Molecular Diagnostics for Infectious Diseases

Nearly 40 years have elapsed since the invention of the PCR, with its extremely sensitive and specific ability to detect nucleic acids via in vitro enzyme-mediated amplification. In turn, more than 2 years have passed since the onset of the coronavirus disease 2019 (COVID-19) pandemic, during which time molecular diagnostics for infectious diseases have assumed a larger global role than ever before. In this context, we review broadly the progression of molecular techniques in clinical microbiology, to their current prominence. Notably, these methods now entail both the detection and quantification of microbial nucleic acids, along with their sequence-based characterization. Overall, we seek to provide a combined perspective on the techniques themselves, as well as how they have come to shape health care at the intersection of technologic innovation, pathophysiologic knowledge, clinical/laboratory logistics, and even financial/regulatory factors.

Clinical Microbiology in Detection and Identification of Emerging Microbial Pathogens: Past, Present and Future

Clinical microbiology has possessed a marvellous past, an important present and a bright future. Western medicine modernization started with the discovery of bacterial pathogens, and from then, clinical bacteriology became a cornerstone of diagnostics. Today, clinical microbiology uses standard techniques including Gram stain morphology, in vitro culture, antigen and antibody assays, and molecular biology both to establish a diagnosis and monitor the progression of microbial infections. Clinical microbiology has played a critical role in pathogen detection and characterization for emerging infectious diseases as evidenced by the ongoing COVID-19 pandemic. Revolutionary changes are on the way in clinical microbiology with the application of “-omic” techniques, including transcriptomics and metabolomics, and optimization of clinical practice configurations to improve outcomes of patients with infectious diseases.

Formulation and cytotoxicity evaluation of new self-emulsifying multiple W/O/W nanoemulsions

Three multiple water-in-oil-in-water (W/O/W) nanoemulsions have been designed for potential inclusion of either lipophilic or hydrophilic drugs using a two-step emulsification process exclusively based on low-energy self-emulsification. The W/O primary emulsion was constituted by a blend of oil (medium chain triglyceride), a mixture (7:3) of two surfactants, and a 10% water phase. The surfactants were a mixture of Polysorbate-85/Labrasol^®, Polysorbate-85/Cremophor^® EL or glycerol/Polysorbate-85. The final W/O/W nanoemulsions were obtained by the addition of water, with a weight ratio nanoemulsion/water of 1:2. The multiple emulsion stability was found to increase from 24 hours to 2 and 6 months with Labrasol, glycerol, and Cremophor, respectively. Cytotoxicity was found for formulations including Labrasol and Cremophor EL. The concentration of emulsion inhibiting 50% cell viability (IC₅₀) was determined using the alamarBlue^® test, giving after 24 hours of incubation, IC₅₀ = 10.2 mg/mL for the Labrasol formulation and IC₅₀ = 11.8 mg/mL for the Cremophor EL formulation. Corresponding calculated IC₅₀ values for surfactants were 0.51 mg/mL for Labrasol and 0.59 mg/mL for Cremophor EL. In both cases, cytotoxicity was due to an apoptotic mechanism, evidenced by chromatin condensation and P2X7 cell death receptor activation. The formulation including glycerol, investigated between 1 and 100 mg/mL concentration of nanoemulsion, did not affect cell viability. Moreover, neither chromatin condensation nor P2X7 activation was found between the 10 and 30 mg/mL final concentration of the emulsion. This last formulation would therefore be of major interest for further developments.

QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data

Background

Effective quality assessment is an important part of any high-throughput flow cytometry data analysis pipeline, especially when considering the complex designs of the typical flow experiments applied in clinical trials. Technical issues like instrument variation, problematic antibody staining, or reagent lot changes can lead to biases in the extracted cell subpopulation statistics. These biases can manifest themselves in non–obvious ways that can be difficult to detect without leveraging information about the study design or other experimental metadata. Consequently, a systematic and integrated approach to quality assessment of flow cytometry data is necessary to effectively identify technical errors that impact multiple samples over time. Gated cell populations and their statistics must be monitored within the context of the experimental run, assay, and the overall study.

Results

We have developed two new packages, flowWorkspace and QUAliFiER to construct a pipeline for quality assessment of gated flow cytometry data. flowWorkspace makes manually gated data accessible to BioConductor’s computational flow tools by importing pre–processed and gated data from the widely used manual gating tool, FlowJo (Tree Star Inc, Ashland OR). The QUAliFiER package takes advantage of the manual gates to perform an extensive series of statistical quality assessment checks on the gated cell sub–populations while taking into account the structure of the data and the study design to monitor the consistency of population statistics across staining panels, subject, aliquots, channels, or other experimental variables. QUAliFiER implements SVG–based interactive visualization methods, allowing investigators to examine quality assessment results across different views of the data, and it has a flexible interface allowing users to tailor quality checks and outlier detection routines to suit their data analysis needs.

Conclusion

We present a pipeline constructed from two new R packages for importing manually gated flow cytometry data and performing flexible and robust quality assessment checks. The pipeline addresses the increasing demand for tools capable of performing quality checks on large flow data sets generated in typical clinical trials. The QUAliFiER tool objectively, efficiently, and reproducibly identifies outlier samples in an automated manner by monitoring cell population statistics from gated or ungated flow data conditioned on experiment–level metadata.

