‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics

A systematic review of predictive, diagnostic, and prognostic biomarkers for detecting reproductive diseases in cattle using traditional and omics approaches

Reproductive diseases and illnesses pose significant challenges in cattle farming, affecting fertility, milk production, and overall herd health. In recent years, the integration of various omics approaches, including transcriptomics, proteomics, metagenomics, miRNAomics, and metabolomics, has revolutionized the study of these conditions. This systematic review summarised the findings from studies that investigated reproductive disease biomarkers in both male and female cattle. After extracting 6137 studies according to exclusion and inclusion criteria, a total of 60 studies were included in this review. All studies identified were associated with female cattle and none were related to reproductive diseases in bulls. The analysis highlights specific biomarkers, metabolic pathways, and microbial compositions associated with bovine reproductive disease conditions, providing valuable insights into the underlying molecular mechanisms of disease. Pro-inflammatory cytokines such as IL-1β, IL-8, IL-4, IL-6, TNFα and acute-phase response proteins such as SAA and HP have been identified as promising biomarkers for bovine reproductive diseases. However, further research is needed to validate these markers clinically and to explore potential strategies for improving cow reproductive health. The role of bulls as carriers of venereal diseases has been underestimated in the current literature and therefore needs more attention to understand their impact on infectious reproductive diseases of female cattle.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Alteration of Fecal Microbiota, Fecal Metabolites, and Serum Metabolites in Dairy Cows with Pre-Retained Placenta

Retained placenta (RP) affects lactation and fertility in dairy cows and causes economic losses to the dairy industry. Therefore, screening for early warning of this disease is important. This study used multi omics techniques to reveal the metabolic differences of dairy cows before RP onset and to find potential warning markers. Fecal samples and serum samples of 90 healthy Holstein cows were collected 7 days pre-calving; 10 healthy and 10 RP cows were enrolled according to normal expulsion of fetal membranes after calving. Fecal samples were subjected to 16S rRNA sequencing and untargeted metabolomics analysis, while plasma was analyzed using targeted metabolomics. Pathogenic bacteria levels increased in the intestines of cows with RP compared to those in healthy cows. Lipid metabolites constituted the largest proportion of differential metabolites between feces and plasma. Six potential warning markers for RP in cows were identified, including two fecal microbiomics markers (Oscillospiraceae UCG-005 and Escherichia-Shigella), one fecal untargeted metabolomics marker (N-acetylmuramic acid), and three plasma targeted metabolomics markers (glycylcholic acid-3 sulfate, 7-ketolithocholic acid, and 12-ketolithocholic acid). These biomarkers can predict RP occurrence in the early perinatal period. These results lay a theoretical foundation for early nutritional intervention and pathogenesis research in dairy cows.

Analyzing the impact of synthetic and natural steroids: a study of cytochrome P450 metabolism, morphological alterations through metabolomics, and histopathological Examination

This study focuses on the comparative metabolic profiling and effects of two steroid types: natural and synthetic, specifically 17α-methyl testosterone (17α-MT) at varying concentrations (1.5, 2, and 3 mg/kg) in rainbow trout (Oncorhynchus mykiss). Over a 75-day feeding trial, growth metrics, such as feed efficiency, daily specific growth, live weight gain, total weight gain, and survival rate were systematically monitored every 15 days. At the end of the feeding trial, histopathology, immunohistochemistry, and metabolome analyses were performed in the high-concentration groups (3 mg/kg natural and 3 mg/kg synthetic), in which the lowest survival rate was determined. Key findings reveal that the type of hormone significantly influences growth parameters. While some natural steroids enhanced certain growth aspects, synthetic variants often yielded better results. The metabolomic analysis highlighted significant shifts in the metabolism of tryptophan, purine, folate, primary bile acids, phosphonates, phosphinates, and xenobiotics via cytochrome P450 pathways. Histopathologically, the natural hormone groups showed similar testicular, hepatic, muscular, gill, cerebral, renal, and intestinal tissue structures to the control, with minor DNA damage and apoptosis observed through immunohistochemistry. Conversely, the synthetic hormone groups exhibited moderate DNA damage and mild degenerative and necrotic changes in histopathology.

Integrating metabolomics into reproduction: Sperm metabolism and fertility enhancement in pigs

The last decades of research have revealed that many other factors besides gamete genomes are able to determine the reproductive outcomes. Indeed, paternal factors have been observed to be capable of modulating multiple crucial features of the reproductive process, such as sperm physiology, the maternal environment and, even, the offspring health. These recent advances have been encompassed with the emergence of OMICS technologies, as they comprehensively characterise the molecular composition of biological systems. The present narrative review aimed to take a closer look at the potential of these technologies in the field of reproductive biology. This literature revision shows that most studies up to date have followed a non-targeted approach to screen mammalian seminal plasma (SP) and sperm metabolite composition through different metabolome platforms. These studies have proposed metabolites of multiple natures as potential in vivo fertility biomarkers. Yet, targeted approaches can be used to answer specific biological question, and their power is exemplified herein. For instance, metabolomic studies have uncovered not only that glycolysis is the main ATP energy source of pig sperm, but also that sperm metabolism can trigger DNA damage, hence compromise embryo development. In conclusion, this review shows the potential of both non-targeted and targeted metabolomics for the discovery of cell pathways that govern the reproductive process. Understanding these systems could help make progress in different areas, including livestock efficient breeding, the improvement of artificial reproductive technologies, and the development of biomarkers for infertility detection.

