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

The Multifaceted Effects of Short-Term Acute Hypoxia Stress: Insights into the Tolerance Mechanism of Propsilocerus akamusi (Diptera: Chironomidae)

Plenty of freshwater species, especially macroinvertebrates that are essential to the provision of numerous ecosystem functions, encounter higher mortality due to acute hypoxia. However, within the family Chironomidae, a wide range of tolerance to hypoxia/anoxia is displayed. Propsilocerus akamusi depends on this great tolerance to become a dominant species in eutrophic lakes. To further understand how P. akamusi responds to acute hypoxic stress, we used multi-omics analysis in combination with histomorphological characteristics and physiological indicators. Thus, we set up two groups-a control group (DO 8.4 mg/L) and a hypoxic group (DO 0.39 mg/L)-to evaluate enzyme activity and the transcriptome, metabolome, and histomorphological characteristics. With blue-black chromatin, cell tightness, cell membrane invagination, and the production of apoptotic vesicles, tissue cells displayed typical apoptotic features in the hypoxic group. Although lactate dehydrogenase (LDH), alcohol dehydrogenase (ADH), catalase (CAT), and Na+/K+ -ATPase (NKA) activities were dramatically enhanced under hypoxic stress, glycogen content, and superoxide dismutase (SOD) activities were significantly reduced compared to the control group. The combined analysis of the transcriptome and metabolome, which further demonstrated, in addition to carbohydrates, including glycogen, the involvement of energy metabolism pathways, including fatty acid, protein, trehalose, and glyoxylate cycles, provided additional support for the aforementioned findings. Lactate is the end product of glycogen degradation, and HIF-1 plays an important role in promoting glycogenolysis in acute hypoxic conditions. However, we discovered that the ethanol tested under hypoxic stress likely originates from the symbiodinium of P. akamusi. These results imply that some parameters related to energy metabolism, antioxidant enzyme activities, and histomorphological features may be used as biomarkers of eutrophic lakes in Chironomus riparius larvae. The study also provides a scientific reference for assessing toxicity and favoring policies to reduce their impact on the environment.

Building a large gene expression-cancer knowledge base with limited human annotations

Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on 'fine-grained' gene expression-cancer associations-a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URL https://zenodo.org/record/7577127.

Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms

Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank p-value of 0.044 for the final gene signatures. The analysis of tumor immunocyte infiltration showed a weak correlation with the expression of the gene signatures. CA2, CA7, and ITM2C can serve as gene signatures for the early detection of CRC and may provide valuable information for prognostic and therapeutic decision making. Further research is needed to fully understand the potential of these genes in the context of CRC.

Metabolite profiling and transcriptomic analyses demonstrate the effects of biocontrol agents on alkaloid accumulation in Fritillaria thunbergii

BACKGROUND

During Fritillaria thunbergii planting, pests and diseases usually invade the plant, resulting in reduced yield and quality. Previous studies have demonstrated that using biocontrol agents can effectively control grubs and affect the steroid alkaloids content in F. thunbergii. However, the molecular regulatory mechanisms underlying the differences in the accumulation of steroid alkaloids in response to biocontrol agents remain unclear.

RESULTS

Combined transcriptomic and metabolic analyses were performed by treating the bulbs of F. thunbergii treated with biocontrol agents during planting. Otherwise, 48 alkaloids including 32 steroid alkaloids, 6 indole alkaloids, 2 scopolamine-type alkaloids, 1 isoquinoline alkaloid, 1 furoquinoline alkaloid, and 6 other alkaloids were identified. The content of steroidal alkaloids particularly peimine, peiminine, and veratramine, increased significantly in the group treated with the biocontrol agents. Transcriptome sequencing identified 929 differential genes using biocontrol agents, including 589 upregulated and 340 downregulated genes. Putative biosynthesis networks of steroid alkaloids have been established and combined with differentially expressed structural unigenes, such as acetyl-CoA C-acetyl-transferase, acelyl-CoAC-acetyltransferase3-hydroxy-3-methylglutaryl-coenzyme A synthase, 1-deoxy-D-xylulose-5-phosphate reductor-isomerase, 2-C-methyl-D-erythritol-4-phosphate cytidylyltransferase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase. In addition, biological processes such as amino acid accumulation and oxidative phosphorylation were predicted to be related to the synthesis of steroid alkaloids. Cytochrome P450 enzymes also play crucial roles in the steroid alkaloid synthesis. The transcription factor families MYB and bHLH were significantly upregulated after using biocontrol agents.

CONCLUSIONS

Biocontrol agents increased the steroid alkaloids accumulation of steroid alkaloids by affecting key enzymes in the steroid alkaloid synthesis pathway, biological processes of oxidative phosphorylation and amino acid synthesis, cytochrome P450 enzymes, and transcription factors. This study revealed the mechanism underlying the difference in steroidal alkaloids in F. thunbergii after using biocontrol agents, laying the groundwork for future industrial production of steroid alkaloids and ecological planting of medicinal materials in the future.

