High-throughput Strategy Research Articles

Simplified MS2 phage-like particle production including a novel maturation protein internal fusion affinity tag

Phage-like particles (PLPs) are fabricated self-assembling nanoparticles derived from the structural elements of bacteriophages. These particles have biotechnological utility because of the ability to easily modify surface chemistry and compartmentalize nucleic acids or other materials. A consequential implementation of PLPs in diagnostics is as process controls in nucleic acid amplification tests, where control RNAs are packaged within the protein capsid and protected from degradation by RNases in the sample matrix. Key developments in PLP controls have enhanced the packing efficiency of RNAs into particles, reduced the complexity of their plasmid expression systems, and shifted purification from ultracentrifugation to affinity chromatography, producing progressively greater yields with higher purity. Expanding on prior improvements, this study establishes a revised set of plasmid vectors for Emesvirus zinderi (MS2) derived PLPs that streamline vector manipulations for rapid prototyping of new particles, provide validation of an alternative affinity tag for purification, and contributes a high-throughput low-volume spin column purification strategy. These advancements are combined with a novel internal fusion site in MS2 maturation protein A, a passive element of the MS2 capsid in prior PLP designs, that is capable of displaying polypeptides on the particles’ surface. The functionality of the chimeric maturation protein’s surface display is verified with an affinity tag fusion and subsequent purification. This advancement increases the number of available peptide display sites for the MS2 PLP platform with wide-ranging implications for future applications.

High-throughput screening strategies for plastic-depolymerizing enzymes.

A multitude of plastic-depolymerizing microorganisms and enzymes have been discovered in the plastisphere. Identifying and engineering such microbial strains and enzymes necessitate robust and high-throughput screening strategies for developing effective microbial solutions to counter the plastic accumulation problem and decouple the reliance on fossil resources. This review covers new methods and approaches for the effective high-throughput screening of depolymerizing enzymes for various plastics, such as polyethylene terephthalate (PET), polyurethane (PU), and polylactic acid (PLA). We discuss the application scope of the existing methods, as well as potential developments and integration of screening techniques to identify and enhance plastic depolymerases. The prospects for screening a wider range of plastic depolymerases with the advances in biotechnology tools such as droplet microfluidics and biosensors are highlighted.

High-Throughput Screening Strategy and Metal-Organic Framework-Based Multifunctional Controlled-Release Nanomaterial for Osteoarthritis Therapy.

Osteoarthritis (OA) is a prevalent degenerative disease that lacks effective therapy. Oxidative stress is one of the major factors contributing to OA; however, treatments targeting oxidative stress are still lacking. In the current study, we established an oxidative stress-induced cell death model in chondrocytes in vitro and screened drugs that may suppress oxidative stress-induced cell death. Ethyl gallate (EG) was identified as the most potent drug against oxidative stress-induced cell death out of more than 600 drugs in the natural product library. Application of drugs without an appropriate delivery system for OA therapy may have drawbacks such as low bioavailability, short action time, and poor efficacy. Herein, poly-His6-zinc assembly (PZA), a pH-responsive metal-organic framework (MOF) loaded with EG (EG@PZA) was designed for OA therapy. It was demonstrated that EG@PZA may have the lysosome escape property, which dramatically increases the utilization of EG. Furthermore, EG@PZA showed enhanced release capability of EG in the acidic microenvironment. In vitro and in vivo studies demonstrated that EG@PZA effectively suppresses oxidative stress-induced extracellular matrix degradation, ferroptosis, and senescence in chondrocytes and also ameliorates OA in the destabilization of the medial meniscus (DMM) mouse model in vivo. Together, the current study showed that EG@PZA may become a potential controlled-release nanomaterial for effective OA therapy.

Interpretable adenylation domain specificity prediction using protein language models.