Efficacy of Integrated Risk Score Using Omics-Based Biomarkers for the Prediction of Acute Rejection in Kidney Transplantation: A Randomized Prospective Pilot Study

Acute rejection (AR) is critical for long-term graft survival in kidney transplant recipients (KTRs). This study aimed to evaluate the efficacy of the integrated risk score of omics-based biomarkers in predicting AR in KTRs. This prospective, randomized, controlled, multicenter, pilot study enrolled 40 patients who recently underwent high-immunologic-risk kidney transplantation (KT). Five omics biomarkers were measured, namely, blood mRNA (three-gene signature), urinary exosomal miRNA (three-gene signature), urinary mRNA (six-gene signature), and two urinary exosomal proteins (hemopexin and tetraspanin-1) at 2 weeks and every 4 weeks after KT for 1 year. An integrated risk score was generated by summing each biomarker up. The biomarker group was informed about the integrated risk scores and used to adjust immunosuppression, but not the control group. The outcomes were graft function and frequency of graft biopsy. Sixteen patients in the biomarker group and nineteen in the control group completed the study. The mean estimated glomerular filtration rate after KT did not differ between the groups. Graft biopsy was performed in two patients (12.5%) and nine (47.4%) in the biomarker and control groups, respectively, with the proportion being significantly lower in the biomarker group (p = 0.027). One patient (6.3%) in the biomarker group and two (10.5%) in the control group were diagnosed with AR, and the AR incidence did not differ between the groups. The tacrolimus trough level was significantly lower in the biomarker group than in the control group at 1 year after KT (p = 0.006). Integrated omics biomarker monitoring may help prevent unnecessary or high-complication-risk biopsy and enables tailored immunosuppression by predicting the risk of AR in KTRs.

Impact of inflammatory preconditioning on murine microglial proteome response induced by focal ischemic brain injury

Preconditioning with lipopolysaccharide (LPS) induces neuroprotection against subsequent cerebral ischemic injury, mainly involving innate immune pathways. Microglia are resident immune cells of the central nervous system (CNS) that respond early to danger signals through memory-like differential reprogramming. However, the cell-specific molecular mechanisms underlying preconditioning are not fully understood. To elucidate the distinct molecular mechanisms of preconditioning on microglia, we compared these cell-specific proteomic profiles in response to LPS preconditioning and without preconditioning and subsequent transient focal brain ischemia and reperfusion, - using an established mouse model of transient focal brain ischemia and reperfusion. A proteomic workflow, based on isolated microglia obtained from mouse brains by cell sorting and coupled to mass spectrometry for identification and quantification, was applied. Our data confirm that LPS preconditioning induces marked neuroprotection, as indicated by a significant reduction in brain infarct volume. The established brain cell separation method was suitable for obtaining an enriched microglial cell fraction for valid proteomic analysis. The results show a significant impact of LPS preconditioning on microglial proteome patterns by type I interferons, presumably driven by the interferon cluster regulator proteins signal transducer and activator of transcription1/2 (STAT1/2).

Multi-omics resources for the Australian southern stuttering frog (Mixophyes australis) reveal assorted antimicrobial peptides

The number of genome-level resources for non-model species continues to rapidly expand. However, frog species remain underrepresented, with up to 90% of frog genera having no genomic or transcriptomic data. Here, we assemble the first genomic and transcriptomic resources for the recently described southern stuttering frog (Mixophyes australis). The southern stuttering frog is ground-dwelling, inhabiting naturally vegetated riverbanks in south-eastern Australia. Using PacBio HiFi long-read sequencing and Hi-C scaffolding, we generated a high-quality genome assembly, with a scaffold N50 of 369.3 Mb and 95.1% of the genome contained in twelve scaffolds. Using this assembly, we identified the mitochondrial genome, and assembled six tissue-specific transcriptomes. We also bioinformatically characterised novel sequences of two families of antimicrobial peptides (AMPs) in the southern stuttering frog, the cathelicidins and β-defensins. While traditional peptidomic approaches to peptide discovery have typically identified one or two AMPs in a frog species from skin secretions, our bioinformatic approach discovered 12 cathelicidins and two β-defensins that were expressed in a range of tissues. We investigated the novelty of the peptides and found diverse predicted activities. Our bioinformatic approach highlights the benefits of multi-omics resources in peptide discovery and contributes valuable genomic resources in an under-represented taxon.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model

In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.

Extraction of Proteins from Municipal Wastewater and Activated Sludge

Wastewater treatment plants (WWTPs) are the main barrier to cope with the increased pressure of municipal and industrial wastewater on natural water resources in terms of both polluting load and produced volumes. For this reason, WWTP's efficiency should be the highest; thus, their monitoring becomes critical. In conventional WWTPs, biodegradation of pollutants mainly occurs in the biological reactors, and an increasing interest in a deeper characterization of the biomasses involved in these processes (made of biofilms, granules, and suspended activated sludge) rose up in recent years. In this sense, the meta-omics approaches were recently developed to investigate the entire set of biomolecules of a given class in a microbial community with the same general objective: the identification of the biomolecules through the sequence similarity of high degree in the already available databases. Particularly, metaproteomics concerns the identification of all proteins in a microbial community in a given moment or condition. In this chapter, a protocol for the extraction and separation of proteins from activate sludge sampled at WWTPs is proposed.

Changes in root metabolites and soil microbial community structures in rhizospheres of sugarcanes under different propagation methods

Root metabolites and soil microbial community structure in the rhizosphere play critical roles in crop growth. Here, we assessed the efficiency of conventional and tissue culture propagation methods in modulating the soil health and microbiota in the rhizosphere of sugarcane (Saccharum officinarum L.) plants. The seeding canes were obtained using newly planted and two-year ratooned canes propagated by conventional (CSN and CSR) or tissue culture (TCN and TCR) methods. Changes in soil fertility, root metabolites and soil microbial community structure in the rhizosphere of sugarcane plants obtained using these canes were assessed. The activities of soil β-glucosidase and aminopeptidase, soil microbial biomass nitrogen, and abundances of soil beneficial microbes, both at phyla and genera levels, were significantly higher in the rhizosphere of sugarcane plants in TCN and TCR treatments than those in that of plants in CSN and CSR treatments. Furthermore, flavonoid and flavonol biosynthesis and alanine, aspartate and glutamate metabolism were significantly upregulated in the roots of TCR and TCN plants compared with those in the roots of CSN and CSR plants. These results suggest that the tissue culture propagation method is a sustainable method for sugarcane cultivation to improve soil fertility and health in sugarcane rhizosphere.