Digital entrepreneurship from cellular data: How omics afford the emergence of a new wave of digital ventures in health

Data has become an indispensable input, throughput, and output for the healthcare industry. In recent years, omics technologies such as genomics and proteomics have generated vast amounts of new data at the cellular level including molecular, structural, and functional levels. Cellular data holds the potential to innovate therapeutics, vaccines, diagnostics, consumer products, or even ancestry services. However, data at the cellular level is generated with rapidly evolving omics technologies. These technologies use scientific knowledge from resource-rich environments. This raises the question of how new ventures can use cellular-level data from omics technologies to create new products and scale their business. We report on a series of interviews and a focus group discussion with entrepreneurs, investors, and data providers. By conceptualizing omics technologies as external enablers, we show how characteristics of cellular-level data negatively affect the combination mechanisms that drive venture creation and growth. We illustrate how data characteristics set boundary conditions for innovation and entrepreneurship and highlight how ventures seek to mitigate their impact.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12525-023-00669-w.

Genetic Enhancement of Cereals Using Genomic Resources for Nutritional Food Security

Advances in genomics resources have facilitated the evolution of cereal crops with enhanced yield, improved nutritional values, and heightened resistance to various biotic and abiotic stresses. Genomic approaches present a promising avenue for the development of high-yielding varieties, thereby ensuring food and nutritional security. Significant improvements have been made within the omics domain, specifically in genomics, transcriptomics, and proteomics. The advent of Next-Generation Sequencing (NGS) techniques has yielded an immense volume of data, accompanied by substantial progress in bioinformatic tools for proficient analysis. The synergy between genomics and computational tools has been acknowledged as pivotal for unravelling the intricate mechanisms governing genome-wide gene regulation. Within this review, the essential genomic resources are delineated, and their harmonization in the enhancement of cereal crop varieties is expounded upon, with a paramount focus on fulfilling the nutritional requisites of humankind. Furthermore, an encompassing compendium of the available genomic resources for cereal crops is presented, accompanied by an elucidation of their judicious utilization in the advancement of crop attributes.

Proteome deconvolution of liver biopsies reveals hepatic cell composition as an important marker of fibrosis

Human liver tissue is composed of heterogeneous mixtures of different cell types and their cellular stoichiometry can provide information on hepatic physiology and disease progression. Deconvolution algorithms for the identification of cell types and their proportions have recently been developed for transcriptomic data. However, no method for the deconvolution of bulk proteomics data has been presented to date. Here, we show that proteomes, which usually contain less data than transcriptomes, can provide useful information for cell type deconvolution using different algorithms. We demonstrate that proteomes from defined mixtures of cell lines, isolated primary liver cells, and human liver biopsies can be deconvoluted with high accuracy. In contrast to transcriptome-based deconvolution, liver tissue proteomes also provided information about extracellular compartments. Using deconvolution of proteomics data from liver biopsies of 56 patients undergoing Roux-en-Y gastric bypass surgery we show that proportions of immune and stellate cells correlate with inflammatory markers and altered composition of extracellular matrix proteins characteristic of early-stage fibrosis. Our results thus demonstrate that proteome deconvolution can be used as a molecular microscope for investigations of the composition of cell types, extracellular compartments, and for exploring cell-type specific pathological events. We anticipate that these findings will allow the refinement of retrospective analyses of the growing number of proteome datasets from various liver disease states and pave the way for AI-supported clinical and preclinical diagnostics.

Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits

This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (n = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM10, PM2.5, O3, NO2, CO, and SO2. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO2 also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O3 demonstrated mixed results. Both PM10 and PM2.5 showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.

Future prospects in mRNA vaccine development

The recent advancements in messenger ribonucleic acid (mRNA) vaccine development have vastly enhanced their use as alternatives to conventional vaccines in the prevention of various infectious diseases and treatment of several types of cancers. This is mainly due to their remarkable ability to stimulate specific immune responses with minimal clinical side effects. This review gives a detailed overview of mRNA vaccines currently in use or at various stages of development, the recent advancements in mRNA vaccine development, and the challenges encountered in their development. Future perspectives on this technology are also discussed.

Quantification of heat shock proteins in the posterior interosseous nerve among subjects with type 1 and type 2 diabetes compared to healthy controls

Introduction

Diabetic peripheral neuropathy (DPN) is a common complication of both type 1 (T1D) and type 2 diabetes (T2D). No cure for DPN is available, but several potential targets have been proposed for treatment. Heat shock proteins (HSPs) are known to respond to both hyper- and hypoglycemia. DPN can be diagnosed using electrophysiology and studied using peripheral nerve biopsies.

Aim

This study aimed to analyze the presence and patterns of HSPs in peripheral nerve biopsies from subjects with T1D, T2D, and healthy controls.

Methods

Posterior interosseous nerves (PIN) from a total of 56 subjects with T1D (n = 9), with T2D (n = 24), and without diabetes (i.e., healthy controls, n = 23) were harvested under local anesthesia and prepared for quantitative mass spectrometry analysis. Protein intensities were associated with electrophysiology data of the ulnar nerve and morphometry of the same PIN, and differences in protein intensities between groups were analyzed.

Results

In total, 32 different HSPs were identified and quantified in the nerve specimens. No statistically significant differences were observed regarding protein intensities between groups. Furthermore, protein intensities did not correlate with amplitude or conduction velocity in the ulnar nerve or with the myelinated nerve fiber density of PIN.

Conclusion

Quantitative proteomics can be used to study HSPs in nerve biopsies, but no clear differences in protein quantities were observed between groups in this cohort.