Natural products have long been a rich source of diverse and clinically effective drug candidates. Non-ribosomal peptides (NRPs), polyketides (PKs), and NRP-PK hybrids are three classes of natural products that display a broad range of bioactivities, including antibiotic, antifungal, anticancer, and immunosuppressant activities. However, discovering these compounds through traditional bioactivity-guided techniques is costly and time-consuming, often resulting in the rediscovery of known molecules. Consequently, genome mining has emerged as a high-throughput strategy to screen hundreds of thousands of microbial genomes to identify their potential to produce novel natural products. Adenylation domains play a key role in the biosynthesis of NRPs and NRP-PKs by recruiting substrates to incrementally build the final structure. We propose MASPR, a machine learning method that leverages protein language models for accurate and interpretable predictions of A-domain substrate specificities. MASPR demonstrates superior accuracy and generalization over existing methods and is capable of predicting substrates not present in its training data, or zero-shot classification. We use MASPR to develop Seq2Hybrid, an efficient algorithm to predict the structure of hybrid NRP-PK natural products from microbial genomes. Using Seq2Hybrid, we propose putative biosynthetic gene clusters for the orphan natural products Octaminomycin A, Dityromycin, SW-163B, and JBIR-39.

Profiling Exosomal Metabolomics as a Means for Diagnosis and Researching Early-Stage Hypertensive Nephropathy.

Aims/Background Hypertension (HT) is a prevalent medical condition showing an increasing incidence rate in various populations over recent years. Long-term hypertension increases the risk of the occurrence of hypertensive nephropathy (HTN), which is also a health-threatening disorder. Given that very little is known about the pathogenesis of HTN, this study was designed to identify disease biomarkers, which enable early diagnosis of the disease, through the utilization of high-throughput untargeted metabolomics strategies. Methods The participants of this study were patients admitted to The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, who were randomly divided into three groups: Normal group (n = 11), HT group (n = 10), and HTN group (n = 12). Urine exosomes were extracted, purified, and subjected to untargeted metabolomics analysis. Differential metabolites and their significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified. The least absolute shrinkage and selection operator (LASSO) regression analysis was then employed to establish a diagnostic model for early-stage HTN. Finally, logistic regression and receiver operating characteristic (ROC) curve analysis were performed to identify biomarkers related to early HTN. Results Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed significant differences in the metabolic profiles of the three patient groups. Compared to subjects of the Normal group, the HT and HTN groups exhibited significantly upregulated and downregulated profiles of differential metabolites, respectively. LASSO regression analysis results indicated that 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid are potential biomarkers for HTN or HT. Conclusion With untargeted metabolomics analysis, we successfully identified differential metabolites in HTN. A further LASSO regression analysis revealed that four key metabolites, namely 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid, hold promise for the diagnosis of early-stage HTN.

ITASO: A Novel Photosensitive Probe for High-Throughput and Selective Submetabolomic Analysis via Flow Injection-Mass Spectrometry.

Flow injection mass spectrometry (FI-MS) is widely employed for high-throughput metabolome analysis, yet the absence of prior separation leads to significant matrix effects, thereby limiting the metabolome coverage. In this study, we introduce a novel photosensitive MS probe, iTASO-ONH2, integrated with FI-MS to establish a high-throughput strategy for submetabolome analyses. The iTASO probe features a conjugated-imino sulfonate moiety for efficient photolysis under 365 nm irradiation and a reactive group for selective metabolite labeling. The iTASO-ONH2 probe effectively and selectively labels carbonyl compounds, forming highly stable labeled products. Upon UV exposure, the labeled products rapidly release sulfonic acid-containing photolysis products, detectable with high sensitivity in ESI-negative mode and low matrix effect, offering femtomole-level detection sensitivity. The iTASO-ONH2-based FI-MS strategy was applied to fecal samples from chronic sleep-deprived and control mice, revealing 192 potential carbonyl compounds of which 37 exhibited significant alterations. Additionally, three other photosensitive probes─iTASO-NH2, iTASO-NHS, and iTASO-MAL─were synthesized to selectively label carboxyl, amino, and thiol metabolites, respectively, underscoring the versatility of the iTASO-based FI-MS strategy for submetabolomic analysis across diverse metabolite classes.

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature of UC symptoms, current pharmacological treatments fail to meet patients' expectations. This study presents a machine learning-assisted high-throughput screening strategy to expedite the discovery of efficient nanozymes for UC treatment. Therapeutic requirements, including antioxidant property, acid stability, and zeta potential, are quantified and predicted by using a machine learning model. Non-quantifiable attributes, including intestinal barrier repair efficacy and biosafety, are assessed via high-throughput screening. Feature significance analysis, sure independence screening, and sparsifying operator symbolic regression reveal the high-dimensional structure-activity relationships between material features and therapeutic needs. SrDy2O4 with high stability, low toxicity, targeting ability, and reactive oxygen species (ROS) scavenging capability is identified, which reduces ROS production, lowers cytochrome C levels in cytoplasm, and inhibits apoptosis in intestinal epithelial cells by stabilizing the mitochondrial membrane potential. Mice treated with SrDy2O4 show improvements in colon length and body weight compared with dextran sodium sulfate salt-treated model group. Transcriptomic and 16S rRNA sequencing analyses show that SrDy2O4 boosts beneficial gut bacteria, and decreases pathogenic bacteria, thereby effectively restoring gut microbiota balance. Moreover, SrDy2O4 offers the advantage of X-ray imaging without side effects.

