Capture of endogenous lipids in peptidiscs and effect on protein stability and activity.

Compared to protein-protein and protein-nucleic acid interactions, our knowledge of protein-lipid interactions remains limited. This is primarily due to the inherent insolubility of membrane proteins (MPs) in aqueous solution. The traditional use of detergents to overcome the solubility barrier destabilizes MPs and strips away certain lipids that are increasingly recognized as crucial for protein function. Recently, membrane mimetics have been developed to circumvent the limitations. In this study, using the peptidisc, we find that MPs in different lipid states can be isolated based on protein purification and reconstitution methods, leading to observable effects on MP activity and stability. Peptidisc also enables re-incorporating specific lipids to fine-tune the protein microenvironment and assess the impact on downstream protein associations. This study offers a first look at the illusive protein-lipid interaction specificity, laying the path for a systematic evaluation of lipid identity and contributions to membrane protein function.

The underappreciated diversity of bile acid modifications.

The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.

Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism.

Glioblastoma is the most lethal primary brain tumor with glioblastoma stem cells (GSCs) atop a cellular hierarchy. GSCs often reside in a perivascular niche, where they receive maintenance cues from endothelial cells, but the role of heterogeneous endothelial cell populations remains unresolved. Here, we show that lymphatic endothelial-like cells (LECs), while previously unrecognized in brain parenchyma, are present in glioblastomas and promote growth of CCR7-positive GSCs through CCL21 secretion. Disruption of CCL21-CCR7 paracrine communication between LECs and GSCs inhibited GSC proliferation and growth. LEC-derived CCL21 induced KAT5-mediated acetylation of HMGCS1 on K273 in GSCs to enhance HMGCS1 protein stability. HMGCS1 promoted cholesterol synthesis in GSCs, favorable for tumor growth. Expression of the CCL21-CCR7 axis correlated with KAT5 expression and HMGCS1K273 acetylation in glioblastoma specimens, informing patient outcome. Collectively, glioblastomas contain previously unrecognized LECs that promote the molecular crosstalk between endothelial and tumor cells, offering potentially alternative therapeutic strategies.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis.

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Cholesterol-Mediated Coenzyme A Depletion in Catabolic Mutants of Mycobacteria Leads to Toxicity.

Cholesterol is a critical growth substrate for Mycobacterium tuberculosis (Mtb) during infection, and the cholesterol catabolic pathway has been targeted for the development of new antimycobacterial agents. A key metabolite in cholesterol catabolism is 3aα-H-4α(3'-propanoate)-7aß-methylhexahydro-1,5-indanedione (HIP). Many of the HIP metabolites are acyl-coenzyme A (CoA) thioesters, whose accumulation in deletion mutants can cause cholesterol-mediated toxicity. We used LC-MS/MS analysis to demonstrate that deletion of genes involved in HIP catabolism leads to acyl-CoA accumulation with concomitant depletion of free CoASH, leading to dysregulation of central metabolic pathways. CoASH and acyl-CoAs inhibited PanK, the enzyme that catalyzes the first step in the transformation of pantothenate to CoASH. Inhibition was competitive with respect to ATP with Kic values ranging from 9 µM for CoASH to 57 µM for small acyl-CoAs and 180 ± 30 µM for cholesterol-derived acyl-CoA. These findings link two critical metabolic pathways and suggest that therapeutics targeting cholesterol catabolic enzymes could both prevent the utilization of an important growth substrate and simultaneously sequester CoA from essential cellular processes, leading to bacterial toxicity.

Meeting Report on the 3rd Chinese American Society for Mass Spectrometry Conference-Advancing Biological and Pharmaceutical Mass Spectrometry.

Following the highly successful Chinese American Society for Mass Spectrometry (CASMS) conferences in the previous 2 years, the 3rd CASMS Conference was held virtually on August 28-31, 2023, using the Gather.Town platform to bring together scientists in the MS field. The conference offered a 4-day agenda with a scientific program consisting of two plenary lectures, and 14 parallel symposia in which a total of 70 speakers presented technological innovations and their applications in proteomics and biological MS and metabo-lipidomics and pharmaceutical MS. In addition, 16 invited speakers/panelists presented at two research-focused and three career development workshops. Moreover, 86 posters, 12 lightning talks, 3 sponsored workshops, and 11 exhibitions were presented, from which 9 poster awards and 2 lightning talk awards were selected. Furthermore, the conference featured four young investigator awardees to highlight early-career achievements in MS from our society. The conference provided a unique scientific platform for young scientists (i.e. graduate students, postdocs, and junior faculty/investigators) to present their research, meet with prominent scientists, learn about career development, and job opportunities (http://casms.org).