Proteomics-Driven Biomarkers in Pancreatic Cancer

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

A Tale of Three Proteomes: Visualizing Protein and Transcript Abundance Relationships in the Breast Cancer Proteome Portal

Molecular characterization is transforming research on novel therapeutics in breast cancer. High-throughput methodologies are unbiased to hypotheses; thus, data produced are relevant to address unlimited questions and provide resources for the experimental design process. However, the opportunity is often overlooked because data are not readily accessed or analyzed. Herein, the Breast Cancer Proteome Portal, the only online tool for analyzing protein and transcript abundances across the three breast cancer proteomics studies, is presented. The tool is applied to demonstrate that cofunctioning protein abundances are highly correlated and, conversely, high abundance correlation may be an indicator of cofunction. Furthermore, the cofunction-correlation relationship is less resolved at the transcript level. By applying analysis and visualization tools within the Breast Cancer Proteome Portal, insights are garnered about serine synthesis and the compartmentalization of one-carbon metabolism in breast cancer, and a transcription factor tumorigenic regulatory network of glutamine deamination and oxidation is proposed, illustrating that the Breast Cancer Proteome Portal provides an interface for garnering insights from the information-rich studies of the breast cancer proteome.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Traumatic brain injury induces an adaptive immune response in the meningeal transcriptome that is amplified by aging

Traumatic Brain Injury (TBI) is a major cause of disability and mortality, particularly among the elderly, yet our mechanistic understanding of how age renders the post-traumatic brain vulnerable to poor clinical outcomes and susceptible to neurological disease remains poorly understood. It is well established that dysregulated and sustained immune responses contribute to negative outcomes after TBI, however our understanding of the interactions between central and peripheral immune reservoirs is still unclear. The meninges serve as the interface between the brain and the immune system, facilitating important bi-directional roles in healthy and disease settings. It has been previously shown that disruption of this system exacerbates inflammation in age related neurodegenerative disorders such as Alzheimer's disease, however we have an incomplete understanding of how the meningeal compartment influences immune responses after TBI. Here, we examine the meningeal tissue and its response to brain injury in young (3-months) and aged (18-months) mice. Utilizing a bioinformatic approach, high-throughput RNA sequencing demonstrates alterations in the meningeal transcriptome at sub-acute (7-days) and chronic (1 month) timepoints after injury. We find that age alone chronically exacerbates immunoglobulin production and B cell responses. After TBI, adaptive immune response genes are up-regulated in a temporal manner, with genes involved in T cell responses elevated sub-acutely, followed by increases in B cell related genes at chronic time points after injury. Pro-inflammatory cytokines are also implicated as contributing to the immune response in the meninges, with ingenuity pathway analysis identifying interferons as master regulators in aged mice compared to young mice following TBI. Collectively these data demonstrate the temporal series of meningeal specific signatures, providing insights into how age leads to worse neuroinflammatory outcomes in TBI.

What do we mean by "aging"? Questions and perspectives revealed by studies in Drosophila

What is the nature of aging, and how best can we study it? Here, using a series of questions that highlight differing perspectives about the nature of aging, we ask how data from Drosophila melanogaster at the organismal, tissue, cellular, and molecular levels shed light on the complex interactions among the phenotypes associated with aging. Should aging be viewed as an individual's increasing probability of mortality over time or as a progression of physiological states? Are all age-correlated changes in physiology detrimental to vigor or are some compensatory changes that maintain vigor? Why do different age-correlated functions seem to change at different rates in a single individual as it ages? Should aging be considered as a single, integrated process across the scales of biological resolution, from organismal to molecular, or must we consider each level of biological scale as a separate, distinct entity? Viewing aging from these differing perspectives yields distinct but complementary interpretations about the properties and mechanisms of aging and may offer a path through the complexities related to understanding the nature of aging.

Advances in proteomic phenotyping of microglia in neurodegeneration

Microglia are dynamic resident immune cells of the central nervous system (CNS) that sense, survey, and respond to changes in their environment. In disease states, microglia transform from homeostatic to diverse molecular phenotypic states that play complex and causal roles in neurologic disease pathogenesis, as evidenced by the identification of microglial genes as genetic risk factors for neurodegenerative disease. While advances in transcriptomic profiling of microglia from the CNS of humans and animal models have provided transformative insights, the transcriptome is only modestly reflective of the proteome. Proteomic profiling of microglia is therefore more likely to provide functionally and therapeutically relevant targets. In this review, we discuss molecular insights gained from transcriptomic studies of microglia in the context of Alzheimer's disease as a prototypic neurodegenerative disease, and highlight existing and emerging approaches for proteomic profiling of microglia derived from in vivo model systems and human brain.

A Review of Classification, Biosynthesis, Biological Activities and Potential Applications of Flavonoids

Flavonoids represent the main class of plant secondary metabolites and occur in the tissues and organs of various plant species. In plants, flavonoids are involved in many biological processes and in response to various environmental stresses. The consumption of flavonoids has been known to reduce the risk of many chronic diseases due to their antioxidant and free radical scavenging properties. In the present review, we summarize the classification, distribution, biosynthesis pathways, and regulatory mechanisms of flavonoids. Moreover, we investigated their biological activities and discuss their applications in food processing and cosmetics, as well as their pharmaceutical and medical uses. Current trends in flavonoid research are also briefly described, including the mining of new functional genes and metabolites through omics research and the engineering of flavonoids using nanotechnology. This review provides a reference for basic and applied research on flavonoid compounds.