Synthesis and biofilm inhibitory activity of cyclic dinucleotide analogues prepared with macrocyclic ribose-phosphate skeleton.

Cyclic diguanosine monophosphate (c-di-GMP) is the key second messenger regulating bacterial biofilm formation related genes. Several c-di-GMP analogues have demonstrated biofilm inhibition activity. In this study, ribose-phosphate macrocyclic skeleton containing 1'-azido groups was constructed, and CDN analogues were prepared via click chemistry. The biofilm formation inhibition activity of the analogues was evaluated, and compound 17 illustrated better activity than c-di-GMP. This high-throughput strategy could be extended to synthesize cyclic analogues for biological research and immunotherapeutic development.

MALDI-TOF mass spectrometric immunoassay of multiple tumor biomarkers for non-small cell lung cancer screening.

Cancer biomarkers have been facing some issues such as poor accuracy and low sensitivity in the early diagnosis of tumors. Utilizing biotin-labelled peptide as a mass tag (MT), this work proposes a high-throughput biosensing strategy for matrix-assisted laser desorption/ionization-time of flight mass spectrometric (MALDI-TOF-MS) immunoassay of multiple lung cancer biomarkers. Due to little required dosage, satisfied stability, high sensitivity and accuracy, this method can achieve off-site centralized signal detection after on-site sample incubation. The proposed approach has been successfully applied for the detection of carcinoembryonic antigen (CEA), carbohydrate antigen199 (CA199), carbohydrate antigen 125 (CA125) and cytokeratin-19-fragment (CY211) in serum samples from various stages of non-small cell lung cancer. Based on the analysis of multiple parameters and pathological results, significant differences in biomarkers are found in serum samples of lung cancer patients at different stages. More importantly, the analysis of multiple tumor biomarkers can improve the accuracy and sensitivity of early diagnosis. Therefore, the multiple immunoassay based on MALDI-TOF MS exhibits exceptional performance in terms of high throughput, little sample dosage, stability and sensitivity.

A high throughput strategy for synthesis of molecularly imprinted polymers microspheres via oxygen tolerant visible light-mediated RAFT polymerization for sweeteners sensing in sucrose

A high throughput strategy for synthesis of molecularly imprinted polymers microspheres via oxygen tolerant visible light-mediated RAFT polymerization for sweeteners sensing in sucrose

Identification of Olfactory Receptors Responding to Androstenone and the Key Structure Determinant in Domestic Pig.

Olfactory receptors (ORs) are members of the transmembrane G protein-coupled receptor superfamily, playing a crucial role in odor recognition, which further mediates crucial biological processes in mammals. In sows, androstenone can trigger sexual behaviors through olfaction, but the underlying mechanism remains to be explored. To efficiently and accurately screen pig olfactory receptors responding to androstenone and the key structure determinant, we adapted the high-throughput RNA-seq strategy to screen the altered genes upon androstenone treatment in the olfactory epithelium of pigs, yielding 1397 downregulated genes. Of which, 15 OR genes and 49 OR-like genes were candidate androstenone-responsive genes, and 5 ORs (OR2D2, OR8D1, OR8D2, OR10Z1 and OR7D4) were proven as responsible for androstenone-mediated olfaction in vitro. Among the five ORs, pig OR7D4 has the highest level of androstenone response. To further find the structural determinant, we performed ligand-binding cavity analysis on pig OR7D4 with androstenone, predicted seven potential structural sites and further confirmed that F178 and T203 are the key sites for recognizing androstenone. Nevertheless, the natural non-synonymous mutation M133V (rs696400829) of pig OR7D4 was proven to significantly impair the respondence to androstenone. This is the first time the ORs responding to androstenone in pigs and the key structural determinant of pig OR7D4 were identified, which highlights the significance of investigating the role of OR7D4 in pig reproduction performance in the future.