Molecular insights and omics-based understanding of plant-microbe interactions under drought stress

The detrimental effects of adverse environmental conditions are always challenging and remain a major concern for plant development and production worldwide. Plants deal with such constraints by physiological, biochemical, and morphological adaptations as well as acquiring mutual support of beneficial microorganisms. As many stress-responsive traits of plants are influenced by microbial activities, plants have developed a sophisticated interaction with microbes to cope with adverse environmental conditions. The production of numerous bioactive metabolites by rhizospheric, endo-, or epiphytic microorganisms can directly or indirectly alter the root system architecture, foliage production, and defense responses. Although plant-microbe interactions have been shown to improve nutrient uptake and stress resilience in plants, the underlying mechanisms are not fully understood. "Multi-omics" application supported by genomics, transcriptomics, and metabolomics has been quite useful to investigate and understand the biochemical, physiological, and molecular aspects of plant-microbe interactions under drought stress conditions. The present review explores various microbe-mediated mechanisms for drought stress resilience in plants. In addition, plant adaptation to drought stress is discussed, and insights into the latest molecular techniques and approaches available to improve drought-stress resilience are provided.

Using custom-built primers and nanopore sequencing to evaluate CO-utilizer bacterial and archaeal populations linked to bioH2 production

The microbial community composition of five distinct thermophilic hot springs was effectively described in this work, using broad-coverage nanopore sequencing (ONT MinION sequencer). By examining environmental samples from the same source, but from locations with different temperatures, bioinformatic analysis revealed dramatic changes in microbial diversity and archaeal abundance. More specifically, no archaeal presence was reported with universal bacterial primers, whereas a significant archaea presence and also a wider variety of bacterial species were reported. These results revealed the significance of primer preference for microbiomes in extreme environments. Bioinformatic analysis was performed by aligning the reads to 16S microbial databases for identification using three different alignment methods, Epi2Me (Fastq 16S workflow), Kraken, and an in-house BLAST tool, including comparison at the genus and species levels. As a result, this approach to data analysis had a significant impact on the genera identified, and thus, it is recommended that use of multiple analysis tools to support findings on taxonomic identification using the 16S region until more precise bioinformatics tools become available. This study presents the first compilation of the ONT-based inventory of the hydrogen producers in the designated hot springs in Türkiye.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Expanding the concept of serotoninomics: perspectives for serotonin studies in the 20's of the 21st century

Surely, Vittorio Erspamer, discoverer of Enteramine in 1935, and Irvine Page, Maurice M. Rapport and Arda Green, discoverers of Serotonin in 1948, never imagined the biological importance that this fundamental molecule has in the living beings of our planet; from its physiological, passing through endocrine, neural, developmental and reproductive functions and even its role in evolution. For this reason, our workgroup is commemorating these researchers and celebrating their great discovery, which deeply influenced science and medicine, in the present perspective article. As a consequence of their seminal work, and the work of many other researchers in the field of serotonin over the following years, now we stand in front of the practical concept of "Serotoninomics," which we think will contribute to find out precise answers regarding basic, clinical, and translational research related to serotonin, just as the emerging medical and "omics" sciences have done before.

Multi-omics Integration and Epilepsy: Towards a Better Understanding of Biological Mechanisms

The epilepsies are a group of complex neurological disorders characterised by recurrent seizures. Approximately 30% of patients fail to respond to anti-seizure medications, despite the recent introduction of many new drugs. The molecular processes underlying epilepsy development are not well understood and this knowledge gap impedes efforts to identify effective targets and develop novel therapies against epilepsy. Omics studies allow a comprehensive characterisation of a class of molecules. Omics-based biomarkers have led to clinically validated diagnostic and prognostic tests for personalised oncology, and more recently for non-cancer diseases. We believe that, in epilepsy, the full potential of multi-omics research is yet to be realised and we envisage that this review will serve as a guide to researchers planning to undertake omics-based mechanistic studies.

Epigenetics Approaches toward Precision Medicine for Idiopathic Pulmonary Fibrosis: Focus on DNA Methylation

Genetic information is not transmitted solely by DNA but by the epigenetics process. Epigenetics describes molecular missing link pathways that could bridge the gap between the genetic background and environmental risk factors that contribute to the pathogenesis of pulmonary fibrosis. Specific epigenetic patterns, especially DNA methylation, histone modifications, long non-coding, and microRNA (miRNAs), affect the endophenotypes underlying the development of idiopathic pulmonary fibrosis (IPF). Among all the epigenetic marks, DNA methylation modifications have been the most widely studied in IPF. This review summarizes the current knowledge concerning DNA methylation changes in pulmonary fibrosis and demonstrates a promising novel epigenetics-based precision medicine.

Research trends of omics in ulcerative colitis: A bibliometric analysis

Background

Omics has emerged as a promising biological science to shed light on the etiology, pathogenesis, and treatment of ulcerative colitis (UC). At present, although research on the omics of UC has drawn global attention, there is still a lack of bibliometric analysis in this field. This study aimed to access the trends and hotspots of omics in UC research.

Method

Publications related to omics in UC from 1 January 2000 to 15 October 2022 were retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and the online bibliometric analysis platform “Bibliometrix” were adopted to extract and visualize information.

Results

A total of 385 publications were finally included and the annual number of publications fluctuated. The trend in publications increased rapidly after 2019. The United States showed its dominant position in several publications, total citations, and international collaborations. The top five research organizations for publications on the research of omics in UC were Harvard Medical School, the Icahn School of Medicine at Mount Sinai, Karolinska Institutet, the Brigham and Women's Hospital, and the Massachusetts General Hospital. Ashwin Ananthakrishnan from the Massachusetts General Hospital was the most productive author, and Séverine Vermeire from the Catholic University of Leuven was co-cited most often. Inflammatory bowel disease was the most popular and co-cited journal in this field. The reference with citation bursts and trend topics showed that “ulcerative colitis,” “inflammatory bowel disease,” “microbiome,” “transcriptomics,” “genomics,” “metabolomics,” “proteomics,” “dysbiosis,” “biomarkers,” “loci,” and “therapy” are currently research hotspots.

Conclusion

Our study presents several important insights into the research trends and developments in the field of omics in UC, which will provide key information for further research.