Pediatric pharmacogenetic and pharmacogenomic studies: the current state and future perspectives

Genetic differences among individuals can explain some of the variability observed during drug treatment. Many studies have correlated the different pharmacological response to genetic variability, but most of them have been conducted on adult populations. Much less attention has been given to the pediatric population. Pediatric patients constitute a vulnerable group with regard to rational drug prescribing since they present differences arising from the various stages of development. However, only a few steps have been made in developmental pharmacogenomics. This review attempts to describe the current methods for pharmacogenetic and pharmacogenomic studies, providing some of the most studied examples in pediatric patients. It also gives an overview on the implication and importance of microRNA polymorphisms, transcriptomics, metabonomics, and proteomics in pharmacogenetics and pharmacogenomics studies.

A pipeline for automated analysis of flow cytometry data: preliminary results on lymphoma sub-type diagnosis

Flow cytometry (FCM) is widely used in health research and is a technique to measure cell properties such as phenotype, cytokine expression, etc., for up to millions of cells from a sample. FCM data analysis is a highly tedious, subjective and manually time-consuming (to the level of impracticality for some data) process that is based on intuition rather than standardized statistical inference. This study proposes a pipeline for automatic analysis of FCM data. The proposed pipeline identifies biomarkers that correlate with physiological/pathological conditions and classifies the samples to specific pathological/physiological entities. The pipeline utilizes a model-based clustering approach to identify cell populations that share similar biological functions. Support vector machine (SVM) and random forest (RF) classifiers were then used to classify the samples and identify biomarkers associated with disease status. The performance of the proposed data analysis pipeline has been evaluated on lymphoma patients. Preliminary results show more than 90% accuracy in differentiating between some sub-types of lymphoma. The proposed pipeline also finds biologically meaningful biomarkers that differ between lymphoma subtypes.

A survey of flow cytometry data analysis methods

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.

flowClust: a Bioconductor package for automated gating of flow cytometry data

Background

As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.

Results

In view of the aforementioned issues, we have developed an R package called flowClust to automate FCM analysis. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. The package provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. It offers various tools to summarize and visualize a wealth of features of the clustering results. In addition, to ensure its convenience of use, flowClust has been adapted for the current FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis.

Conclusion

flowClust addresses the issue of a dearth of software that helps automate FCM analysis with a sound theoretical foundation. It tends to give reproducible results, and helps reduce the significant subjectivity and human time cost encountered in FCM analysis. The package contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.

Does the lipid-lowering peroxisome proliferator-activated receptors ligand bezafibrate prevent colon cancer in patients with coronary artery disease?

BACKGROUND

Epidemiologic studies have suggested that hypertriglyceridemia and insulin resistance are related to the development of colon cancer. Nuclear peroxisome proliferator-activated receptors (PPAR), which play a central role in lipid and glucose metabolism, had been hypothesized as being involved in colon cancerogenesis. In animal studies the lipid-lowering PPAR ligand bezafibrate suppressed colonic tumors. However, the effect of bezafibrate on colon cancer development in humans is unknown. Therefore, we proposed to investigate a possible preventive effect of bezafibrate on the development of colon cancer in patients with coronary artery disease during a 6-year follow-up.

METHODS

Our population included 3011 patients without any cancer diagnosis who were enrolled in the randomized, double blind Bezafibrate Infarction Prevention (BIP) Study. The patients received either 400 mg of bezafibrate retard (1506 patients) or placebo (1505 patients) once a day. Cancer incidence data were obtained by matching a subject's identification numbers with the National Cancer Registry. Each matched record was checked for correct identification.

RESULTS

Development of new cancer (all types) was recorded in 177 patients: in 79 (5.25%) patients from the bezafibrate group vs. 98 (6.51%) from the placebo group. Development of colon cancer was recorded in 25 patients: in 8 (0.53%) patients from the bezafibrate group vs. 17 (1.13%) from the placebo group, (Fisher's exact test: one side p = 0.05; two side p = 0.07). A difference in the incidence of cancer was only detectable after a 4 year lag and progressively increased with continued follow-up. On multivariable analysis the colon cancer risk in patients who received bezafibrate tended to be lower with a hazard ratio of 0.47 and 95% confidence interval 0.2-1.1.

CONCLUSION

Our data, derived from patients with coronary artery disease, support the hypothesis regarding a possible preventive effect of bezafibrate on the development of colon cancer.

Application of clinical proteomics in diagnosis and management of trauma patients

Poly-trauma remains a medical entity with major implications, for patient's morbidity, mortality and healthcare economics. Advances in molecular medicine have improved diagnostic techniques in detecting devastating complication after major trauma. Patients at high risk of multiple organ dysfunction syndrome (MODS) or adult respiratory distress syndrome (ARDS), could be identified early, monitored and treated. Proteomics is the systematic evaluation of proteins produced by the cell under normal or pathological circumstances. Investigating protein production will allow us to identify and modify disease natural history and treatment. In this review, we summarise the proteomic methods currently applied in trauma research.