Age-Related Changes in Post-Translational Modifications of Proteins from Whole Male and Female Skeletal Elements

One of the key questions in forensic cases relates to some form of age inference, whether this is how old a crime scene is, when in time a particular crime was committed, or how old the victim was at the time of the crime. These age-related estimations are currently achieved through morphological methods with varying degrees of accuracy. As a result, biomolecular approaches are considered of great interest, with the relative abundances of several protein markers already recognized for their potential forensic significance; however, one of the greatest advantages of proteomic investigations over genomics ones is the wide range of post-translational modifications (PTMs) that make for a complex but highly dynamic resource of information. Here, we explore the abundance of several PTMs including the glycosylation, deamidation, and oxidation of several key proteins (collagen, fetuin A, biglycan, serum albumin, fibronectin and osteopontin) as being of potential value to the development of an age estimation tool worthy of further evaluation in forensic contexts. We find that glycosylations lowered into adulthood but deamidation and oxidation increased in the same age range.

The Crosstalk between Gut Microbiota and Nervous System: A Bidirectional Interaction between Microorganisms and Metabolome

Several studies have shown that the gut microbiota influences behavior and, in turn, changes in the immune system associated with symptoms of depression or anxiety disorder may be mirrored by corresponding changes in the gut microbiota. Although the composition/function of the intestinal microbiota appears to affect the central nervous system (CNS) activities through multiple mechanisms, accurate epidemiological evidence that clearly explains the connection between the CNS pathology and the intestinal dysbiosis is not yet available. The enteric nervous system (ENS) is a separate branch of the autonomic nervous system (ANS) and the largest part of the peripheral nervous system (PNS). It is composed of a vast and complex network of neurons which communicate via several neuromodulators and neurotransmitters, like those found in the CNS. Interestingly, despite its tight connections to both the PNS and ANS, the ENS is also capable of some independent activities. This concept, together with the suggested role played by intestinal microorganisms and the metabolome in the onset and progression of CNS neurological (neurodegenerative, autoimmune) and psychopathological (depression, anxiety disorders, autism) diseases, explains the large number of investigations exploring the functional role and the physiopathological implications of the gut microbiota/brain axis.

iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants

The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on "green" control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

The impact of genomic variants on patient response to inhaled bronchodilators: a comprehensive update

INTRODUCTION

The bronchodilator response (BDR) depends on many factors, including genetic ones. Numerous single nucleotide polymorphisms (SNPs) influencing BDR have been identified. However, despite several studies in this field, genetic variations are not currently being utilized to support the use of bronchodilators.

AREAS COVERED

In this narrative review, the possible impact of genetic variants on BDR is discussed.

EXPERT OPINION

Pharmacogenetic studies of β₂-agonists have mainly focused on ADRB2 gene. Three SNPs, A46G, C79G, and C491T, have functional significance. However, other uncommon variants may contribute to individual variability in salbutamol response. SNPs haplotypes in ADRB2 may have a role. Many variants in genes coding for muscarinic ACh receptor (mAChR) have been reported, particularly in the M₂ and, to a lesser degree, M₃ mAChRs, but no consistent evidence for a pharmacological relevance of these SNPs has been reported. Moreover, there is a link between SNPs and ethnic and/or age profiles regarding BDR. Nevertheless, replication of pharmacogenetic results is limited and often, BDR is dissociated from what is expected based on SNP identification. Pharmacogenetic studies on bronchodilators must continue. However, they must integrate data derived from a multi-omics approach with epigenetic factors that may modify BDR.

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Crosstalking interactions between P2X4 and 5-HT3A receptors

Ionotropic receptors are ligand-gated ion channels triggering fast neurotransmitter responses. Among them, P2X and 5-HT₃ receptors have been shown to physically interact each other and functionally inducing cross inhibitory responses. Nevertheless, despite the importance of P2X4 and 5-HT_3A receptors that mediate for example neuropathic pain and psychosis respectively, complementary evidence has recently started to move forward in the understanding of this interaction. In this review, we discuss current evidence supporting the mechanism of crosstalking between both receptors, from the structural to the transduction pathway level. We expect this work may guide the design of further experiments to obtain a comprehensive view for the neuropharmacological role of these interacting receptors.

Advances in single-molecule junctions as tools for chemical and biochemical analysis

The development of miniaturized electronics has led to the design and construction of powerful experimental platforms capable of measuring electronic properties to the level of single molecules, along with new theoretical concepts to aid in the interpretation of the data. A new area of activity is now emerging concerned with repurposing the tools of molecular electronics for applications in chemical and biological analysis. Single-molecule junction techniques, such as the scanning tunnelling microscope break junction and related single-molecule circuit approaches have a remarkable capacity to transduce chemical information from individual molecules, sampled in real time, to electrical signals. In this Review, we discuss single-molecule junction approaches as emerging analytical tools for the chemical and biological sciences. We demonstrate how these analytical techniques are being extended to systems capable of probing chemical reaction mechanisms. We also examine how molecular junctions enable the detection of RNA, DNA, and traces of proteins in solution with limits of detection at the zeptomole level.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Analysis of differentially expressed proteins after EHP-infection and characterization of caspase 3 protein in the whiteleg shrimp (Litopenaeus vannamei)

