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Plant growth is ultimately driven by cell division and expansion, but how these processes are regulated to mediate a wide range of genotypic variation in organ size is still poorly understood. To address this, we screened an EMS maize mutant population to identify a new EMS maize dwarf mutant with small, pale-yellow leaves (dpl). The mutation was mapped to a region of 11.58 Mb at the 3’ end of chromosome 7. We identified Zm00001d022394 as a potential causal gene for the dpl phenotype, encoding a pentatricopeptide repeat-containing (PPR) family protein involved in chloroplast gene expression and function, explaining the pale color of dpl. Mature dpl leaves are thinner and shorter due to a reduced number of cells of approximately normal length. The chloroplasts of dpl are reduced in size and number, correlating with a decreased chlorophyll content, however chloroplast ultrastructure was not affected. Consistent with the reduced chlorophyll content photosynthetic rate of dpl were reduced by 50 % and a 30 reduction of Fv/Fm suggests photoinhibition. As a consequence, soluble and insoluble sugar levels are severely reduced throughout the leaf growth zone. At the cell level reduced cell division rates and size of the division zone, explain the reduced leaf elongation rate (LER). The growth of dpl leaves can be restored by supplying growing leaves with sucrose through their cut tips, which also restores sucrose levels in the division zone of maize leaf, demonstrating that limited sugar availability explains the reduced growth phenotype. Inversely, we phenocopied the mutant growth phenotype by inhibiting photosynthetic electron transport in wild type plants with DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea). Our study of dpl provides a functional link between inhibition of photosynthesis, soluble sugar flux to the leaf growth zone, the regulation of cell division and whole leaf growth.

Abstract Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. Scientific Contribution We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

Abstract Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Abstract A large proportion of HIV-coinfected visceral leishmaniasis (VL-HIV) patients exhibit chronic disease with frequent VL recurrence. However, knowledge on immunological determinants underlying the disease course is scarce. We longitudinally profiled the circulatory cellular immunity of an Ethiopian HIV cohort that included VL developers. We show that chronic VL-HIV patients exhibit high and persistent levels of TIGIT and PD-1 on CD8+/CD8- T cells, in addition to a lower frequency of IFN-γ+ TIGIT- CD8+/CD8- T cells, suggestive of impaired T cell functionality. At single T cell transcriptome and clonal resolution, the patients show CD4+ T cell anergy, characterised by a lack of T cell activation and lymphoproliferative response. These findings suggest that PD-1 and TIGIT play a pivotal role in VL-HIV chronicity, and may be further explored for patient risk stratification. Our findings provide a strong rationale for adjunctive immunotherapy for the treatment of chronic VL-HIV patients to break the recurrent disease cycle.

Summary: The role of T cell receptor (TCR) diversity in infectious disease susceptibility is not well understood. We use a systems immunology approach on three cohorts of herpes zoster (HZ) patients and controls to investigate whether TCR diversity against varicella-zoster virus (VZV) influences the risk of HZ. We show that CD4+ T cell TCR diversity against VZV glycoprotein E (gE) and immediate early 63 protein (IE63) after 1-week culture is more restricted in HZ patients. Single-cell RNA and TCR sequencing of VZV-specific T cells shows that T cell activation pathways are significantly decreased after stimulation with VZV peptides in convalescent HZ patients. TCR clustering indicates that TCRs from HZ patients co-cluster more often together than TCRs from controls. Collectively, our results suggest that not only lower VZV-specific TCR diversity but also reduced functional TCR affinity for VZV-specific proteins in HZ patients leads to lower T cell activation and consequently affects the susceptibility for viral reactivation.