Omics approaches to understand cocoa processing and chocolate flavor development: A review

The global market of chocolate has increased worldwide during the last decade and is expected to reach a value of USD 200 billion by 2028. Chocolate is obtained from different varieties of Theobroma cacao L, a plant domesticated more than 4000 years ago in the Amazon rainforest. However, chocolate production is a complex process requiring extensive post-harvesting, mainly involving cocoa bean fermentation, drying, and roasting. These steps have a critical impact on chocolate quality. Standardizing and better understanding cocoa processing is, therefore, a current challenge to boost the global production of high-quality cocoa worldwide. This knowledge can also help cocoa producers improve cocoa processing management and obtain a better chocolate. Several recent studies have been conducted to dissect cocoa processing via omics analysis. A vast amount of data has been produced regarding omics studies of cocoa processing performed worldwide. This review systematically analyzes the current data on cocoa omics using data mining techniques and discusses opportunities and gaps for cocoa processing standardization from this data. First, we observed a recurrent report in metagenomics studies of species of the fungi genus Candida and Pichia as well as bacteria from the genus Lactobacillus, Acetobacter, and Bacillus. Second, our analyzes of the available metabolomics data showed clear differences in the identified metabolites in cocoa and chocolate from different geographical origin, cocoa type, and processing stage. Finally, our analysis of peptidomics data revealed characteristic patterns in the gathered data including higher diversity and lower size distribution of peptides in fine-flavor cocoa. In addition, we discuss the current challenges in cocoa omics research. More research is still required to fill gaps in central matter in chocolate production as starter cultures for cocoa fermentation, flavor evolution of cocoa, and the role of peptides in the development of specific flavor notes. We also offer the most comprehensive collection of multi-omics data in cocoa processing gathered from different research articles.

Impact of Washing Processes on RNA Quantity and Quality in Patient-Derived Colorectal Cancer Tissues

Background: Colorectal cancer (CRC) is a common and lethal cancer worldwide. Extraction of high-quality RNA from CRC samples plays a key role in scientific research and translational medicine. Specimen collection and washing methods that do not compromise RNA quality or quantity are needed to ensure high quality specimens for gene expression analysis and other RNA-based downstream applications. We investigated the effect of tissue specimen collection and different preparation processes on the quality and quantity of RNA extracted from surgical CRC tissues. Materials and Methods: After surgical resection, tissues were harvested and prepared with various washing processes in a room adjacent to the operating room. One hundred fourteen tissues from 36 CRC patients were separately washed in either cold phosphate-buffered saline reagent (n = 34) or Dulbecco's modified Eagle's medium (DMEM; n = 34) for 2-3 minutes until the stool was removed, and unwashed specimens served as controls (n = 34). Six tissue specimens were washed and immersed in DMEM for up to 1 hour at 4°C. Before RNA extraction, all specimens were kept in the stabilizing reagent for 3 months at -80°C. RNA was extracted, and the concentration per milligram of tissue was measured. RNA quality was assessed using the RNA integrity number (RIN) value. Results: Different washing processes did not result in significant differences in RNA quantity or RIN values. In the six tissues that were washed and immersed in DMEM for 1 hour, RIN values significantly decreased. The quality of the extracted RNA from most specimens was excellent with the average RIN greater than 7. Conclusions: RNA is stable in specimens washed in different processes for short periods, but RIN values may decrease with prolonged wash times.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Revealing the diversity of bacteria and fungi in the active layer of permafrost at Spitsbergen island (Arctic) - Combining classical microbiology and metabarcoding for ecological and bioprospecting exploration

Arctic soils are constantly subjected to extreme environmental conditions such as low humidity, strong winds, high salinity, freeze-thaw cycles, UV exposition, and low nutrient availability, therefore, they have developed unique microbial ecosystems. These environments provide excellent opportunities to study microbial ecology and evolution within pristine (i.e. with limited anthropogenic influence) regions since the High Arctic is still considered one of the wildest and least explored environments on the planet. This environment is also of interest for the screening and recovery of unique microbial strains suitable for various biotechnological applications. In this study, a combination of culture-depended and culture-independent approaches was used to determine the cultivation bias in studies of the diversity of cold-active microorganisms. Cultivation bias is a reduction in recovered diversity, introduced when applying a classical culturing technique. Six different soil types, collected in the vicinity of the Polish Polar Station Hornsund (Spitsbergen, Norway), were tested. It was revealed that the used media allowed recovery of only 6.37 % of bacterial and 20 % of fungal genera when compared with a culture-independent approach. Moreover, it was shown that a combination of R2A and Marine Broth media recovered as much as 93.6 % of all cultivable bacterial genera detected in this study. Based on these results, a novel protocol for genome-guided bioprospecting, combining a culture-dependent approach, metabarcoding, next-generation sequencing, and genomic data reuse was developed. With this methodology, 14 psychrotolerant, multi-metal-resistant strains, including the highly promising Rhodococcus spp., were obtained. These strains, besides increased metal tolerance, have a petroleum hydrocarbon utilization capacity, and thus may be good candidates for future bioremediation technologies, also suited to permanently cold regions.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

Precision Medicine in Asthma Therapy

Asthma is a complex, heterogeneous disease that necessitates a proper patient evaluation to decide the correct treatment and optimize disease control. The recent introduction of new target therapies for the most severe form of the disease has heralded a new era of treatment options, intending to treat and control specific molecular pathways in asthma pathophysiology. Precision medicine, using omics sciences, investigates biological and molecular mechanisms to find novel biomarkers that can be used to guide treatment selection and predict response. The identification of reliable biomarkers indicative of the pathological mechanisms in asthma is essential to unravel new potential treatment targets. In this chapter, we provide a general description of the currently available -omics techniques, focusing on their implications in asthma therapy.

Omics as a Tool to Help Determine the Effectiveness of Supplements

There has been considerable interest in dietary supplements in the last two decades. Companies are releasing new specifics at an alarming pace, while dietary supplements are one of the less-studied substances released for public consumption. However, access to state-of-the-art and high-throughput techniques, such as the ones used in omics, make it possible to check the impact of a substance on human transcriptome or proteome and provide answers to whether its use is reasonable and beneficial. In this review, the main domains of omics are briefly introduced. The review focuses on the three most widely used omics techniques: NGS, LC-MS, NMR, and their usefulness in studying dietary supplements. Examples of studies are described for some of the most commonly supplemented substances, such as vitamins: D, E, A, and plant extracts: resveratrol, green tea, ginseng, and curcumin extract. Techniques used in omics have proven to be useful in studying dietary supplements. NGS techniques are helpful in identifying pathways that change upon supplementation and determining polymorphisms or conditions that qualify for the necessity of a given supplementation. LC-MS techniques are used to establish the serum content of supplemented a compound and its effects on metabolites. Both LC-MS and NMR help establish the actual composition of a compound, its primary and secondary metabolites, and its potential toxicity. Moreover, NMR techniques determine what conditions affect the effectiveness of supplementation.

