Omics Methods Research Articles

Omics technology draws a comprehensive heavy metal resistance strategy in bacteria.

The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the most harmful and toxic threats to all environmental components (air, soil, water, animals, and plants until reaching to human). Therefore, scientists try to find a promising and eco-friendly technique to solve this problem i.e., bacterial bioremediation. Various heavy metal resistance mechanisms were reported. Omics technologies can significantly improve our understanding of heavy metal resistant bacteria and their communities. They are a potent tool for investigating the adaptation processes of microbes in severe conditions. These omics methods provide unique benefits for investigating metabolic alterations, microbial diversity, and mechanisms of resistance of individual strains or communities to harsh conditions. Starting with genome sequencing which provides us with complete and comprehensive insight into the resistance mechanism of heavy metal resistant bacteria. Moreover, genome sequencing facilitates the opportunities to identify specific metal resistance genes, operons, and regulatory elements in the genomes of individual bacteria, understand the genetic mechanisms and variations responsible for heavy metal resistance within and between bacterial species in addition to the transcriptome, proteome that obtain the real expressed genes. Moreover, at the community level, metagenome, meta transcriptome and meta proteome participate in understanding the microbial interactive network potentially novel metabolic pathways, enzymes and gene species can all be found using these methods. This review presents the state of the art and anticipated developments in the use of omics technologies in the investigation of microbes used for heavy metal bioremediation.

Joint transcriptomic and metabolomic analysis provides new insights into drought resistance in watermelon (Citrullus lanatus)

IntroductionWatermelon is an annual vine of the family Cucurbitaceae. Watermelon plants produce a fruit that people love and have important nutritional and economic value. With global warming and deterioration of the ecological environment, abiotic stresses, including drought, have become important factors that impact the yield and quality of watermelon plants. Previous research on watermelon drought resistance has included analyzing homologous genes based on known drought-responsive genes and pathways in other species.MethodsHowever, identifying key pathways and genes involved in watermelon drought resistance through high-throughput omics methods is particularly important. In this study, RNA-seq and metabolomic analysis were performed on watermelon plants at five time points (0 h, 1 h, 6 h, 12 h and 24 h) before and after drought stress.ResultsTranscriptomic analysis revealed 7829 differentially expressed genes (DEGs) at the five time points. The DEGs were grouped into five clusters using the k-means clustering algorithm. The functional category for each cluster was annotated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; different clusters were associated with different time points after stress. A total of 949 metabolites were divided into 10 categories, with lipids and lipid-like molecules accounting for the most metabolites. Differential expression analysis revealed 22 differentially regulated metabolites (DRMs) among the five time points. Through joint analysis of RNA-seq and metabolome data, the 6-h period was identified as the critical period for watermelon drought resistance, and the starch and sucrose metabolism, plant hormone signal transduction and photosynthesis pathways were identified as important regulatory pathways involved in watermelon drought resistance. In addition, 15 candidate genes associated with watermelon drought resistance were identified through joint RNA-seq and metabolome analysis combined with weighted correlation network analysis (WGCNA). Four of these genes encode transcription factors, including bHLH (Cla97C03G068160), MYB (Cla97C01G002440), HSP (Cla97C02G033390) and GRF (Cla97C02G042620), one key gene in the ABA pathway, SnRK2-4 (Cla97C10G186750), and the GP-2 gene (Cla97C05G105810), which is involved in the starch and sucrose metabolism pathway.DiscussionIn summary, our study provides a theoretical basis for elucidating the molecular mechanisms underlying drought resistance in watermelon plants and provides new genetic resources for the study of drought resistance in this crop.

