Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning.

Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen Streptococcus pneumoniae. We explored a collection of 662 S. pneumoniae genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the folA and sulA loci are responsible for TMP non-susceptibility in S. pneumoniae and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within ±1 twofold dilution factor of 86.3%. Our roadmap of in silico analysis-wet-lab validation-diagnostic tool building could be adapted to explore AMR in other combinations of bacteria-antibiotic. IMPORTANCE: In the age of next-generation sequencing (NGS), while data-driven methods such as genome-wide association study (GWAS) and machine learning (ML) excel at finding patterns, functional validation can be challenging due to the high numbers of candidate variants. We designed an integrative approach combining a GWAS on S. pneumoniae clinical isolates, followed by whole-genome transformation coupled with NGS to functionally characterize a large set of GWAS candidates. Our study validated several phenotypic folA mutations beyond the standard Ile100Leu mutation, and showed that the overexpression of the sulA locus produces trimethoprim (TMP) resistance in Streptococcus pneumoniae. These validated loci, when used to build ML models, were found to be the best inputs for predicting TMP minimal inhibitory concentrations. Integrative approaches can bridge the genotype-phenotype gap by biological insights that can be incorporated in ML models for accurate prediction of drug susceptibility.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

MOTIVATION: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox. RESULTS: In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. AVAILABILITY AND IMPLEMENTATION: DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Single cell tracing of Pomc neurons reveals recruitment of 'Ghost' subtypes with atypical identity in a mouse model of obesity.

The hypothalamus contains a remarkable diversity of neurons that orchestrate behavioural and metabolic outputs in a highly plastic manner. Neuronal diversity is key to enabling hypothalamic functions and, according to the neuroscience dogma, it is predetermined during embryonic life. Here, by combining lineage tracing of hypothalamic pro-opiomelanocortin (Pomc) neurons with single-cell profiling approaches in adult male mice, we uncovered subpopulations of 'Ghost' neurons endowed with atypical molecular and functional identity. Compared to 'classical' Pomc neurons, Ghost neurons exhibit negligible Pomc expression and are 'invisible' to available neuroanatomical approaches and promoter-based reporter mice for studying Pomc biology. Ghost neuron numbers augment in diet-induced obese mice, independent of neurogenesis or cell death, but weight loss can reverse this shift. Our work challenges the notion of fixed, developmentally programmed neuronal identities in the mature hypothalamus and highlight the ability of specialised neurons to reversibly adapt their functional identity to adult-onset obesogenic stimuli.

THBS1+ myeloid cells expand in SLD hepatocellular carcinoma and contribute to immunosuppression and unfavorable prognosis through TREM1.

Hepatocellular carcinoma (HCC) is an inflammation-associated cancer arising from viral or non-viral etiologies including steatotic liver diseases (SLDs). Expansion of immunosuppressive myeloid cells is a hallmark of inflammation and cancer, but their heterogeneity in HCC is not fully resolved and might underlie immunotherapy resistance. Here, we present a high-resolution atlas of innate immune cells from patients with HCC that unravels an SLD-associated contexture characterized by influx of inflammatory and immunosuppressive myeloid cells, including a discrete population of THBS1+ regulatory myeloid (Mreg) cells expressing monocyte- and neutrophil-affiliated genes. THBS1+ Mreg cells expand in SLD-associated HCC, populate fibrotic lesions, and are associated with poor prognosis. THBS1+ Mreg cells are CD163+ but distinguished from macrophages by high expression of triggering receptor expressed on myeloid cells 1 (TREM1), which contributes to their immunosuppressive activity and promotes HCC tumor growth in vivo. Our data support myeloid subset-targeted immunotherapies to treat HCC.

Systemic Convergent Multitarget Interactions of Plant Polyphenols Revealed by Affinity-Based Protein Profiling of Bone Cells Using C-Glucosidic Vescal(ag)in-Bearing Chemoproteomic Probes.

The ellagitannins vescalagin and vescalin, known as actin-dependent inhibitors of osteoclastic bone resorption, were mounted onto chemical probes to explore their interactions with bone cell proteins by means of affinity-based chemoproteomics and bioinformatics. The chemical reactivity of the pyrogallol units of these polyphenols toward oxidation into electrophilic ortho-quinones was exploited using NaIO4 to promote the covalent capture of target proteins, notably those expressed at lower abundance and those interacting with polyphenols at low-to-moderate levels of affinity. Different assays revealed the multitarget nature of both ellagitannins, with 100-370 statistically significant proteins captured by their corresponding probes. A much higher number of proteins were captured from osteoclasts than from osteoblasts. Bioinformatic analyses unveiled a preference for the capture of proteins having phosphorylated ligands and GTPase regulators and enabled the identification of 33 potential target proteins with systemic relevance to osteoclast differentiation and activity, as well as to the regulation of actin dynamics.