Identification of chemical inhibitors targeting long noncoding RNA through gene signature-based high throughput screening.

Scalable methods for functionally high-throughput screening of RNA-targeting small molecules are currently limited. Here, an RNA knockdown gene signature and high-throughput sequencing-based high-throughput screening (HTS2) were integrated to identify RNA-targeting compounds. We first generated a gene signature characterizing the knockdown of the long non-coding RNA LINC00973. Then, screening of 8199 compounds by HTS2 assay identified that treatments of Hesperadin and GSK1070916 significantly mimic the expression pattern of the LINC00973 knockdown gene signature. Functionally, cell phenotype changes after treatments of these two compounds also mimic the losing function of LINC00973 in multiple types of cancer cells. Mechanistically, the inhibitory action of these two compounds on LINC00973 primarily operates via the AURKB-mediated MAPK signaling pathway, resulting in reduced expression of the transcription factor c-Jun. Consequently, this leads to the suppression of LINC00973 transcription. Moreover, these two compounds significantly inhibit xenograft tumor growth in vivo. Clinically, we further found that breast tumors with high expression of LINC00973 also show relatively high expression of AURKB or JUN, and vice versa. In summary, we established a novel high-throughput screening strategy to identify small molecules capable of targeting RNA, provided two promising compounds targeting LINC00973 and further shed light on the underlying transcriptional upregulation mechanism of LINC00973 within cancer cells.

Insights into functional divergence, catalytic versatility and specificity of small molecule glycosyltransferases.

Glycosylation is one of the most fundamental biochemical processes in cells. It plays crucial roles in diversifying plant natural products for structures, bioavailability and bioactivity, and thus, renders the glycosylated compounds valuable as food additives, nutraceuticals and pharmaceuticals. Moreover, glycosylated compounds impact plant growth, development and stress response. Therefore, understanding the biochemical function of the glycosyltransferases (GTs) is crucial to the elucidation of natural product biosynthetic pathways, improving plant traits and development of processes for industrially-important compounds. UDP-dependent glycosyltransferases (UGTs) that belong to the glycosyltransferase family-1 (GT1) and catalyze the transfer of glycosyl moieties from UDP-sugars to various small molecules, are the key players in natural product glycosylation. Recent studies also found the involvement of non-canonical cellulose synthase-like (CesAs) and glycosyl hydrolase (GH) family enzymes in the glycosylation of plant specialized metabolites. Decades of research on GTs provided critical insights into catalytic mechanism, substrate/product specificity and catalytic promiscuity, but biochemical function and physiological roles of GTs in majority of the natural product biosynthetic pathways remain to be understood. It is also important to redefine high-throughput strategies of GT mining to uncover novel biochemical function, considering that GTs are the large superfamily members in plants and other organisms. This review underscores the roles of GTs in small molecule glycosylation, plant development and stress responses, highlighting the catalytic versatility and substrate/product specificity of GTs in shaping plant metabolic diversity, and discusses the emerging strategies for mining of uncharacterized GTs to unravel biochemical and physiological functions and to elucidate natural product biosynthetic pathways.

Comprehensive characterization of anthraquinones in Damnacanthus indicus using mass spectrometry molecular networking and metabolomics-based herb discrimination.

Damnacanthus indicus is a widely used folk medicine in China, renowned for its various bioactivities. The key active components, anthraquinones, have not been comprehensively profiled due to their complex chemical nature. Establishing a high-throughput strategy to systematically characterize these anthraquinones is essential. Additionally, the cultivation of D. indicus across various provinces results in significant quality differences in the harvested herbs. Thus, developing an effective strategy to distinguish herbs from different regions and identify characteristic chemical markers for quality evaluation and control is crucial. In this study, a strategy based on ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to systematically characterize the chemical composition of D. indicus. Mass spectrometry molecular networking was utilized to rapidly recognize and identify anthraquinones. Principal component analysis (PCA) was applied to cluster the herbs from different habitats, while partial least square discriminant analysis (PLS-DA) was used to screen for chemical markers distinguishing herb origins. The result showed that a total of 112 anthraquinones and 66 non-anthraquinone compounds were identified in D. indicus. The biosynthetic pathways of anthraquinones in this herb were proposed. PCA grouped 15 batches of herbs from different origins into three clusters, corresponding to the climate types of their habitats. PLS-DA identified 27 significant chemical markers that could robustly distinguish the geographical origins of the herbs. This study provides a valuable reference for the quality evaluation and control of D. indicus and offers a scientific basis for the pharmacological research and rational utilization of these medicinal resources.