The -omics era and its impact

OBJECTIVE

To review the advances in clinically useful molecular biologic techniques and to identify their applications, as presented at the 12th Annual William Beaumont Hospital DNA Symposium.

DATA SOURCES

The 7 manuscripts submitted were reviewed and their major findings were compared with literature on the same or related topics.

STUDY SELECTION

Manuscripts address the use of molecular techniques in the detection of severe acute respiratory syndrome (SARS) and bacterial ribosome mutations, which may lead to ribosome-targeted drug resistance; pharmacogenomics as a clinical laboratory service and example of warfarin dosing using CYP2C9 mutation analysis; definition of the potential of cytosine arabinoside incorporation into DNA to disrupt transcription using an in vitro model of oligonucleotides; use of laser capture microdissection to isolate solid tumor cells free of nontumor cells; and molecular methods used to classify lymphomas.

DATA SYNTHESIS

Two current issues related to the use of molecular tests in the clinical laboratories are (1) decentralization of molecular-based testing to a variety of nonmolecular laboratories and (2) need for wider acceptance of molecular-based testing through its incorporation in clinical practice guidelines. Molecular methods have had a major impact on infectious disease through the rapid identification of new infectious agents, SARS, and the characterization of drug resistance. Pharmacogenomics identifies the genetic basis for heritable and interindividual variation in response to drugs. The incorporation of the nucleoside analog, cytosine arabinoside, into DNA leads to local perturbation of DNA structure and reduces the ability of transcription factors to bind to their specific DNA binding elements as measured by electrophoretic mobility shift assays. Laser capture microdissection of tumor cells can provide an adequate number of cells for whole genome amplification. Gene expression microassay profiles of various lymphomas have modified classification systems and predict prognosis and response to therapy.

CONCLUSIONS

The current -omics era will continue to emphasize the use of microarrays and database software for genomic, transcriptomic, and proteomic screening to search for a useful clinical assay. The number of molecular pathologic techniques will expand as additional disease-associated mutations are defined.

Molecular biomarkers in drug development

Arguably, the most immediately promising reverberation of the genomics era has been the application of biomarkers to drug development. The promise of applying biomarkers to early drug development is that they might aid in preclinical and early clinical decisions such as dose ranging, definition of treatment regimen, or even a preview of efficacy. Later in the clinic, biomarkers could be used to facilitate patient stratification, selection and the description of surrogate endpoints. Information derived from biomarkers should result in a better understanding of preclinical and clinical data, which ultimately benefits patients and drug developers. If the promise of biomarkers is realized, they will become a routine component of drug development and companions to newly discovered therapies.

Advances in pharmacogenomics and individualized drug therapy: exciting challenges that lie ahead

Between the 1930s and 1990s, several dozen predominantly monogenic, high-penetrance disorders involving pharmacogenetics were described, fueling the crusade that gene-drug interactions are quite simple. Then, in 1990, the Human Genome Project was established; in 1995, the term pharmacogenomics was introduced; finally, the complexities of determining an unequivocal phenotype, as well as an unequivocal genotype, have recently become apparent. Since 1965, more than 1000 reviews on this topic have painted an overly optimistic picture-suggesting that the advent of individualized drug therapy used by the practicing physician is fast approaching. For many reasons listed here, however, we emphasize that these high expectations must be tempered. We now realize that the nucleotide sequence of the genome represents only a starting point from which we must proceed to a more difficult stage: knowledge of the function encoded and how this affects the phenotype. To achieve individualized drug therapy, a high level of accuracy and precision is required of any clinical test proposed in human patients. Finally, we suggest that metabonomics, perhaps in combination with proteomics, might complement genomics in eventually helping us to achieve individualized drug therapy.

Post-genome molecular diagnostics in obstetrics

PURPOSE OF REVIEW

The intention of this review is to familiarize the practicing clinician with the current status and future direction of molecular testing in obstetrics. As a discipline, obstetrics and gynecology is unique in that it deals with the full spectrum of molecular genetic testing. This spectrum includes infectious disease, neoplasia and inherited diseases. This review will focus on inherited conditions and complex diseases, as it is in this context that we may fully realize the true promise of the human genome and its application to the practice of medicine.

RECENT FINDINGS

Despite the successful sequencing of the human genome, very few new molecular genetic tests have become available. The apparent reason for this lies in the relative paucity of information gleaned from examining the genes themselves. Two new avenues of investigation are presently underway to improve the 'infirmity' of this information archive. Rather than merely looking at differential gene expression, clinician scientists have begun to examine genetic polymorphisms of single and multiple genes within and between individuals in an attempt to explain biologic processes, including disease states. The second avenue involves the characterization of the products of gene expression--proteins. Proteomics, in conjunction with high throughput polymorphism analysis, may enable us to diagnose and treat complex multifactorial diseases.

SUMMARY

Molecular diagnostics for multifactorial diseases will become conceptually and technologically more complex than present DNA testing modalities. The development and ultimate acceptance of these tests will require greater coordination between the medical and scientific communities to ensure that the right technologies are applied to the highest quality samples to answer the most relevant questions.