Abstract Background Plasmodium vivax is the second most important cause of human malaria worldwide, and accounts for the majority of malaria cases in South America. A high-quality reference genome exists for Papua Indonesia (PvP01) and Thailand (PvW1), but is lacking for South America. A reference genome specifically for South America would be beneficial though, as P. vivax is a genetically diverse parasite with geographical clustering. Results This study presents a new high-quality assembly of a South American P. vivax isolate, referred to as PvPAM (P. vivax Peruvian AMazon). The genome was obtained from a low input patient sample from the Peruvian Amazon and sequenced using PacBio technology, resulting in a highly complete assembly with 6497 functional genes. Telomeric ends were present in 17 out of 28 chromosomal ends, and additional (sub)telomeric regions are present in 12 unassigned contigs. A comparison of multigene families between PvPAM and the PvP01 genome revealed remarkable variation in vir genes, and the presence of merozoite surface proteins (MSP) 3.6 and 3.7. Three dhfr and dhps drug resistance associated mutations are present in PvPAM, similar to those found in other Peruvian isolates. Mapping of publicly available South American whole genome sequencing (WGS) data to PvPAM resulted in significantly fewer variants and truncated reads compared to the use of PvP01 or PvW1 as reference genomes. To minimize the number of core genome variants in non-South American samples, PvW1 is most suited for Southeast Asian isolates, both PvPAM and PvW1 are suited for South Asian isolates, and PvPAM is recommended for African isolates. Interestingly, non-South American samples still contained the least subtelomeric variants when mapped to PvPAM, indicating high quality of the PvPAM subtelomeric regions. Conclusions Our findings show that the PvPAM reference genome more accurately represents South American P. vivax isolates in comparison to PvP01 and PvW1. In addition, PvPAM has a high level of completeness, and contains a similar number of annotated genes as PvP01 or PvW1. The PvPAM genome therefore will be a valuable resource to improve future genomic analyses on P. vivax isolates from the South American continent.

BackgroundRifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. Whole genome sequencing could more accurately guide individualized treatment as the full drug resistance profile is obtained with a single test. Whole genome sequencing has not reached its full potential for patient care, in part due to the complexity of translating a resistance profile into the most effective individualized regimen.MethodsWe developed a treatment recommender clinical decision support system (CDSS) and an accompanying web application for user-friendly recommendation of the optimal individualized treatment regimen to a clinician.ResultsFollowing expert stakeholder meetings and literature review, nine drug features and 14 treatment regimen features were identified and quantified. Using machine learning, a model was developed to predict the optimal treatment regimen based on a training set of 3895 treatment regimen-expert feedback pairs. The acceptability of the treatment recommender CDSS was assessed as part of a clinical trial and in a routine care setting. Within the clinical trial setting, all patients received the CDSS recommended treatment. In 8 of 20 cases, the initial recommendation was recomputed because of stock out, clinical contra-indication or toxicity. In routine care setting, physicians rejected the treatment recommendation in 7 out of 15 cases because it deviated from the national TB treatment guidelines. A survey indicated that the treatment recommender CDSS is easy to use and useful in clinical practice but requires digital infrastructure support and training.ConclusionsOur findings suggest that global implementation of the novel treatment recommender CDSS holds the potential to improve treatment outcomes of patients with RR-TB, especially those with 'difficult-to-treat' forms of RR-TB.