Whiteleg shrimp (Litopenaeus vannamei) is the most important species of shrimp farmed worldwide in terms of its economic value. Enterocytozoon hepatopenaei (EHP) infects the hepatopancreas, resulting in the hepatopancreatic microsporidiosis (HPM) of the host, which causes slow growth of the shrimp and poses a threat to the farming industry. In this study, differentially expressed proteins (DEPs) between EHP-infected and uninfected shrimp were investigated through proteomics sequencing. A total of 9908 peptides and 2092 proteins were identified. A total of 69 DEPs were identified in the hepatopancreas (HP), of which, 28 were upregulated and 41 were downregulated. Our results showed that the differences among the level of multiple proteins involved in the apoptosis were significant after the EHP infection, which indicated that the apoptosis pathway was activated in whiteleg shrimp. In addition, expression leve of caspase 3 gene were identified related to the EHP infection. Furthermore, predictions of spatial structure, analysis of phylogeny and chromosome-level linearity of the caspase 3 protein were performed as well. In conclusion, a relatively complete proteomic data set of hepatopancreas tissues in whiteleg shrimp were established in this study. Findings about genes involved in the apoptosis here will provide a further understanding of the molecular mechanism of EHP infection in the internal immunity of whiteleg shrimp.

Antagonistic Interactions in Mitochondria ROS Signaling Responses to Manganese

Antagonistic interaction refers to opposing beneficial and adverse signaling by a single agent. Understanding opposing signaling is important because pathologic outcomes can result from adverse causative agents or the failure of beneficial mechanisms. To test for opposing responses at a systems level, we used a transcriptome-metabolome-wide association study (TMWAS) with the rationale that metabolite changes provide a phenotypic readout of gene expression, and gene expression provides a phenotypic readout of signaling metabolites. We incorporated measures of mitochondrial oxidative stress (mtOx) and oxygen consumption rate (mtOCR) with TMWAS of cells with varied manganese (Mn) concentration and found that adverse neuroinflammatory signaling and fatty acid metabolism were connected to mtOx, while beneficial ion transport and neurotransmitter metabolism were connected to mtOCR. Each community contained opposing transcriptome-metabolome interactions, which were linked to biologic functions. The results show that antagonistic interaction is a generalized cell systems response to mitochondrial ROS signaling.

Vitality Revisited: The Evolving Concept of Flourishing and Its Relevance to Personal and Public Health

Human flourishing, the state of optimal functioning and well-being across all aspects of an individual's life, has been a topic of philosophical and theological discussion for centuries. In the mid-20th century, social psychologists and health scientists began exploring the concept of flourishing in the context of health and high-level wellness. However, it is only in recent years, in part due to the USD 43 million Global Flourishing Study including 22 countries, that flourishing has entered the mainstream discourse. Here, we explore this history and the rapid acceleration of research into human flourishing, defined as "the relative attainment of a state in which all aspects of a person's life are good" by the Harvard University's Flourishing Program. We also explore the construct of "vitality", which refers to a sense of aliveness, energy, and motivation; we contend that this has been neglected in the flourishing movement. We explore why incorporating measures of vitality, together with a broader biopsychosocial approach, considers all dimensions of the environment across time (the total exposome), which will greatly advance research, policies, and actions to achieve human flourishing.

The coral microbiome: towards an understanding of the molecular mechanisms of coral-microbiota interactions

Corals live in a complex, multipartite symbiosis with diverse microbes across kingdoms, some of which are implicated in vital functions, such as those related to resilience against climate change. However, knowledge gaps and technical challenges limit our understanding of the nature and functional significance of complex symbiotic relationships within corals. Here, we provide an overview of the complexity of the coral microbiome focusing on taxonomic diversity and functions of well-studied and cryptic microbes. Mining the coral literature indicate that while corals collectively harbour a third of all marine bacterial phyla, known bacterial symbionts and antagonists of corals represent a minute fraction of this diversity and that these taxa cluster into select genera, suggesting selective evolutionary mechanisms enabled these bacteria to gain a niche within the holobiont. Recent advances in coral microbiome research aimed at leveraging microbiome manipulation to increase coral's fitness to help mitigate heat stress-related mortality are discussed. Then, insights into the potential mechanisms through which microbiota can communicate with and modify host responses are examined by describing known recognition patterns, potential microbially derived coral epigenome effector proteins and coral gene regulation. Finally, the power of omics tools used to study corals are highlighted with emphasis on an integrated host-microbiota multiomics framework to understand the underlying mechanisms during symbiosis and climate change-driven dysbiosis.

A novel approach to Lactiplantibacillus plantarum: From probiotic properties to the omics insights

Lactiplantibacillus plantarum (previously known as Lactobacillus plantarum) strains are one of the lactic acid bacteria (LAB) commonly used in fermentation and their probiotic and functional properties along with their health-promoting roles come to the fore. Food-derived L. plantarum strains have shown good resistance and adhesion in the gastrointestinal tract (GI) and excellent antioxidant and antimicrobial properties. Furthermore, many strains of L. plantarum can produce bacteriocins with interesting antimicrobial activity. This probiotic properties of L. plantarum and existing in different niches give a great potential to have beneficial effects on health. It is also has been shown that L. plantarum can regulate the intestinal microbiota composition in a good way. Recently, omics approaches such as metabolomics, secretomics, proteomics, transcriptomics and genomics try to understand the roles and mechanisms of L. plantarum that are related to its functional characteristics. This review provides an overview of the probiotic properties, including the specific interactions between microbiota and host, and omics insights of L. plantarum.