Australian Donation and Transplantation Biobank: A Research Biobank Integrated Within a Deceased Organ and Tissue Donation Program

We aimed to facilitate the donation of tissue samples for research by establishing a centralized system integrated in the organ donation program for collection, storage, and distribution of samples (the Australian Donation and Transplantation Biobank [ADTB]).

Methods

Feasibility of a research biobank integrated within the deceased organ and tissue donation program was assessed. DonateLife Victoria sought consent for ADTB donation after consent was received for organ donation for transplantation from the donor's senior available next of kin. ADTB samples were collected during donation surgery and distributed fresh to researchers or stored for future research. The main outcome measures were ADTB donation rates, ADTB sample collection, ADTB sample use, and to identify ethical considerations.

Results

Over 2 y, samples were collected for the ADTB from 69 donors (28% of 249 donors). Samples were obtained from the spleen (n = 59, 86%), colon (n = 57, 83%), ileum (n = 56, 82%), duodenum (n = 55, 80%), blood (n = 55, 80%), bone marrow (n = 55, 80%), skin (n = 54, 78%), mesenteric lymph nodes (n = 56, 81%), liver (n = 21, 30%), lung (n = 29, 42%), and lung-draining lymph node (n = 29, 42%). Heart (n = 20), breast (n = 1), and lower urinary tract (n = 1) samples were obtained in the second year. Five hundred fifty-six samples were used in 19 ethics-approved research projects spanning the fields of immunology, microbiology, oncology, anatomy, physiology, and surgery.

Conclusions

The integration of routine deceased donation and transplantation activities with a coordinated system for retrieval and allocation of donor samples for use in a range of research projects is feasible and valuable.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Proteomic study on the lymphocytes from pregnant Wistar rat females treated with immunosuppressive regimen

Kidney transplantation remains the therapeutic option for patients with end-stage kidney disease. Current immunosuppressive regimens are efficient in combating acute kidney rejection. However, insights into chronic kidney allograft injury remains limited. Simultaneously, pregnancy is more common after kidney transplantation than during dialysis treatment. Due to ethical issues, comprehensive studies on the impact of immunosuppressive regimens on pregnancy are challenging. The study aimed to investigate the proteomic status of lymphocytes obtained from pregnant female rats under immunosuppressive treatment. The experiment involved a group of 10 female, pregnant Wistar rats, five of which were treated with tacrolimus, mofetil mycophenolate, and glucocorticosteroids; five were used as control. The lymphocytes were obtained and analyzed with mass spectrometry. Measurements were processed by a database search in the ProteinPilot software with a cutoff of 1% false discovery rate. The outcomes were verified statistically by a t-test (p value < 0.05) regarding proteins up- and downregulation. A total of 2082 proteins were identified in all experiments. Eight hundred five proteins were quantified in an absolute manner in a data-independent acquisition-total protein approach analysis. Ninety-five proteins were recognized as present at different concentrations in analyzed groups and were annotated to intracellular pathways. The proteins involved in nonsense-mediated decay and L13a-mediated translational silencing of ceruloplasmin expression were recognized as downregulated. The set of proteins clinically identified as acute phase proteins was upregulated. Despite the blockade of adaptive cellular immunity, the lymphocytes in the analyzed group reveal sustained proinflammatory status with decreased ability to regulate translation. This potentially affects pregnancy and immunity.

The change of plasma metabolic profile and gut microbiome dysbiosis in patients with rheumatoid arthritis

Objective

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease, which is associated with progressive disability, systemic complications, and early death. But its etiology and pathogenesis are not fully understood. We aimed to investigate the alterations in plasma metabolite profiles, gut bacteria, and fungi and their role of them in the pathogenesis of RA.

Methods

Metabolomics profiling of plasma from 363 participants including RA (n = 244), systemic lupus erythematosus (SLE, n = 50), and healthy control (HC, n = 69) were performed using the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The differentially expressed metabolites were selected among groups and used to explore important metabolic pathways. Gut microbial diversity analysis was performed by 16S rRNA sequencing and ITS sequencing (RA = 195, HC = 269), and the specific microbial floras were identified afterward. The diagnosis models were established based on significant differential metabolites and microbial floras, respectively.

Results

There were 63 differential metabolites discovered between RA and HC groups, mainly significantly enriched in the arginine and proline metabolism, glycine, serine, and threonine metabolism, and glycerophospholipid metabolism between RA and HC groups. The core differential metabolites included L-arginine, creatine, D-proline, ornithine, choline, betaine, L-threonine, LysoPC (18:0), phosphorylcholine, and glycerophosphocholine. The L-arginine and phosphorylcholine were increased in the RA group. The AUC of the predictive model was 0.992, based on the combination of the 10 differential metabolites. Compared with the SLE group, 23 metabolites increased and 61 metabolites decreased in the RA group. However, no significant metabolic pathways were enriched between RA and SLE groups. On the genus level, a total of 117 differential bacteria genera and 531 differential fungal genera were identified between RA and HC groups. The results indicated that three bacteria genera (Eubacterium_hallii_group, Escherichia-Shigella, Streptococcus) and two fungal genera (Candida and Debaryomyces) significantly increased in RA patients. The AUC was 0.80 based on a combination of six differential bacterial genera and the AUC was 0.812 based on a combination of seven differential fungal genera. Functional predictive analysis displayed that differential bacterial and differential fungus both were associated with KEGG pathways involving superpathway of L-serine and glycine biosynthesis I, arginine, ornithine, and proline interconversion.

Conclusion

The plasma metabolism profile and gut microbe profile changed markedly in RA. The glycine, serine, and threonine metabolism and arginine and proline metabolism played an important role in RA.