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Elucidation of Physiological, Transcriptomic and Metabolomic Salinity Response Mechanisms in Medicago sativa.

Alfalfa (Medicago sativa L.) is a widely grown perennial leguminous forage crop with a number of positive attributes. However, despite its moderate ability to tolerate saline soils, which are increasing in prevalence worldwide, it suffers considerable yield declines under these growth conditions. While a general framework of the cascade of events involved in plant salinity response has been unraveled in recent years, many gaps remain in our understanding of the precise molecular mechanisms involved in this process, particularly in non-model yet economically important species such as alfalfa. Therefore, as a means of further elucidating salinity response mechanisms in this species, we carried out in-depth physiological assessments of M. sativa cv. Beaver, as well as transcriptomic and untargeted metabolomic evaluations of leaf tissues, following extended exposure to salinity (grown for 3-4 weeks under saline treatment) and control conditions. In addition to the substantial growth and photosynthetic reductions observed under salinity treatment, we identified 1233 significant differentially expressed genes between growth conditions, as well as 60 annotated differentially accumulated metabolites. Taken together, our results suggest that changes to cell membranes and walls, cuticular and/or epicuticular waxes, osmoprotectant levels, antioxidant-related metabolic pathways, and the expression of genes encoding ion transporters, protective proteins, and transcription factors are likely involved in alfalfa's salinity response process. Although some of these alterations may contribute to alfalfa's modest salinity resilience, it is feasible that several may be disadvantageous in this context and could therefore provide valuable targets for the further improvement of tolerance to this stress in the future.

Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression.

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics.

The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.

Meeting Report on the 2nd Chinese American Society for Mass Spectrometry Conference: Advancing Biological and Pharmaceutical Mass Spectrometry.

The 2nd CASMS conference was held virtually through Gather. Town platform from October 17 to 21, 2022, with a total of 363 registrants including an outstanding and diverse group of scientists at the forefront of their research fields from both academia and industry worldwide, especially in the United States and China. The conference offered a 5-day agenda with an exciting scientific program consisting of two plenary lectures, 14 parallel symposia, and 4 special sessions in which a total of 97 invited speakers presented technological innovations and their applications in proteomics & biological mass spectrometry and metabo-lipidomics & pharmaceutical mass spectrometry. In addition, 18 invited speakers/panelists presented at 3 research-focused and 2 career development workshops. Moreover, 144 posters, 54 lightning talks, 5 sponsored workshops, and 14 exhibitions were presented, from which 20 posters and 8 lightning talks received presentation awards. Furthermore, the conference featured 1 MCP lectureship and 5 young investigator awardees for the first time to highlight outstanding mid-career and early-career rising stars in mass spectrometry from our society. The conference provided a unique scientific platform for young scientists (i.e., graduate students, postdocs and junior faculty/investigators) to present their research, meet with prominent scientists, and learn about career development and job opportunities (http://casms.org).

The cell envelope of Thermotogae suggests a mechanism for outer membrane biogenesis.

The presence of a cell membrane is one of the major structural components defining life. Recent phylogenomic analyses have supported the hypothesis that the last universal common ancestor (LUCA) was likely a diderm. Yet, the mechanisms that guided outer membrane (OM) biogenesis remain unknown. Thermotogae is an early-branching phylum with a unique OM, the toga. Here, we use cryo-electron tomography to characterize the in situ cell envelope architecture of Thermotoga maritima and show that the toga is made of extended sheaths of ß-barrel trimers supporting small (~200 nm) membrane patches. Lipidomic analyses identified the same major lipid species in the inner membrane (IM) and toga, including the rare to bacteria membrane-spanning ether-bound diabolic acids (DAs). Proteomic analyses revealed that the toga was composed of multiple SLH-domain containing Ompα and novel ß-barrel proteins, and homology searches detected variable conservations of these proteins across the phylum. These results highlight that, in contrast to the SlpA/OmpM superfamily of proteins, Thermotoga possess a highly diverse bipartite OM-tethering system. We discuss the implications of our findings with respect to other early-branching phyla and propose that a toga-like intermediate may have facilitated monoderm-to-diderm cell envelope transitions.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation.

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data.

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography-Mass Spectrometry-Based Metabolomics.

Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky-Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC-MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.

HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization.

BACKGROUND: Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans. OBJECTIVES: Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern. METHODS: We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances. RESULTS: We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07µM, the mean absolute error (MAE) values of 1.28 and 1.56µM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 µM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants. DISCUSSION: We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.

Amino Acid Availability Determines Plant Immune Homeostasis in the Rhizosphere Microbiome.