Integrative analyses of biomarkers and pathways for metformin reversing cisplatin resistance in head and neck squamous cell carcinoma cells

OBJECTIVES

In this study, transcriptome sequencing were performed to elucidate the molecular mechanism by which metformin inhibits head and neck squamous cell carcinoma (HNSCC) cells progression and sensitizes HNSCC cells to chemotherapy. We aimed to propose a novel chemotherapeutic approach with high efficacy and few side effects and provide a new strategy for HNSCC treatment.

DESIGN

The effects of metformin on the biological behaviors of HNSCC cells were validated by CCK8 cell proliferation assays, would healing assays and flow cytometric apoptosis assays. The appropriate metformin concentrations for the experimental pretreatment of HNSCC cells were selected based on experimental results, and the treated cells were subjected to transcriptome sequencing. After bioinformatics analysis and intersection with a post-chemotherapy resistance dataset from the GEO database numbered GSE102787, the genes were identified and used to predict potential metformin targets after functional enrichment analysis.

RESULTS

Metformin significantly inhibited the proliferation and migration and induced the apoptosis of Cal27 and FaDu cells. A total of 284 genes that are potentially targeted by metformin during HNSCC cell sensitization were identified by bioinformatics, and ten hub genes with high connectivity were selected. In particular, Fen1 overexpression was associated with poor prognosis in HNSCC patients.

CONCLUSIONS

Our study demonstrates that Fen1 is overexpressed in HNSCC tissues compared with normal tissues and that Fen1 overexpression is a poor prognostic factor in HNSCC patients. Metformin enhances the ability of cisplatin to inhibit HNSCC progression. Further studies are needed to explore the therapeutic value of Fen1 in HNSCC.

A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA)

These days, the ratio of cancer diseases among patients has been growing day by day. Recently, many cancer cases have been reported in different clinical hospitals. Many machine learning algorithms have been suggested in the literature to predict cancer diseases with the same class types based on trained and test data. However, there are many research rooms available for further research. In this paper, the studies look into the different types of cancer by analyzing, classifying, and processing the multi-omics dataset in a fog cloud network. Based on SARSA on-policy and multi-omics workload learning, made possible by reinforcement learning, the study made new hybrid cancer detection schemes. It consists of different layers, such as clinical data collection via laboratories and tool processes (biopsy, colonoscopy, and mammography) at the distributed omics-based clinics in the network. The study considers the different cancer classes such as carcinomas, sarcomas, leukemias, and lymphomas with their types in work and processes them using the multi-omics distributed clinics in work. In order to solve the problem, the study presents omics cancer workload reinforcement learning state action reward state action "SARSA" (OCWLS) schemes, which are made up of an on-policy learning scheme on different parameters like states, actions, timestamps, reward, accuracy, and processing time constraints. The goal is to process multiple cancer classes and workload feature matching while reducing the time it takes to process in clinical hospitals that are spread out. Simulation results show that OCWLS is better than other machine learning methods regarding+ processing time, extracting features from multiple classes of cancer, and matching in the system.

Sex dimorphism and metabolic profiles in management of metabolic-associated fatty liver disease

Metabolic-associated fatty liver disease (MAFLD) refers to the build-up of fat in the liver associated with metabolic dysfunction and has been estimated to affect a quarter of the population worldwide. Although metabolism is highly influenced by the effects of sex hormones, studies of sex differences in the incidence and progression of MAFLD are scarce. Metabolomics represents a powerful approach to studying these differences and identifying potential biomarkers and putative mechanisms. First, metabolomics makes it possible to obtain the molecular phenotype of the individual at a given time. Second, metabolomics may be a helpful tool for classifying patients according to the severity of the disease and obtaining diagnostic biomarkers. Some studies demonstrate associations between circulating metabolites and early and established MAFLD, but little is known about how metabolites relate to and encompass sex differences in disease progression and risk management. In this review, we will discuss the epidemiological metabolomic studies for sex differences in the development and progression of MAFLD, the role of metabolic profiles in understanding mechanisms and identifying sex-dependent biomarkers, and how this evidence may help in the future management of the disease.

Latent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework

BACKGROUND

Current clinical routines rely more and more on "omics" data such as flow cytometry data from host and microbiota. Cohorts variability in addition to patients' heterogeneity and huge dimensions make it difficult to understand underlying structure of the data and decipher pathologies. Patients stratification and diagnostics from such complex data are extremely challenging. There is an acute need to develop novel statistical machine learning methods that are robust with respect to the data heterogeneity, efficient from the computational viewpoint, and can be understood by human experts.

RESULTS

We propose a novel approach to stratify cell-based observations within a single probabilistic framework, i.e., to extract meaningful phenotypes from both patients and cells data simultaneously. We define this problem as a double clustering problem that we tackle with the proposed approach. Our method is a practical extension of the Latent Dirichlet Allocation and is used for the Double Clustering task (LDA-DC). We first validate the method on artificial datasets, then we apply our method to two real problems of patients stratification based on cytometry and microbiota data. We observe that the LDA-DC returns clusters of patients and also clusters of cells related to patients' conditions. We also construct a graphical representation of the results that can be easily understood by humans and are, therefore, of a big help for experts involved in pre-clinical research.