(Invited) the Use of Metal-Organic Frameworks in Adsorption Cooling/Heating: From the Perspective of Computational Screening

With the severe climate change, there is a growing energy demand for cooling and heating, which takes up 20% of the global energy consumption. Adsorption cooling/heating driven by low-grade heat sources including waste heat and solar energy has been a promising alternative owing to its reduced electricity consumption, low carbon emission and easy operation. However, the coefficient of performance for cooling/heating (COPC/ COPH) and specific cooling/heating effects(SCE/SHE) is still unsatisfactory due to the low uptake and high regeneration cost of conventional adsorbents such as activated carbon and zeolites. Metal-organic frameworks (MOFs) have been recognized as the potential adsorbents for adsorption cooling/heating due to their outstanding adsorption capacity resulted from ultrahigh surface area. Nevertheless, given the huge number of MOFs, it is a challenging task to quickly identify the potential candidates. Besides, the relationship between structure properties, adsorption behaviors and the cooling/heating performance is still elusive. In this study, high-throughput computational screening strategy based on integrated grand canonical Monte Carlo (GCMC) simulation and numerical simulation for efficiently evaluating the cooling/heating performance of a large number of MOFs was developed for both alcohol and water working fluids. The extracted structure-performance relationship highlighted the importance of both structure properties of MOFs and their adsorption characteristics. Machine learning algorithm was also carried out to accelerate the cooling/heating performance prediction of MOFs. To be consistent with experimental measurement, the adsorption dynamics was also taken into consideration. Multiscale modeling combining the molecular simulation and the mathematical simulation was proposed to obtain the COP and specific cooling power (SCP) for a vast number of MOF-based working pairs with high efficiency. This study provides the theoretical approaches to quickly identify high-efficient MOF adsorbents for high-performing adsorption cooling/heating.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for rapid analysis of eight Gelsemium elegans Benth alkaloids in human plasma and urine

Gelsemium elegans Benth alkaloids are the main components of G. elegans and can cause acute toxicosis or even death. Although several studies have reported methods for detecting G. elegans alkaloids, a high-throughput and environmental-friendly strategy for detection of multiple G. elegans alkaloids has not been realized. In this work, a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry method was developed for rapid detection of G. elegans alkaloids in human plasma and urine for diagnosis of poisoning. Multiple matrices and crystal spotting methods were evaluated to obtain stable and high peak intensities without “sweet spot”. We verified the methodology and obtained excellent results. The matrix effects with different dilutions were compared and good recoveries and a low relative standard deviation were obtained with a 40-fold dilution. This method could shorten the analysis time and greatly reduce the consumption of chemical solvents. Furthermore, it could be applied to quantitative assessment of G. elegans alkaloid poisoning incidents.

Comprehensive Understanding of Laboratory Evolution for Food Enzymes: From Design to Screening Innovations.

In the field of food processing, enzymes play a pivotal role in improving product quality and flavor, and extending shelf life. However, the exposure of traditional food enzymes to high temperatures during processing often leads to a decrease in activity or even inactivation, limiting the effectiveness of their application under high-temperature conditions. Therefore, the modification of thermostability and activity of enzymes to adapt to extreme conditions through protein engineering has become a key way to improve the efficiency and economic benefits of industrial production. Directed evolution and semirational design strategies in the laboratory have proven to be broadly applicable frameworks for biochemical researchers in the food field, including those who are beginners. In this review, we systematically summarize semirational design strategies and high-throughput screening strategies, and introduce some intuitive computer simulation software to improve the thermostability and enzyme activity of food enzymes. The application of these strategies and techniques provides a comprehensive guide for the optimization of food enzymes. In addition, the latest hot topics of genetically engineered food enzymes in the field of application are discussed.