Sex-specific DNA methylation and transcription of zbtb38 and effects of gene-environment interactions on its natural antisense transcript in zebrafish.

There is increasing evidence for the involvement of epigenetics in sex determination, maintenance, and plasticity, from plants to humans. In our previous work, we reported a transgenerational feminization of a zebrafish population for which the first generation was exposed to cadmium, a metal with endocrine disrupting effects. In this study, starting from the previously performed whole methylome analysis, we focused on the zbtb38 gene and hypothesized that it could be involved in sex differentiation and Cd-induced offspring feminization. We observed sex-specific patterns of both DNA methylation and RNA transcription levels of zbtb38. We also discovered that the non-coding exon 3 of zbtb38 encodes for a natural antisense transcript (NAT). The activity of this NAT was found to be influenced by both genetic and environmental factors. Furthermore, increasing transcription levels of this NAT in parental gametes was highly correlated with offspring sex ratios. Since zbtb38 itself encodes for a transcription factor that binds methylated DNA, our results support a non-negligible role of zbtb38 not only in orchestrating the sex-specific transcriptome (i.e., sex differentiation) but also, via its NAT, offspring sex ratios.

Synthesis of Flavonol-Bearing Probes for Chemoproteomic and Bioinformatic Analyses of Asteraceae Petals in Search of Novel Flavonoid Enzymes.

This study aimed at searching for the enzymes that are responsible for the higher hydroxylation of flavonols serving as UV-honey guides for pollinating insects on the petals of Asteraceae flowers. To achieve this aim, an affinity-based chemical proteomic approach was developed by relying on the use of quercetin-bearing biotinylated probes, which were thus designed and synthesized to selectively and covalently capture relevant flavonoid enzymes. Proteomic and bioinformatic analyses of proteins captured from petal microsomes of two Asteraceae species (Rudbeckia hirta and Tagetes erecta) revealed the presence of two flavonol 6-hydroxylases and several additional not fully characterized proteins as candidates for the identification of novel flavonol 8-hydroxylases, as well as relevant flavonol methyl- and glycosyltransferases. Generally speaking, this substrate-based proteome profiling methodology constitutes a powerful tool for the search for unknown (flavonoid) enzymes in plant protein extracts.

Transgenerational endocrine disruptor effects of cadmium in zebrafish and contribution of standing epigenetic variation to adaptation.

Evidence has emerged that environmentally-induced epigenetic changes can have long-lasting effects on gene transcription across generations. These recent findings highlight the need to investigate the transgenerational impacts of pollutants to assess their long term effects on populations. In this study, we investigated the transgenerational effect of cadmium on zebrafish across 4 generations. A first whole methylome approach carried out on fish of the first two generations led us to focus our investigations on the estradiol receptor alpha gene (esr1). We observed a sex-dependent transgenerational inheritance of Cd-induced DNA methylation changes up to the last generation. These changes were associated with single nucleotide polymorphisms (SNPs) that were themselves at the origin of the creation or deletion of methylation sites. Thus, Cd-induced genetic selection gave rise to DNA methylation changes. We also analyzed the transcription level of various sections of esr1 as well as estrogen responsive genes. While Cd triggered transgenerational disorders, Cd-induced epigenetic changes in esr1 contributed to the rapid transgenerational adaptation of fish to Cd. Our results provide insight into the processes underpinning rapid adaptation and highlight the need to maintain genetic diversity within natural populations to bolster the resilience of species faced with the global environmental changes.

Effects of Lumacaftor-Ivacaftor on Airway Microbiota-Mycobiota and Inflammation in Patients with Cystic Fibrosis Appear To Be Linked to Pseudomonas aeruginosa Chronic Colonization.

Lumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination approved for patients with cystic fibrosis (CF) who are homozygous for the F508del allele. This treatment showed significant clinical improvement; however, few studies have addressed the evolution of the airway microbiota-mycobiota and inflammation in patients receiving lumacaftor-ivacaftor treatment. Seventy-five patients with CF aged 12 years or older were enrolled at the initiation of lumacaftor-ivacaftor therapy. Among them, 41 had spontaneously produced sputa collected before and 6 months after treatment initiation. Airway microbiota and mycobiota analyses were performed via high-throughput sequencing. Airway inflammation was assessed by measuring the calprotectin levels in sputum; the microbial biomass was evaluated via quantitative PCR (qPCR). At baseline (n = 75), bacterial alpha-diversity was correlated with pulmonary function. After 6 months of lumacaftor-ivacaftor treatment, a significant improvement in the body mass index and a decreased number of intravenous antibiotic courses were noted. No significant changes in bacterial and fungal alpha- and beta-diversities, pathogen abundances, or calprotectin levels were observed. However, for patients not chronically colonized with Pseudomonas aeruginosa at treatment initiation, calprotectin levels were lower, and a significant increase in bacterial alpha-diversity was observed at 6 months. This study shows that the evolution of the airway microbiota-mycobiota in CF patients depends on the patient's characteristics at lumacaftor-ivacaftor treatment initiation, notably chronic colonization with P. aeruginosa. IMPORTANCE The management of cystic fibrosis has been transformed recently by the advent of CFTR modulators, including lumacaftor-ivacaftor. However, the effects of such therapies on the airway ecosystem, particularly on the microbiota-mycobiota and local inflammation, which are involved in the evolution of pulmonary damage, are unclear. This multicenter study of the evolution of the microbiota under protein therapy supports the notion that CFTR modulators should be started as soon as possible, ideally before the patient is chronically colonized with P. aeruginosa. (This study has been registered at ClinicalTrials.gov under identifier NCT03565692).