Petroleum Hydrocarbon Catabolic Pathways as Targets for Metabolic Engineering Strategies for Enhanced Bioremediation of Crude-Oil-Contaminated Environments

Anthropogenic activities and industrial effluents are the major sources of petroleum hydrocarbon contamination in different environments. Microbe-based remediation techniques are known to be effective, inexpensive, and environmentally safe. In this review, the metabolic-target-specific pathway engineering processes used for improving the bioremediation of hydrocarbon-contaminated environments have been described. The microbiomes are characterised using environmental genomics approaches that can provide a means to determine the unique structural, functional, and metabolic pathways used by the microbial community for the degradation of contaminants. The bacterial metabolism of aromatic hydrocarbons has been explained via peripheral pathways by the catabolic actions of enzymes, such as dehydrogenases, hydrolases, oxygenases, and isomerases. We proposed that by using microbiome engineering techniques, specific pathways in an environment can be detected and manipulated as targets. Using the combination of metabolic engineering with synthetic biology, systemic biology, and evolutionary engineering approaches, highly efficient microbial strains may be utilised to facilitate the target-dependent bioprocessing and degradation of petroleum hydrocarbons. Moreover, the use of CRISPR-cas and genetic engineering methods for editing metabolic genes and modifying degradation pathways leads to the selection of recombinants that have improved degradation abilities. The idea of growing metabolically engineered microbial communities, which play a crucial role in breaking down a range of pollutants, has also been explained. However, the limitations of the in-situ implementation of genetically modified organisms pose a challenge that needs to be addressed in future research.

Filamentous fungi for sustainable remediation of pharmaceutical compounds, heavy metal and oil hydrocarbons

This review presents a comprehensive summary of the latest research in the field of bioremediation with filamentous fungi. The main focus is on the issue of recent progress in remediation of pharmaceutical compounds, heavy metal treatment and oil hydrocarbons mycoremediation that are usually insufficiently represented in other reviews. It encompasses a variety of cellular mechanisms involved in bioremediation used by filamentous fungi, including bio-adsorption, bio-surfactant production, bio-mineralization, bio-precipitation, as well as extracellular and intracellular enzymatic processes. Processes for wastewater treatment accomplished through physical, biological, and chemical processes are briefly described. The species diversity of filamentous fungi used in pollutant removal, including widely studied species of Aspergillus, Penicillium, Fusarium, Verticillium, Phanerochaete and other species of Basidiomycota and Zygomycota are summarized. The removal efficiency of filamentous fungi and time of elimination of a wide variety of pollutant compounds and their easy handling make them excellent tools for the bioremediation of emerging contaminants. Various types of beneficial byproducts made by filamentous fungi, such as raw material for feed and food production, chitosan, ethanol, lignocellulolytic enzymes, organic acids, as well as nanoparticles, are discussed. Finally, challenges faced, future prospects, and how innovative technologies can be used to further exploit and enhance the abilities of fungi in wastewater remediation, are mentioned.

Enhancing plant growth promoting rhizobacterial activities through consortium exposure: A review

Plant Growth Promoting Rhizobacteria (PGPR) has gained immense importance in the last decade due to its in-depth study and the role of the rhizosphere as an ecological unit in the biosphere. A putative PGPR is considered PGPR only when it may have a positive impact on the plant after inoculation. From the various pieces of literature, it has been found that these bacteria improve the growth of plants and their products through their plant growth-promoting activities. A microbial consortium has a positive effect on plant growth-promoting (PGP) activities evident by the literature. In the natural ecosystem, rhizobacteria interact synergistically and antagonistically with each other in the form of a consortium, but in a natural consortium, there are various oscillating environmental conditions that affect the potential mechanism of the consortium. For the sustainable development of our ecological environment, it is our utmost necessity to maintain the stability of the rhizobacterial consortium in fluctuating environmental conditions. In the last decade, various studies have been conducted to design synthetic rhizobacterial consortium that helps to integrate cross-feeding over microbial strains and reveal their social interactions. In this review, the authors have emphasized covering all the studies on designing synthetic rhizobacterial consortiums, their strategies, mechanism, and their application in the field of environmental ecology and biotechnology.

Current Progress, Applications and Challenges of Multi-Omics Approaches in Sesame Genetic Improvement

Sesame is one of the important traditional oil crops in the world, and has high economic and nutritional value. Recently, due to the novel high throughput sequencing techniques and bioinformatical methods, the study of the genomics, methylomics, transcriptomics, proteomics and metabonomics of sesame has developed rapidly. Thus far, the genomes of five sesame accessions have been released, including white and black seed sesame. The genome studies reveal the function and structure of the sesame genome, and facilitate the exploitation of molecular markers, the construction of genetic maps and the study of pan-genomes. Methylomics focus on the study of the molecular level changes under different environmental conditions. Transcriptomics provide a powerful tool to study abiotic/biotic stress, organ development, and noncoding RNAs, and proteomics and metabonomics also provide some support in studying abiotic stress and important traits. In addition, the opportunities and challenges of multi-omics in sesame genetics breeding were also described. This review summarizes the current research status of sesame from the perspectives of multi-omics and hopes to provide help for further in-depth research on sesame.

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control. The "Omics" field has recently gained attention, as it can help in identifying and exploring metabolites and metabolic pathways that assist cancer cells in proliferation, which can be further utilized to improve the diagnosis and treatment of skin cancer. Although skin tissues contain diverse metabolic enzymes, it remains challenging to fully characterize these metabolites. Metabolomics is a powerful omics technique that allows us to measure and compare a vast array of metabolites in a biological sample. This technology enables us to study the dermal metabolic effects and get a clear explanation of the pathogenesis of skin diseases. The purpose of this literature review is to illustrate how metabolomics technology can be used to evaluate the metabolic profile of human skin cancer, using a variety of analytical platforms including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Data collection has not been based on any analytical method.

Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model's original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.

Seminal plasma metabolomics analysis of differences in liquid preservation ability of boar sperm

The preservation of semen is pivotal in animal reproduction to ensure successful fertilization and genetic improvement of livestock and poultry. However, investigating the underlying causes of differences in sperm liquid preservation ability and identifying relevant biomarkers remains a challenge. This study utilized liquid chromatography-mass spectrometry (LC-MS) to analyze the metabolite composition of seminal plasma (SP) from two groups with extreme differences in sperm liquid preservation ability. The two groups namely the good liquid preservation ability (GPA) and the poor preservation ability (PPA). The aim was to explore the relationship between metabolite composition in SP and sperm liquid preservation ability, and to identify candidate biomarkers associated with this ability of sperm. The results revealed the identification of 756 metabolites and 70 differentially expressed metabolites (DEM) in the SP from two groups of boar semen with differing liquid preservation abilities at 17 °C. The majority of identified metabolites in the SP belonged to organic acids and derivatives as well as lipids and lipid-like molecules. The DEM in the SP primarily consisted of amino acids, peptides, and analogs. The Kyoto Encyclopedia of Genes and Genomes analysis also demonstrated that the DEM are mainly concentrated in amino acid synthesis and metabolism-related pathways (P < 0.05). Furthermore, eleven key metabolites were identified and six target amino acids were verified, and the results were consistent with the non-targeted metabolic analysis. These findings indicated that amino acids and their associated pathways play a potential role in determining boar sperm quality and liquid preservation ability. D-proline, arginine, L-citrulline, phenylalanine, leucine, DL-proline, DL-serine, and indole may serve as potential biomarkers for early assessment of boar sperm liquid preservation ability. The findings of this study are helpful in understanding the causes and mechanisms of differences in the liquid preservation ability of boar sperm, and provide valuable insights for improving semen quality assessment methods and developing novel extenders or protocols.

Neuropharmacology of Alcohol Addiction with Special Emphasis on Proteomic Approaches for Identification of Novel Therapeutic Targets

Alcohol is a generic pharmacological agent with only a few recognized primary targets. Nmethyl- D-aspartate, gamma-aminobutyric acid, glycine, 5-hydroxytryptamine 3 (serotonin), nicotinic acetylcholine receptors, and L-type Ca2+ channels and G-protein-activated inwardly rectifying K channels are all involved. Following the first hit of alcohol on specific brain targets, the second wave of indirect effects on various neurotransmitter/neuropeptide systems begins, leading to the typical acute behavioral effects of alcohol, which range from disinhibition to sedation and even hypnosis as alcohol concentrations rise. Recent research has revealed that gene regulation is significantly more complex than previously thought and does not fully explain changes in protein levels. As a result, studying the proteome directly, which differs from the genome/transcriptome in terms of complexity and dynamicity, has provided unique insights into extraordinary advances in proteomic techniques that have changed the way we can analyze the composition, regulation, and function of protein complexes and pathways underlying altered neurobiological conditions. Neuroproteomics has the potential to revolutionize alcohol research by allowing researchers to gain a better knowledge of how alcohol impacts protein structure, function, connections, and networks on a global scale. The amount of information collected from these breakthroughs can aid in identifying valuable biomarkers for early detection and improved prognosis of an alcohol use disorder and future pharmaceutical targets for the treatment of alcoholism.

Multi-Omics Methods Applied to Flower Development

Developmental processes in multicellular organisms depend on the proficiency of cells to orchestrate different gene expression programs. Over the past years, several studies of reproductive organ development have considered genomic analyses of transcription factors and global gene expression changes, modeling complex gene regulatory networks. Nevertheless, the dynamic view of developmental processes requires, as well, the study of the proteome in its expression, complexity, and relationship with the transcriptome. In this chapter, we describe a dual extraction method-for protein and RNA-for the characterization of genome expression at proteome level and its correlation to transcript expression data. We also present a shotgun proteomic procedure (LC-MS/MS) followed by a pipeline for the imputation of missing values in mass spectrometry results.

Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation

T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways

Mass spectrometry (MS) is an important tool for biological studies because it is capable of interrogating a diversity of biomolecules (proteins, drugs, metabolites) not captured via alternate genomic platforms. Unfortunately, downstream data analysis becomes complicated when attempting to evaluate and integrate measurements of different molecular classes and requires the aggregation of expertise from different relevant disciplines. This complexity represents a significant bottleneck that limits the routine deployment of MS-based multi-omic methods, despite the unmatched biological and functional insight the data can provide. To address this unmet need, our group introduced Omics Notebook as an open-source framework for facilitating exploratory analysis, reporting and integrating MS-based multi-omic data in a way that is automated, reproducible and customizable. By deploying this pipeline, we have devised a framework for researchers to more rapidly identify functional patterns across complex data types and focus on statistically significant and biologically interesting aspects of their multi-omic profiling experiments. This chapter aims to describe a protocol which leverages our publicly accessible tools to analyze and integrate data from high-throughput proteomics and metabolomics experiments and produce reports that will facilitate more impactful research, cross-institutional collaborations, and wider data dissemination.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.