Microbes possess conserved microbe-associated molecular patterns (MAMPs) that are recognized by plant receptors to induce pattern-triggered immunity (PTI). Despite containing the same MAMPs as pathogens, commensals thrive in the plant rhizosphere microbiome, indicating they must suppress or evade host immunity. Previous work found that bacterial-secreted gluconic acid is sufficient to suppress PTI. Here, we show that gluconic acid biosynthesis is not necessary for immunity suppression by the beneficial bacterial strain Pseudomonas simiae WCS417. We performed a forward genetic screen with EMS-mutagenized P. simiae WCS417 and a flagellin-inducible CYP71A12pro:GUS reporter as a PTI readout. We identified a loss of function mutant in ornithine carbamoyltransferase argF, which is required for ornithine conversion to arginine, that cannot suppress PTI or acidify the rhizosphere. Fungal pathogens use alkalization through production of ammonia and glutamate, and arginine biosynthetic precursors, to promote their own growth and virulence. While a ΔargF mutant has a growth defect in the rhizosphere, we found that restoring growth with exogenous arginine resulted in rhizosphere alkalization in a mutant that cannot make gluconic acid, indicating that arginine biosynthesis is required for both growth and acidification. Furthermore, blocking bacterial arginine, glutamine, or proline biosynthesis through genetic mutations or feedback inhibition by adding corresponding amino acids, resulted in rhizosphere alkalization. Untargeted metabolomics determined that ornithine, an alkaline molecule, accumulates under conditions associated with rhizosphere alkalization. Our findings show that bacterial amino acid biosynthesis contributes to acidification by preventing accumulation of ornithine and the resulting alkalization. IMPORTANCE Understanding how microbiota evade and suppress host immunity is critical to our knowledge of how beneficial microbes persist in association with a host. Prior work has shown that secretion of organic acids by beneficial microbes is sufficient to suppress plant immunity. This work shows that microbial amino acid metabolism is not only critical for growth in the plant rhizosphere microbiome, but also for regulation of plant rhizosphere pH, and, consequentially, regulation of plant immunity. We found that, in the absence of microbial glutamate and arginine metabolism, rhizosphere alkalization and microbial overgrowth occurs. Collectively, our findings suggest that, by regulating nutrient availability, plants have the potential to regulate their immune homeostasis in the rhizosphere microbiome.

The role of the HLA allelic repertoire on the clinical severity of COVID-19 in Canadians, living in the Saskatchewan province.

AIMS: The HLA system has been implicated as an underlying determinant for modulating the immune response to SARS-CoV-2. In this study, we aimed to determine the association of patients' HLA genetic profiles with the disease severity of COVID-19 infection. METHODS: Prospective study was conducted on COVID-19 patients (n = 40) admitted to hospitals in Saskatoon, Canada, between March and December 2020. Next-generation sequencing was performed on the patient samples to obtain high-resolution HLA typing profiles. The statistical association between HLA allelic frequency and disease severity was examined. The disease severity was categorized based on the length of hospital stay and intensive care needs or demise during the hospital stay. RESULTS: HLA allelic frequencies of the high and low-severity cohorts were normalized against corresponding background allelic frequencies. In the high-severity cohort, A*02:06 (11.8-fold), B*51:01 (2.4-fold), B*15:01(3.1-fold), C*01:02 (3.3-fold), DRB1*08:02 (31.2-fold), DQ*06:09 (11-fold), and DPB1*04:02(4-fold) were significantly overrepresented (p < 0.05) making these deleterious alleles. In the low-severity cohort, A*24:02 (2.8-fold), B*35:01 (2.8-fold), DRB1*04:07 (5.3-fold), and DRB1*08:11 (22-fold) were found to be significantly overrepresented (p < 0.05) making these protective alleles. These above alleles interact with NK cell antiviral activity via the killer immunoglobulin-like receptors (KIR). The high-severity cohort had a higher predilection for HLA alleles associated with KIR subgroups; Bw4-80I (1.1-fold), and C1 (1.6-fold) which promotes NK cell inhibition, while the low-severity cohort had a higher predilection for Bw4-80T (1.6-fold), and C2 (1.6-fold) which promote NK cell activation. CONCLUSION: In this study, the HLA allelic repository with the distribution of deleterious and protective alleles was found to correlate with the severity of the clinical course in COVID-19. Moreover, the interaction of specific HLA alleles with the KIR-associated subfamily modulates the NK cell-mediated surveillance of SARS-CoV-2. Both deleterious HLA alleles and inhibitory KIR appear prominently in the severe COVID-19 group focusing on the importance of NK cells in the convalescence of COVID-19.