Untargeted muscle tissue metabolites profiling in young, adult, and old rats supplemented with tocotrienol-rich fraction

The greatest significant influence on human life span and health is inevitable ageing. One of the distinguishing characteristics of ageing is the gradual decrease of muscle mass and physical function. There has been growing evidence that tocotrienol can guard against age-associated chronic diseases and metabolic disorders. This study aimed to elucidate the effects of tocotrienol-rich fraction (TRF) on muscle metabolomes and metabolic pathways in ageing Sprague Dawley (SD) rats. Three months, 9 months, and 21 months old male SD rats were divided into control and treated groups with 10 rats per group. Rats in control and treated groups were given 60 mg/kg body weight/day of palm olein and 60 mg/kg body weight/day of TRF, respectively, via oral gavage for 3 months. Muscle performance was assessed at 0 and 3 months of treatment by measuring muscle strength and function. Our results showed that TRF treatment caused a significant increase in the swimming time of the young rats. Comparison in the control groups showed that metabolites involved in lipid metabolisms such as L-palmitoyl carnitine and decanoyl carnitine were increased in ageing. In contrast, several metabolites, such as 3-phosphoglyceric acid, aspartic acid and aspartyl phenylalanine were decreased. These findings indicated that muscle metabolomes involved in lipid metabolism were upregulated in aged rats. In contrast, the metabolites involved in energy and amino acid metabolism were significantly downregulated. Comparison in the TRF-supplemented groups showed an upregulation of metabolites involved in energy and amino acid metabolism. Metabolites such as N6-methyl adenosine, spermine, phenylalanine, tryptophan, aspartic acid, histidine, and N-acetyl neuraminic acid were up-regulated, indicating promotion of amino acid synthesis and muscle regeneration. Energy metabolism was also improved in adult and old rats with TRF supplementation as indicated by the upregulation of nicotinamide adenine dinucleotide and glycerol 3-phosphate compared to the control group. In conclusion, the mechanism underlying the changes in skeletal muscle mass and functions in ageing was related to carbohydrate, lipid and amino acid metabolism. Tocotrienol supplementation showed beneficial effects in alleviating energy and amino acid synthesis that may promote the regeneration and renewal of skeletal muscle in ageing rats.

The Role of Systems Biology in Deciphering Asthma Heterogeneity

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.

Biological effect of dysregulated LBX1 on adolescent idiopathic scoliosis through modulating muscle carbohydrate metabolism

BACKGROUND CONTEXT

Abnormal energy metabolism such as lower body weight and body mass index (BMI) and less fat mass is widely reported in patients with adolescent idiopathic scoliosis (AIS) and has been implicated in deformity development. However, the underlying mechanism is largely unclear. LBX1 is one of the promising AIS predisposing genes validated by multicenter studies.

PURPOSE

This study aimed to identify differentially expressed proteins (DEPs) relating to energy metabolism in AIS by using proteomic and metabolic analysis and to explore if the expression of these DEPs is associated with clinical parameters and modulated by LBX1.

STUDY DESIGN

This is a cross-sectional study using clinical data and biological samples followed by basic study using a cellular model.

PATIENT SAMPLE

Plasma samples were collected from Chinese girls with nonprogressive and progressive AIS (N=7 and 8, respectively) and age-matched healthy girls (N=50). Paraspinal muscle tissues were collected intraoperatively from concave and convex side of the apex of the major spinal curve in AIS (N=24) and either side from nonscoliosis patients (N=14).

OUTCOME MEASURES

Radiological Cobb angle and basic anthropometric data of recruited subjects were measured. The DEPs and metabolites were compared in plasma using proteomics and metabolomics technique. The relative expression of selected genes was measured in muscles.

METHODS

Plasma samples from AIS were collected at first clinical visit and were further divided into nonprogressive or progressive groups according to Cobb angle changes in 6-year follow-up. Age-matched healthy girls were recruited as control. High-performance liquid chromatography-mass spectrometry based proteomic analysis was carried out in three groups to identify DEPs and their annotated metabolic pathways. An independent cohort was used for validation by gas chromatography-mass spectrometry based metabolomic analysis. Paraspinal muscles were subjected to quantitative polymerase chain reaction (qPCR) followed by correlation analysis. Human skeletal muscle myoblast (HSMM) was used as the cellular model.

RESULTS

The likelihood of aberrant galactose metabolism and glycolysis was found to be associated with AIS curve progression as evidenced by the thirteen DEPs and seven related metabolites according to proteomic and metabolomic analysis. Some of the DEPs showed significantly altered expression in AIS concave and convex sides paraspinal muscles compared with those in nonscoliosis control. Four DEPs were found significantly and negatively correlated with LBX1 in AIS convex side paraspinal muscles. Overexpressing LBX1 in HSMM cells led to increased expression of three DEPs and decreased expression of three DEPs, respectively.

CONCLUSIONS

This is the first integrated proteomic and metabolomic analysis on AIS. Our findings show dysregulated galactose metabolism and glycolysis pathways in progressive group of AIS, suggesting the presence of abnormal energy metabolism at early stage of this disease, and their association with higher risk of progressing into more severe curvature. Evidence from ex vivo study with human muscle biopsies and in vitro study with human myoblast cells propose the possible effect of LBX1 on these two pathways in skeletal muscles. The present study provides new evidence of LBX1 function in AIS via modulating effect on the expression of energy metabolism related genes. This study might provide new insights into etiopathogenesis and development of novel treatment strategy targeting on abnormal body weight and BMI in patients with AIS. Additionally, the plasma proteomic and metabolomic studies suggested new candidates as biomarkers for establishing predictive model for AIS onset/progression.