ABSTRACTWhole-genome sequencing of Plasmodium is becoming an increasingly important tool for genomic surveillance of malaria. Due to the predominance of human DNA in a patient blood sample, time-consuming laboratory procedures are required to deplete human DNA or enrich Plasmodium DNA. Here, we investigated the potential of nanopore adaptive sampling to enrich Plasmodium falciparum reads while sequencing unenriched patient blood samples. To compare adaptive sampling versus regular sequencing on a MinION device, a dilution series consisting of 0%–84% P. falciparum DNA in human DNA was sequenced. Half of the flow cell channels were run in adaptive sampling mode, enriching for the P. falciparum reference genome, resulting in a three- to five-fold enrichment of P. falciparum bases in samples containing 0.1%–8.4% P. falciparum DNA. This finding was confirmed by sequencing three P. falciparum patient blood samples with common levels of parasitemia, that is, 0.1%, 0.2%, and 0.6% in adaptive mode. Their estimated enrichment was 5.8, 3.9, and 2.7, respectively, which was sufficient to cover at least 97% of the P. falciparum reference genome at a median depth of 5 (lowest parasitemia) or 355 (highest parasitemia). In all, 38 drug resistance loci were compared to Sanger sequencing results, showing high concordance, which suggests that the obtained sequencing data are of sufficient quality to address common clinical research questions for patients with parasitemias of 0.1% and higher. Overall, our results indicate that adaptive nanopore sequencing has the potential to replace more time-consuming Plasmodium enrichment protocols in the future.IMPORTANCEMalaria is caused by parasites of the genus Plasmodium, and reached a global disease burden of 247 million cases in 2021. To study drug resistance mutations and parasite population dynamics, whole-genome sequencing of patient blood samples is commonly performed. However, the predominance of human DNA in these samples imposes the need for time-consuming laboratory procedures to enrich Plasmodium DNA. We used the Oxford Nanopore Technologies’ adaptive sampling feature to circumvent this problem and enrich Plasmodium reads directly during the sequencing run. We demonstrate that adaptive nanopore sequencing efficiently enriches Plasmodium reads, which simplifies and shortens the timeline from blood collection to parasite sequencing. In addition, we show that the obtained data can be used for monitoring genetic markers, or to generate nearly complete genomes. Finally, owing to its inherent mobility, this technology can be easily applied on-site in endemic areas where patients would benefit the most from genomic surveillance.

Abstract Antimicrobial resistant Salmonella enterica serovar Concord (S. Concord) is known to cause severe gastrointestinal and bloodstream infections in patients from Ethiopia and Ethiopian adoptees, and occasional records exist of S. Concord linked to other countries. The evolution and geographical distribution of S. Concord remained unclear. Here, we provide a genomic overview of the population structure and antimicrobial resistance (AMR) of S. Concord by analysing genomes from 284 historical and contemporary isolates obtained between 1944 and 2022 across the globe. We demonstrate that S. Concord is a polyphyletic serovar distributed among three Salmonella super-lineages. Super-lineage A is composed of eight S. Concord lineages, of which four are associated with multiple countries and low levels of AMR. Other lineages are restricted to Ethiopia and horizontally acquired resistance to most antimicrobials used for treating invasive Salmonella infections in low- and middle-income countries. By reconstructing complete genomes for 10 representative strains, we demonstrate the presence of AMR markers integrated in structurally diverse IncHI2 and IncA/C2 plasmids, and/or the chromosome. Molecular surveillance of pathogens such as S. Concord supports the understanding of AMR and the multi-sector response to the global AMR threat. This study provides a comprehensive baseline data set essential for future molecular surveillance.

Abstract Background Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients’ blood gene expression and applying machine learning on the transcriptome data to develop predictive models. Methods RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. Results Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. Conclusion Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Radiotherapy is the standard of care for breast cancer. However, surviving radioresistant cells can repopulate following treatment and provoke relapse. Better understanding of the molecular mechanisms of radiation resistance may help to improve treatment of radioresistant tumours. To emulate radiation therapy at the cellular level, we exposed MCF7 breast cancer cells to daily radiation doses of 2 Gy up to an accumulated dose of 20 Gy. Fractionally irradiated cells (FIR20) displayed increased clonogenic survival and population doubling time as compared with age‐matched sham‐irradiated cells and untreated parental MCF7 cells. RNA‐sequencing revealed a core signature of 229 mRNAs and 7 circular RNAs of which the expression was significantly altered in FIR20 cells. Dysregulation of several top genes was mirrored at the protein level. The FIR20 cell transcriptome overlapped significantly with canonical radiation response signatures and demonstrated a remarkable commonality with radiation and endocrine therapy resistance expression profiles, suggesting crosstalk between both acquired resistance pathways, as indicated by reduced sensitivity to tamoxifen cytotoxicity of FIR20 cells. Using predictive analyses and functional enrichment, we identified a gene‐regulatory network that promotes stemness and inflammatory signalling in FIR20 cells. We propose that these phenotypic traits render breast cancer cells more radioresistant but may at the same time serve as potential targets for combination therapies.