Genetic and epigenetic interplay allows rapid transgenerational adaptation to metal pollution in zebrafish.

Despite still being a matter of debate, there is growing evidence that pollutant-induced epigenetic changes can be propagated across generations. Whereas such modifications could have long-lasting effects on organisms and even on population, environmentally relevant data from long-term exposure combined with follow-up through multiple generations remain scarce for non-mammalian species. We performed a transgenerational experiment comprising four successive generations of zebrafish. Only fish from the first generation were exposed to an environmentally realistic concentration of cadmium (Cd). Using a whole methylome analysis, we first identified the DNA regions that were differentially methylated in response to Cd exposure and common to fish of the first two generations. Among them, we then focused our investigations on the exon 3 (ex3) of the cep19 gene. We indeed recorded transgenerational growth disorders in Cd-exposed fish, and a mutation in this exon is known to cause morbid obesity in mammals. Its methylation level was thus determined in zebrafish from all the four generations by means of a targeted and base resolution method. We observed a transgenerational inheritance of Cd-induced DNA methylation changes up to the fourth generation. However, these changes were closely associated with genetic variations, mainly a single nucleotide polymorphism. This single nucleotide polymorphism was itself at the origin of the creation or deletion of a methylation site and deeply impacted the methylation level of neighboring methylation sites. Cd-induced epigenetic changes were associated with different mRNA transcripts and an improved condition of Cd fish. Our results emphasize a tight relationship between genetic and epigenetic mechanisms and suggest that their interplay and pre-existing diversity can allow rapid adaptation to anthropogenic environmental changes.

Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis.

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose. Lactate dehydrogenases (LDHA and LDHB), which we found spatially expressed in GB tissues, catalyze the interconversion of pyruvate and lactate. However, ablation of both LDH isoforms, but not only one, led to a reduction in tumor growth and an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OXPHOS) in the LDHA/B KO group which sensitized tumors to cranial irradiation, thus improving mouse survival. When mice were treated with the antiepileptic drug stiripentol, which targets LDH activity, tumor growth decreased. Our findings unveil the complex metabolic network in which both LDHA and LDHB are integrated and show that the combined inhibition of LDHA and LDHB strongly sensitizes GB to therapy.

VirHunter: A Deep Learning-Based Method for Detection of Novel RNA Viruses in Plant Sequencing Data.

High-throughput sequencing has provided the capacity of broad virus detection for both known and unknown viruses in a variety of hosts and habitats. It has been successfully applied for novel virus discovery in many agricultural crops, leading to the current drive to apply this technology routinely for plant health diagnostics. For this, efficient and precise methods for sequencing-based virus detection and discovery are essential. However, both existing alignment-based methods relying on reference databases and even more recent machine learning approaches are not efficient enough in detecting unknown viruses in RNAseq datasets of plant viromes. We present VirHunter, a deep learning convolutional neural network approach, to detect novel and known viruses in assemblies of sequencing datasets. While our method is generally applicable to a variety of viruses, here, we trained and evaluated it specifically for RNA viruses by reinforcing the coding sequences' content in the training dataset. Trained on the NCBI plant viruses data for three different host species (peach, grapevine, and sugar beet), VirHunter outperformed the state-of-the-art method, DeepVirFinder, for the detection of novel viruses, both in the synthetic leave-out setting and on the 12 newly acquired RNAseq datasets. Compared with the traditional tBLASTx approach, VirHunter has consistently exhibited better results in the majority of leave-out experiments. In conclusion, we have shown that VirHunter can be used to streamline the analyses of plant HTS-acquired viromes and is particularly well suited for the detection of novel viral contigs, in RNAseq datasets.

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images.

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.

DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images.