Abstract 7416: GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data

Abstract Identification of genotype-phenotype relationships is of utmost importance and a core effort in biology. Recent developments in efficient and precise gene targeting approaches coupled to omics methods have significantly improved deciphering of molecular interactions and relationships. However, many single gene perturbations can affect the expression of hundreds of other genes and analysis of the resulting omics-derived gene lists currently remains a significant challenge. Here we present Perturb-Seq based Gene Set Analyzer (GeneSetR), a user-friendly web-server that can analyze user-defined gene lists based on the data from a recently published genome-wide Perturb-Seq study, which targeted 9,866 genes with 11,258 sgRNAs in the K562 cell line. Through this tool, users can cluster gene lists following dimensionality reduction by various algorithms, undertake network analysis from RNA sequencing data, identify key nodes among the submitted genes, perform gene signature analyses, and generate heatmaps based on perturbation or gene expression data. GeneSetR enables researchers to readily identify gene clusters associated with specific phenotypes or biological processes, providing insights into the potential functional roles of these clusters and the role of single genes in them. With robust analysis capabilities, GeneSetR is a powerful resource to facilitate the exploration of genotype-phenotype relationships. Citation Format: Omer Faruk Kuzu, Fahri Saatcioglu. GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7416.

Decoupling between the genetic potential and the metabolic regulation and expression in microbial organic matter cleavage across microbiomes.

Omics tools are used to explore adaptive mechanism of microbes in diverse systems, but the advantages and limitations of different omics tools remain skeptical. Here, we reported distinct profiles in microbial secretory enzyme composition revealed by different omics methods. In general, the predicted function from metagenomic analysis decoupled from the expression of corresponding transcripts/proteins. Linking omics results to taxonomic origin, functional capability, substrate specificity, secretion preference, and enzymatic activity measurement suggested the substrate's source, concentration and stoichiometry impose strong filtering on the expression of extracellular enzymes, which may overwrite the genetic potentials. Our results present an integrated perspective on the need for multi-dimensional characterization of microbial adaptation in a changing environment.

Epigenetic control of microglial immune responses.

Microglia, the major population of brain-resident macrophages, are now recognized as a heterogeneous population comprising several cell subtypes with different (so far mostly supposed) functions in health and disease. A number of studies have performed molecular characterization of these different microglial activation states over the last years making use of "omics" technologies, that is transcriptomics, proteomics and, less frequently, epigenomics profiling. These approaches offer the possibility to identify disease mechanisms, discover novel diagnostic biomarkers, and develop new therapeutic strategies. Here, we focus on epigenetic profiling as a means to understand microglial immune responses beyond what other omics methods can offer, that is, revealing past and present molecular responses, gene regulatory networks and potential future response trajectories, and defining cell subtype-specific disease relevance through mapping non-coding genetic variants. We review the current knowledge in the field regarding epigenetic regulation of microglial identity and function, provide an exemplary analysis that demonstrates the advantages of performing joint transcriptomic and epigenomic profiling of single microglial cells and discuss how comprehensive epigenetic analyses may enhance our understanding of microglial pathophysiology.

Advances in single-cell omics and multiomics for high-resolution molecular profiling.

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy.

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

Special Mini-Issue: Quantitative methods to decipher cellular heterogeneity – from single-cell to spatial omic methods

Special Mini-Issue: Quantitative methods to decipher cellular heterogeneity – from single-cell to spatial omic methods

An error correction strategy for image reconstruction by DNA sequencing microscopy.

By pairing adjacent molecules in situ and then mapping these pairs, DNA microscopy could substantially reduce the workload in spatial omics methods by directly inferring geometry from sequencing data alone. However, experimental artifacts can lead to errors in the adjacency data, which distort the spatial reconstruction. Here we describe a method to correct two such errors: spurious crosslinks formed between any two nodes, and fused nodes that are formed out of multiple molecules. We build on the principle that spatially close molecules should be connected and show that these errors violate this principle, allowing for their detection and correction. Our method corrects errors in simulated data, even in the presence of up to 20% errors, and proves to be more efficient at removing errors from experimental data than a read count filter. Integrating this method in DNA microscopy will substantially improve the accuracy of spatial reconstructions with lower data loss.

Omics methods predict the prognosis and treatment efficacy of chronic obstructive pulmonary disease.

Omics methods predict the prognosis and treatment efficacy of chronic obstructive pulmonary disease.

MSIsensor-RNA: Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

Abstract Microsatellite instability (MSI) is an indispensable biomarker in cancer immunotherapy. Currently, MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI detection. However, the MSI detection method on expression data, especially single-cell expression data, is still lacking, limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell level. Herein, we developed MSIsensor-RNA, an accurate, robust, adaptable, and standalone software to detect MSI status based on expression values of MSI-associated genes. We demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing (RNA-seq), microarray, and single-cell RNA-seq. MSIsensor-RNA is a versatile, efficient, and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and applications. MSIsensor-RNA is available at https://github.com/xjtu-omics/msisensor-rna.