Rapid Opto-electrochemical Differentiation of Marine Phytoplankton

The use of electro-generated oxidants in seawater facilitates the discrimination of different plankton groups via monitoring the decay in real time of their chlorophyll-a (chl-a) fluorescence signals following potentiostatic initiation of electrolysis in their vicinity (Yang M.Chem. Sci.2019, 10( (34), ), 7988-7993). In this paper, we explore the sensitivity of phytoplankton to different chemical species produced at various potentials in seawater. At low potentials, the oxidation of ca. millimolar bromide naturally present in seawater to hypobromous acid 'switches-off' the chl-a signal of individual Chlamydomonas concordia cells (green algae) located on the electrode surface within tens of seconds of the potential onset. At higher oxidative potentials, the oxidation of chloride and water produces oxidants (Cl₂, OH, H₂O₂, etc.) that are also lethal to the plankton. To deconvolute the contributions to the response from the chemical identity of the oxidant and the amount of charge delivered to 'titrate' the individual living plankton using the loss of fluorescence as the 'end point', we introduce a ramped galvanostatic method. This approach enabled the controlled injection of charge applied to a bespoke electrochemical cell in which the plankton are immobilized on an electrode surface for rapid and sensitive measurement. It is shown that the number of moles (charge) of oxidants required to react leading to chl-a switch-off is independent of the chemical identity of the electro-generated oxidant(s) among hypobromous acid, chlorine, or water-derived oxidants. Comparative experiments between C. concordia and Emiliania huxleyi (where the latter are encapsulated by extracellular plates of calcium carbonate) show that significantly different amounts of absolute charge (moles of electro-generated oxidants) are required in each case to 'switch-off' the chl-a signal. The method provides the basis for a tool that could distinguish between different plankton cells within ca. 2 min including the setup time.

miR-1/AMPK-Mediated Glucose and Lipid Metabolism under Chronic Hypothermia in the Liver of Freshwater Drum, Aplodinotus grunniens

Our previous study demonstrated that low temperature could induce hepatic inflammation and suppress the immune and oxidation resistance of freshwater drum. However, the metabolism, especially the glucose and lipid metabolism involved, is poorly studied. To further explore the chronic hypothermia response of freshwater drum, an 8-day hypothermia experiment was conducted at 10 °C to investigate the effect of chronic hypothermia on glucose and lipid metabolism via biochemical and physiological indexes, and metabolic enzyme activities, miRNAs and mRNA-miRNA integrate analysis in the liver. Plasma and hepatic biochemical parameters reveal chronic hypothermia-promoted energy expenditure. Metabolic enzyme levels uncover that glycolysis was enhanced but lipid metabolism was suppressed. Differentially expressed miRNAs induced by hypothermia were mainly involved in glucose and lipid metabolism, programmed cell death, disease, and cancerization. Specifically, KEGG enrichment indicates that AMPK signaling was dysregulated. mRNA-miRNA integrated analysis manifests miR-1 and AMPK, which were actively co-related in the regulatory network. Furthermore, transcriptional expression of key genes demonstrates hypothermia-activated AMPK signaling by miR-1 and subsequently inhibited the downstream glucogenic and glycogenic gene expression and gene expression of fatty acid synthesis. However, glycogenesis was alleviated to the control level while fatty acid synthesis was still suppressed at 8 d. Meanwhile, the gene expressions of glycolysis and fatty acid oxidation were augmented under hypothermia. In conclusion, these results suggest that miR-1/AMPK is an important target for chronic hypothermia control. It provides a theoretical basis for hypothermia resistance on freshwater drum.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Investigating the impact of RNA integrity variation on the transcriptome of human leukemic cells

High RNA integrity is essential for good quality of transcriptomics profiling. Nevertheless, in some cases samples with low RNA integrity is the only available material to study. This work was set to investigate the impact of thermal-induced RNA degradation on the transcriptomic profiles of human leukemic cells. DNA microarray-based transcriptomics was conducted on two groups of samples; high RNA integrity samples (n = 4) and low RNA integrity samples (n = 5). RNA degradation caused limited but noticeable changes in the transcriptomes. Only 1945 (6.7%) of 29,230 genes showed altered quantitation (fold change ≥ two-fold, p value ≤ 0.03, corrected p value ≤ 0.05). RNA degradation had the most impact on short transcripts and those with short distance between their 5’end and the probe binding position. Overall, the present work identified the genes whose relative quantification is sensitive to RNA degradation. Therefore, altered expression of these genes should be interpreted with caution when studied in low integrity RNA samples.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.

An Integrated Multi-Omic Network Analysis Identifies Seizure-Associated Dysregulated Pathways in the GAERS Model of Absence Epilepsy

Absence epilepsy syndromes are part of the genetic generalized epilepsies, the pathogenesis of which remains poorly understood, although a polygenic architecture is presumed. Current focus on single molecule or gene identification to elucidate epileptogenic drivers is unable to fully capture the complex dysfunctional interactions occurring at a genetic/proteomic/metabolomic level. Here, we employ a multi-omic, network-based approach to characterize the molecular signature associated with absence epilepsy-like phenotype seen in a well validated rat model of genetic generalized epilepsy with absence seizures. Electroencephalographic and behavioral data was collected from Genetic Absence Epilepsy Rats from Strasbourg (GAERS, n = 6) and non-epileptic controls (NEC, n = 6), followed by proteomic and metabolomic profiling of the cortical and thalamic tissue of rats from both groups. The general framework of weighted correlation network analysis (WGCNA) was used to identify groups of highly correlated proteins and metabolites, which were then functionally annotated through joint pathway enrichment analysis. In both brain regions a large protein-metabolite module was found to be highly associated with the GAERS strain, absence seizures and associated anxiety and depressive-like phenotype. Quantitative pathway analysis indicated enrichment in oxidative pathways and a downregulation of the lysine degradation pathway in both brain regions. GSTM1 and ALDH2 were identified as central regulatory hubs of the seizure-associated module in the somatosensory cortex and thalamus, respectively. These enzymes are involved in lysine degradation and play important roles in maintaining oxidative balance. We conclude that the dysregulated pathways identified in the seizure-associated module may be involved in the aetiology and maintenance of absence seizure activity. This dysregulated activity could potentially be modulated by targeting one or both central regulatory hubs.

Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics

When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets. Metabolomics, the comprehensive profiling of metabolite abundances, are typically measured using mass spectrometry technologies that often introduce missingness via multiple mechanisms: (1) the metabolite signal may be smaller than the instrument limit of detection; (2) the conditions under which the data are collected and processed may lead to missing values; (3) missing values can be introduced randomly. Missingness resulting from mechanism (1) would be classified as Missing Not At Random (MNAR), that from mechanism (2) would be Missing At Random (MAR), and that from mechanism (3) would be classified as Missing Completely At Random (MCAR). Two common approaches for handling missing data are the following: (1) omit missing data from the analysis; (2) impute the missing values. Both approaches may introduce bias and reduce statistical power in downstream analyses such as testing metabolite associations with clinical variables. Further, standard imputation methods in metabolomics often ignore the mechanisms causing missingness and inaccurately estimate missing values within a data set. We propose a mechanism-aware imputation algorithm that leverages a two-step approach in imputing missing values. First, we use a random forest classifier to classify the missing mechanism for each missing value in the data set. Second, we impute each missing value using imputation algorithms that are specific to the predicted missingness mechanism (i.e., MAR/MCAR or MNAR). Using complete data, we conducted simulations, where we imposed different missingness patterns within the data and tested the performance of combinations of imputation algorithms. Our proposed algorithm provided imputations closer to the original data than those using only one imputation algorithm for all the missing values. Consequently, our two-step approach was able to reduce bias for improved downstream analyses.

Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection

Kidney transplantation is the preferred treatment for patients with end-stage kidney disease, because it prolongs survival and improves quality of life. Allograft biopsy is the gold standard for diagnosing allograft rejection. However, it is invasive and reactive, and continuous monitoring is unrealistic. Various biomarkers for diagnosing allograft rejection have been developed over the last two decades based on omics technologies to overcome these limitations. Omics technologies are based on a holistic view of the molecules that constitute an individual. They include genomics, transcriptomics, proteomics, and metabolomics. The omics approach has dramatically accelerated biomarker discovery and enhanced our understanding of multifactorial biological processes in the field of transplantation. However, clinical application of omics-based biomarkers is limited by several issues. First, no large-scale prospective randomized controlled trial has been conducted to compare omics-based biomarkers with traditional biomarkers for rejection. Second, given the variety and complexity of injuries that a kidney allograft may experience, it is likely that no single omics approach will suffice to predict rejection or outcome. Therefore, integrated methods using multiomics technologies are needed. Herein, we introduce omics technologies and review the latest literature on omics biomarkers predictive of allograft rejection in kidney transplant recipients.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

BACKGROUND

Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.

AIM OF REVIEW

To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Let's Get Back to Normal? COVID-19 and the Logic of Cure

The COVID-19 pandemic has inversed certainties of absolutes of cure in everyday life but paradoxically this has occurred during a time when novel scientific advancements seem to herald a new frontier of cures for rare diseases, chronic conditions, disabilities and viruses that were previously incurable. In this paper, I illustrate the development of a logic of cure by first of all noting a lacuna in the medical sociological and anthropological literature, where although a lot of empirical research and theoretical work to understand cure has been undertaken, there has been no sociology or anthropology of cure. Using three case studies, I examine what they reveal about the logic of cure. Firstly, I argue that there is a development of a bioethics of cure in reactions of disability community and disabled people to care as cure during the COVID-19 pandemic. The second case-study focuses on understanding limitations of vaccines and how people react against such indeterminancies of loss of absolutes of cure. Lastly, the final case study describes how while there are cures, for example, for rare genetic conditions, they are often initially curated with long-term cost-benefit analysis for the Global North. In conclusion, it is found that many of the developments within sociology and anthropology are missing from a logic of cure and that a new theory of cure has to develop.

Latest update on predictive indicators, risk factors, and 'Omic' technologies research of retained placenta in dairy cattle- review article

Retained placenta is a pathological condition defined as failure to expel fetal membranes within 12 -24h after parturition. It instigates important financial losses to the dairy industry causing considerable economic loss. It is a multifactorial disorder. Although different hypotheses have been proposed, contradictory results and no single factor explains the development of the disease. This indicates that there are overall significant gaps in our knowledge about the various factors affecting the etiopathology and pathobiology of the retained placenta. Thus, it will be of great interest to investigate the potential contributing factors that influence the occurrence of RP which provide us with the mechanisms to avoid the occurrence of the disease. In addition, different predictive indicators of RP should be part of our research, thus providing the essential procedures applied to deal with the disease and the development of novel therapeutic and prophylactic strategies in its management. Furthermore, lately, the molecular role takes attention to understanding its effect in the occurrence of the disease. Such information might help dairy scientists in the future to design better management and preventive practices for RP in dairy cows.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

Quantitative proteomic analysis of serum-purified exosomes identifies putative pre-eclampsia-associated biomarkers

BACKGROUND

The high incidence of pre-eclampsia, which affects 2-7% of all pregnancies, remains a major health concern. Detection of pre-eclampsia before the appearance of clinical symptoms is essential to allow early intervention, and would benefit from identification of plasma/serum biomarkers to help guide diagnosis and treatment. Liquid biopsy has emerged as a promising source of protein biomarkers that circumvents some of the inherent challenges of proteome-wide analysis of plasma/serum. In this respect, purified exosomes have the added benefit of being carriers of intercellular communication both in physiological and pathological conditions.

METHODS

We compared the protein complement of purified exosomes from three different collections of control and pre-eclamptic serum samples, obtained at the end of the second trimester of pregnancy and at delivery. We employed shotgun label-free proteomics to investigate differential protein expression, which was then validated by targeted proteomics.

RESULTS

We developed a purification method that yielded highly enriched exosome preparations. The presence of specific pregnancy protein markers suggested that a significant proportion of purified exosomes derived from tissues related to pregnancy. Quantitative proteomic analyses allowed us to identify 10, 114 and 98 differentially-regulated proteins in the three sample collections, with a high degree of concordance. Functional analysis suggested that these proteins participate in biological processes related to pre-eclampsia, including angiogenesis, inflammation and cell migration. The differential abundance of 66 proteins was validated by targeted proteomics. Finally, we studied the impact of the pre-eclampsia-associated exosomes in the proteome using an in vitro cellular model.

CONCLUSIONS

We have identified and validated differential exosomal proteins in liquid biopsy of pregnant women that open new possibilities for early detection of pre-eclampsia. Additionally, the functional impact of the proteome composition of purified pre-eclamptic exosomes in target cells provides new information to better understand changes in embryo-maternal interactions and, consequently, the pathogenesis of this disease.