Detection of RNA spots in single-molecule fluorescence in-situ hybridization microscopy images remains a difficult task, especially when applied to large volumes of data. The variable intensity of RNA spots combined with the high noise level of the images often requires manual adjustment of the spot detection thresholds for each image. In this work, we introduce DeepSpot, a Deep Learning-based tool specifically designed for RNA spot enhancement that enables spot detection without the need to resort to image per image parameter tuning. We show how our method can enable downstream accurate spot detection. DeepSpot's architecture is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for context aggregation for small object and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity, and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced with our method by testing DeepSpot on 20 simulated and 3 experimental datasets, and showed that accuracy of more than 97% is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot provides more precise mRNA detection. In addition, we generated single-molecule fluorescence in-situ hybridization images of mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

Prediction of RNA subcellular localization: Learning from heterogeneous data sources.

RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression.

BACKGROUND: Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. METHODS: In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. RESULTS: Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. CONCLUSION: A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Baseline co-medications may alter the anti-tumoural effect of checkpoint inhibitors as well as the risk of immune-related adverse events.

PURPOSE: As gut microbiota composition is an important determinant of response to immune checkpoint inhibitors (ICIs), we examined the effect of various co-medications known for their interaction with microbiota, when given at ICI initiation. PATIENTS AND METHODS: We identified patients with advanced cancer treated with ICI between May 2015 and September 2017 in our institution. Co-medications given within 1 month before or 1 month after the first administration of ICI were reviewed from medical records. Survival data were analysed with univariable Cox regression, and the combined effect of multiple factors was assessed with factor analysis of mixed data (FAMD). The impact of co-medications on immune-related adverse events (irAEs) occurrence was also assessed. RESULTS: A total of 635 patients were included. Psychotropic drugs (41%), proton pump inhibitors (PPIs; 38%), angiotensin-converting enzyme (ACE) inhibitors and/or angiotensin II receptor blockers (ARBs; 32%), glucocorticoids (26%), antibiotics (24%), statins (21%) and morphine (20%) were the most prescribed co-medications. Baseline use of antibiotics, glucocorticoids >10 mg/day, PPIs, psychotropic drugs, morphine and insulin was associated with significantly shortened overall survival and decreased tumour response, whereas coadministration of statins, ACEs and/or ARBs, non-steroidal anti-inflammatory drugs, aspirin and oral antidiabetic drugs did not impact patient outcomes. Treatments that altered the response to ICI were also associated with a decreased incidence of irAEs. FAMD revealed the respective weight of each factor or co-medication on the oncological outcomes. CONCLUSION: Co-medications must be carefully assessed at the time of ICI initiation and clinicians aware of their possible deleterious effect, notably for PPIs, glucocorticoids, antibiotics and psychotropic drugs.

Population genomics of apricots unravels domestication history and adaptive events.

Among crop fruit trees, the apricot (Prunus armeniaca) provides an excellent model to study divergence and adaptation processes. Here, we obtain nearly 600 Armeniaca apricot genomes and four high-quality assemblies anchored on genetic maps. Chinese and European apricots form two differentiated gene pools with high genetic diversity, resulting from independent domestication events from distinct wild Central Asian populations, and with subsequent gene flow. A relatively low proportion of the genome is affected by selection. Different genomic regions show footprints of selection in European and Chinese cultivated apricots, despite convergent phenotypic traits, with predicted functions in both groups involved in the perennial life cycle, fruit quality and disease resistance. Selection footprints appear more abundant in European apricots, with a hotspot on chromosome 4, while admixture is more pervasive in Chinese cultivated apricots. Our study provides clues to the biology of selected traits and targets for fruit tree research and breeding.

Antibodies against HLA cross-reactivity groups: From single antigen bead assay to immunoinformatics interpretation of epitopes.

Identification of anti-human leukocyte antigen (HLA) antibodies (Abs) is based on Luminex™ technology. We used bioinformatics to (i) study the correlations of mean fluorescence intensities (MFIs) for all the possible allele pairs, and (ii) determine the degree of epitope homology between HLA antigens. Using MFI data on anti-HLA Abs from 6000 Luminex™ assays, we provide an updated overview of class I and II HLA antigen cross-reactivity in which each node corresponded to an allele and each link corresponded to a strong correlation between two alleles (Spearman's ρ > 0.8). We compared these correlations with the serological groups and the results of an epitope analysis. The strongest correlations concerned allele-specific Abs directed against the same antigen. For the HLA-A locus, the highest values of Spearman's ρ reflected broad specificity. For the HLA-B locus, graphs defined the HLA-Bw4 public epitope, and correlations between HLA-A and -B alleles were only present for beads with the same Bw4 public epitope. For the HLA-C locus, we identified two groups that differed with regard to their KIR ligand subclassification. Lastly, the HLA-DRB1 subgroups were part of a network. In the epitope analysis, Spearman's ρ was related to the number of matched epitopes within pairs of alleles. The combination of Spearman's ρ with simple, undirected graphing constitutes an effective tool for understanding routinely encountered cross-reactivity profiles. Based on this model, we have implemented an online data visualization tool available at http://cusureau.pythonanywhere.com/.