Genome-Wide Identification and Characterization of Effector Candidates with Conserved Motif in Falciphora oryzae.

Microbes employ effectors to disrupt immune responses and promote host colonization. Conserved motifs including RXLR, LFLAK-HVLVxxP (CRN), Y/F/WxC, CFEM, LysM, Chitin-bind, DPBB_1 (PNPi), and Cutinase have been discovered to play crucial roles in the functioning of effectors in filamentous fungi. Nevertheless, little is known about effectors with conserved motifs in endophytes. This research aims to discover the effector genes with conserved motifs in the genome of rice endophyte Falciphora oryzae. SignalP identified a total of 622 secreted proteins, out of which 227 were predicted as effector candidates by EffectorP. By utilizing HMM features, we discovered a total of 169 effector candidates with conserved motifs and three novel motifs. Effector candidates containing LysM, CFEM, DPBB_1, Cutinase, and Chitin_bind domains were conserved across species. In the transient expression assay, it was observed that one CFEM and one LysM activated cell death in tobacco leaves. Moreover, two CFEM and one Chitin_bind inhibited cell death induced by Bax protein. At various points during the infection, the genes' expression levels were increased. These results will help to identify functional effector proteins involving omics methods using new bioinformatics tools, thus providing a basis for the study of symbiosis mechanisms.

A metabolomics perspective on root‐derived plant immunity and phytohormone interaction

AbstractA plant's root system is essential to its survival as it supports anchorage and absorbs water and minerals from the ground. Several microorganisms interact with roots in the rhizosphere, and they can be neutral, positive, or negative. Identifying beneficial and pathogenic microorganisms and limiting or promoting their colonization is essential for roots to survive. Signaling molecules and metabolites play an important regulatory role in determining root‐microbe associations. Phytohormones modulate plant‐microbe interactions by metabolic reprogramming and transmitting long‐range signals, resulting in local and systemic resistance. Mass spectrometry‐based metabolomics provides a powerful means to study root‐microbe interactions. It provides a qualitative and quantitative approach for determining the mechanisms of root‐microbe interactions and also helps to understand pathogen‐induced metabolic reprogramming. By using metabolomics, one can identify biomarker metabolites that indicate disease resistance or sensitivity in the roots. Compared to other omics methods, metabolomics is still underutilized in root‐pathogen interaction studies. It is, therefore, vital that metabolomics is popularized to gain a better understanding of disease resistance and susceptibility. Through untargeted metabolomics, we can gain insight into plant‐microbial interactions across a variety of tissues, which helps us develop more effective crop and plant protection measures. In this review, we describe the current status and progress in metabolomics applications for studying root‐microbe interactions with an emphasis on root‐derived phytohormones and specialized metabolites. In addition, we also proposed the use of metabolomics to identify novel biomarkers during root‐microbe interactions, to distinguish resistant genotypes from susceptible ones, or to predict nondestructively early disease progression.

Temperature‐smart plants: A new horizon with omics‐driven plant breeding

AbstractThe adverse effects of mounting environmental challenges, including extreme temperatures, threaten the global food supply due to their impact on plant growth and productivity. Temperature extremes disrupt plant genetics, leading to significant growth issues and eventually damaging phenotypes. Plants have developed complex signaling networks to respond and tolerate temperature stimuli, including genetic, physiological, biochemical, and molecular adaptations. In recent decades, omics tools and other molecular strategies have rapidly advanced, offering crucial insights and a wealth of information about how plants respond and adapt to stress. This review explores the potential of an integrated omics‐driven approach to understanding how plants adapt and tolerate extreme temperatures. By leveraging cutting‐edge omics methods, including genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, and ionomics, alongside the power of machine learning and speed breeding data, we can revolutionize plant breeding practices. These advanced techniques offer a promising pathway to developing climate‐proof plant varieties that can withstand temperature fluctuations, addressing the increasing global demand for high‐quality food in the face of a changing climate.

Glioblastoma epigenomics discloses a complex biology and potential therapeutic targets.