Designing a whole-cell biosensor applicable for S-adenosyl-l-methionine-dependent methyltransferases

This study was undertaken to develop a high-throughput screening strategy using a whole-cell biosensor to enhance methyl-group transfer, a rate-limiting step influenced by intracellular methyl donor availability and methyltransferase efficiency. An l-homocysteine biosensor was designed based on regulatory protein MetR from Escherichia coli, which rapidly reported intracellular l-homocysteine accumulation resulted from S-adenosyl-l-homocysteine (SAH) formation after methyl-group transfer. Using S-adenosyl-l-methionine (SAM) as a methyl donor, this biosensor was applied to caffeic acid 3-O-methyltransferase derived from Arabidopsis thaliana (AtComT). After several rounds of directed evolution, the modified enzyme achieved a 13.8-fold improvement when converting caffeic acid to ferulic acid. The best mutant exhibited a 5.4-fold improvement in catalytic efficiency. Characterization of beneficial mutants showed that improved O-methyltransferase dimerization greatly contributed to enzyme activity. This finding was verified when we switched and compared the N-termini involved in dimerization across different sources. Finally, with tyrosine as a substrate, the evolved AtComT mutant greatly improved ferulic acid biosynthesis, yielding 3448 mg L−1 with a conversion rate of 88.8%. These results have important implications for high-efficiency O-methyltransferase design, which will greatly benefit the biosynthesis of a wide range of natural products. In addition, the l-homocysteine biosensor has the potential for widespread applications in evaluating the efficiency of SAM-based methyl transfer.

Simultaneous and High-Throughput Analytical Strategy of 30 Fluorinated Emerging Pollutants Using UHPLC-MS/MS in the Shrimp Aquaculture System.

This study established novel and high-throughput strategies for the simultaneous analysis of 30 fluorinated emerging pollutants in different matrices from the shrimp aquaculture system in eastern China using UHPLC-MS/MS. The parameters of SPE for analysis of water samples and of QuEChERS methods for sediment and shrimp samples were optimized to allow the simultaneous detection and quantitation of 17 per- and polyfluoroalkyl substances (PFASs) and 13 fluoroquinolones (FQs). Under the optimal conditions, the limits of detection of 30 pollutants for water, sediment, and shrimp samples were 0.01-0.30 ng/L, 0.01-0.22 μg/kg, and 0.01-0.23 μg/kg, respectively, while the limits of quantification were 0.04-1.00 ng/L, 0.03-0.73 μg/kg, and 0.03-0.76 μg/kg, with satisfactory recoveries and intra-day precision. The developed methods were successfully applied to the analysis of multiple samples collected from aquaculture ponds in eastern China. PFASs were detected in all samples with concentration ranges of 0.18-0.77 μg/L in water, 0.13-1.41 μg/kg (dry weight) in sediment, and 0.09-0.96 μg/kg (wet weight) in shrimp, respectively. Only two FQs, ciprofloxacin and enrofloxacin, were found in the sediment and shrimp. In general, this study provides valuable insights into the prevalence of fluorinated emerging contaminants, assisting in the monitoring and control of emerging contaminants in aquatic foods.

Mapping, Distribution, Function, and High-Throughput Methodological Strategies for Soil Microbial Communities in the Agroecosystem in the Last Decades

The intricate interplay between SMCs and agroecosystems has garnered substantial attention in recent decades due to its profound implications for agricultural productivity, ecosystem sustainability, and environmental health. Understanding the distribution of SMCs is complemented by investigations into their functional roles within agroecosystems. Soil microbes play pivotal roles in nutrient cycling, organic matter decomposition, disease suppression, and plant‒microbe interactions, profoundly influencing soil fertility, crop productivity, and ecosystem resilience. Elucidating the functional diversity and metabolic potential of SMCs is crucial for designing sustainable agricultural practices that harness the beneficial functions of soil microbes while minimizing detrimental impacts on ecosystem services. Various molecular techniques, such as next-generation sequencing and high-throughput sequencing, have facilitated the elucidation of microbial community structures and dynamics at different spatial scales. These efforts have revealed the influence of factors such as soil type, land management practices, climate, and land use change on microbial community composition and diversity. Advances in high-throughput methodological strategies have revolutionized our ability to characterize SMCs comprehensively and efficiently. These include amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics, which provide insights into microbial taxonomic composition, functional potential, gene expression, and protein profiles. The integration of multiomics approaches allows for a more holistic understanding of the complex interactions within SMCs and their responses to environmental perturbations. In conclusion, this review highlights the significant progress made in mapping, understanding the distribution, elucidating the functions, and employing high-throughput methodological strategies to study SMCs in agroecosystems.