<p>Glioblastoma (GBM), a highly aggressive form of brain tumors, has been extensively studied using OMICS methods, and the most characteristic molecular determinants have been incorporated into the histopathological diagnosis. Research data, nevertheless, only partially have been adopted in clinical practice. Here we aimed to present results of our epige&shy;no&shy;mic GBM profiling to better understand early and late determinants of these tumors, and to share main elements of our findings with practicing professionals.</p>. <p>GBM specimens were surgically obtained after first diagnosis (GBM1) and at recurrence (GBM2). DNA was extracted from 24 sequential pairs of formalin-fixed, paraffin-embedded tumor tissues. The Reduced Representation Bisulfite Sequencing kit was used for library preparation. Pooled libraries were sequenced on an Illumina NextSeq 550 instrument. Methylation controls (MC) were obtained from a publicly available database. Bioinformatic analyses were performed to identify differentially methylated pathways and their elements in cohorts of MC, GBM1 and GBM2.</p>. <p>Several differentially methylated pathways involved in basic intracellular and brain tissue developmental processes were identified in the GBM1 vs. MC and GBM2 vs. MC comparisons. Among differentially me&shy;thylated pathways, those involved in immune regulation, neurotransmitter (particularly dopaminergic, noradrenergic and glutaminergic) responses and regulation of stem cell differentiation and proliferation stood out in the GBM2 vs. GBM1 comparisons.</p>. <p>Our study revealed biological complexity of early and late gliomagenesis encompassing mechanisms from basic intracellular through distorted neurodevelopmental processes to more specific immune and highjacked neurotransmitter pathways in the tumor microenvironment. These findings may offer considerations for therapeutic approaches.</p>.

Spatial Transcriptomics Analysis of Vulnerable Cell Populations in Middle Temporal Gyrus

AbstractBackgroundAlzheimer’s disease (AD) is characterized by cognitive decline due to histopathological changes of neurodegeneration in a stereotypical neuroanatomical progression. Applying single‐nucleus ‘omics methods has extended our understanding of how AD impacts the brain and allowed the Seattle AD Brain Cell Atlas Center (SEA‐AD) to provide a detailed cellular‐molecular view of middle temporal gyrus (MTG) in AD. Previously, we identified transcriptomically‐defined cell types that are specifically affected by AD in MTG, however, cell types identified in ‘omics data lack spatial context. Identifying the location of these cell types in situ is necessary to pinpoint the location of affected cells and connect cellular‐molecular views of disease progression to histopathology. To accomplish this, spatial transcriptomics methods measure individual mRNA molecule locations with subcellular resolution and provide transcriptomic profiles of individual cells in situ. MethodUsing a panel of genes designed to identify transcriptomic cell types, we performed spatial transcriptomics experiments using the Vizgen MERSCOPE platform across MTG tissue from 25 donors spanning AD pathology, yielding transcriptional profiles of over 1M cells across the cortical depth and white matter of 68 sections. These cells were assigned to transcriptomically‐defined cell types by integrating the spatial transcriptomics data with our existing single nucleus dataset. From the resulting maps of cell type locations, we measured cortical layer thickness, cell proportions, densities and cell type‐dependent pairwise distance distributions for each donor.ResultWe have successfully applied the MERSCOPE platform to generate cellular‐resolution spatial transcriptomic data in MTG across a wide range of AD disease progression. We applied novel deep generative models to assign cell type identity. These measurements revealed cell type‐specific spatial distributions and changes in cell type proportions due to disease, validating observations from single‐nucleus studies. Enhanced loss of Layer 2/3 excitatory neuron types and somatostatin‐expressing inhibitory neurons is calculating cell density within layers. Additional analyses uniquely available to spatial transcriptomics studies will be useful for quantitative description of cytoarchitectural changes in AD.ConclusionSpatial transcriptomics experiments across disease progression in MTG confirm cell type abundance changes measured in single‐nucleus RNASeq. The cellular spatial distributions provide detailed knowledge of the location of vulnerable cells within cortex.

Evaluation of antibody-based single cell type imaging techniques coupled to multiplexed imaging of N-glycans and collagen peptides by matrix-assisted laser desorption/ionization mass spectrometry imaging

The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with single cell spatial omics methods allows for a comprehensive investigation of single cell spatial information and matrisomal N-glycan and extracellular matrix protein imaging. Here, the performance of the antibody-directed single cell workflows coupled with MALDI-MSI are evaluated. Miralys™ photocleavable mass-tagged antibody probes (MALDI-IHC, AmberGen, Inc.), GeoMx DSP® (NanoString, Inc.), and Imaging Mass Cytometry (IMC, Standard BioTools Inc.) were used in series with MALDI-MSI of N-glycans and extracellular matrix peptides on formalin-fixed paraffin-embedded tissues. Single cell omics protocols were performed before and after MALDI-MSI. The data suggests that for each modality combination, there is an optimal order for performing both techniques on the same tissue section. An overall conclusion is that MALDI-MSI studies may be completed on the same tissue section as used for antibody-directed single cell modalities. This work increases access to combined cellular and extracellular information within the tissue microenvironment to enhance research on the pathological origins of disease.Graphical

A primer on correlation-based dimension reduction methods for multi-omics analysis.

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Predicting Blomia tropicalis allergens using a multiomics approach.

The domestic mite Blomia tropicalis is a major source of allergens in tropical and subtropical regions. Despite its great medical importance, the allergome of this mite has not been sufficiently studied. Only 14 allergen groups have been identified in B. tropicalis thus far, even though early radioimmunoelectrophoresis techniques (27 uncharacterized allergen complexes) and comparative data based on 40 allergen groups officially recognized by the World Health Organization (WHO)/IUIS in domestic astigmatid mites suggest the presence of a large set of additional allergens. Here, we employ a multiomics approach to assess the allergome of B. tropicalis using genomic and transcriptomic sequence data and perform highly sensitive protein abundance quantification. Among the 14 known allergen groups, we confirmed 13 (one WHO/IUIS allergen, Blo t 19, was not found) and identified 16 potentially novel allergens based on sequence similarity. These data indicate that B. tropicalis shares 27 known/deduced allergen groups with pyroglyphid house dust mites (genus Dermatophagoides). Among these groups, five allergen-encoding genes are highly expressed at the transcript level: Blo t 1, Blo t 5, Blo t 21 (known), Blo t 15, and Blo t 18 (predicted). However, at the protein level, a different set of most abundant allergens was found: Blo t 2, 10, 11, 20 and 21 (mite bodies) or Blo t 3, 4, 6 and predicted Blo t 13, 14 and 36 (mite feces). We report the use of an integrated omics method to identify and predict an array of mite allergens and advanced, label-free proteomics to determine allergen protein abundance. Our research identifies a large set of novel putative allergens and shows that the expression levels of allergen-encoding genes may not be strictly correlated with the actual allergenic protein abundance in mite bodies.

Emerging trends in computational approaches for drug discovery in molecular biology

Purpose of Research: This review paper delves into the transformative impact of computational approaches on drug discovery within molecular biology. It explores how these methods offer efficient and cost-effective solutions to identify and optimize potential drug candidates, addressing the shortcomings of traditional drug discovery methods. Scope of the Experiments: Algorithms, machine learning, artificial intelligence, and quantum mechanics are studied. These approaches analyse large datasets, anticipate drug- target interactions, and improve drug design. Supervised and unsupervised learning techniques enable chemical space exploration, target identification, and compound classification. Bioinformatics and data mining enable target identification, drug discovery, and personalised treatment by analysing large biological databases. Quantum mechanics- based techniques reveal molecular structures, interactions, and reactions, improving drug design and optimisation. Results and Findings: The review demonstrates that computational approaches have the potential to expedite drug discovery by leveraging machine learning, artificial intelligence, quantum mechanics, Big Data, and omics methods. These techniques enable accurate prediction of drug-target interactions and efficient exploration of chemical and biological spaces. The integration of diverse datasets enhances target identification and personalized medicine, while quantum mechanics-based insights improve drug design. Conclusions: Despite their benefits, computational approaches face challenges such as model accuracy, efficiency, and validation. Nonetheless, this review underscores the significance of these approaches and their applications in drug discovery. By addressing challenges and embracing emerging technologies, the field can propel advancements in computational drug discovery. This progress will not only benefit patients but also advance the overall landscape of healthcare.