Despite the general agreement on the significance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain. Understanding this aspect could provide insights for adjusting vaccines and maintaining robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to SARS-CoV-2 epitopes unique to the virus (SC2-unique) or shared with other coronaviruses (CoV-common), we trained a large number of T-cell receptor (TCR) – epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. These models were then applied to longitudinal CD8+ TCR repertoires from critical and non-critical COVID-19 patients. In spite of comparable initial CoV-common TCR repertoire depth and CD8+ T-cell depletion, the temporal dynamics of SC2-unique TCRs differed depending on the disease severity. Specifically, while non-critical patients demonstrated a large and diverse SC2-unique TCR repertoire by the second week of the disease, critical patients did not. Furthermore, only non-critical patients exhibited redundancy in the CD8+ T-cell response to both groups of epitopes, SC2-unique and CoV-common. These findings indicate a valuable contribution of the SC2-unique CD8+ TCR repertoires. Therefore, a combination of specific and cross-reactive CD8+ T-cell responses may offer a stronger clinical advantage. Besides tracking the specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire, our analytical framework can be expanded to more epitopes and assist in the assessment and monitoring of CD8+ T-cell response to other infections.

With Varicella-Zoster Virus (VZV) being an exclusive human pathogen, human induced pluripotent stem cell (hiPSC)-derived neural cell culture models are an emerging tool to investigate VZV neuro-immune interactions. Using a compartmentalized hiPSC-derived neuronal model allowing axonal VZV infection, we previously demonstrated that paracrine interferon (IFN)-α2 signalling is required to activate a broad spectrum of interferon-stimulated genes able to counteract a productive VZV infection in hiPSC-neurons. In this new study, we now investigated whether innate immune signalling by VZV-challenged macrophages was able to orchestrate an antiviral immune response in VZV-infected hiPSC-neurons. In order to establish an isogenic hiPSC-neuron/hiPSC-macrophage co-culture model, hiPSC-macrophages were generated and characterised for phenotype, gene expression, cytokine production and phagocytic capacity. Even though immunological competence of hiPSC-macrophages was shown following stimulation with the poly(dA:dT) or treatment with IFN-α2, hiPSC-macrophages in co-culture with VZV-infected hiPSC-neurons were unable to mount an antiviral immune response capable of suppressing a productive neuronal VZV infection. Subsequently, a comprehensive RNA-Seq analysis confirmed the lack of strong immune responsiveness by hiPSC-neurons and hiPSC-macrophages upon, respectively, VZV infection or challenge. This may suggest the need of other cell types, like T-cells or other innate immune cells, to (co-)orchestrate an efficient antiviral immune response against VZV-infected neurons.

Abstract Background Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.

Schizophrenia (SCZ) and bipolar disorder (BD) are associated with immunological dysfunctions that have been hypothesized to lead to clinical symptomatology in particular through kynurenine pathway abnormalities. The aim of this study was thus to investigate the impact of serum kynurenine metabolite levels on diagnosis, clinical state, symptom severity and clinical course in a large French transdiagnostic cohort of SCZ and BD patients. Four patient groups (total n = 507) were included in a cross-sectional observational study: 1) hospitalized acute bipolar patients (n = 205); 2) stable bipolar outpatients (n = 116); 3) hospitalized acute schizophrenia patients (n = 111) and 4) stable schizophrenia outpatients (n = 75), in addition to healthy controls (HC) (n = 185). The quantitative determination of serum kynurenine metabolites was performed using liquid chromatography–tandem mass spectrometry.Kynurenine levels were lower in all patients combined compared to HC while ANCOVA analyses did not reveal inter-diagnostic difference between SCZ and BD. Interestingly, hospitalized patients of both diagnostic groups combined displayed significantly lower kynurenine levels than stabilized outpatients. Psychotic symptoms were associated with lower quinaldic acid (F = 9.18, p=

The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine responses are governed by levels that, when interrogated, separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modeling could aid in gaining actionable insights on response markers shared across populations, capture the immune system’s diversity, and disentangle confounders. We thus sought to assess this multi-view modeling capacity on the responsiveness to the Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine-induced antibodies against the HBV surface antigen (anti-HBs) in early converters (n = 21; n = 9; 10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T-cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modeling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction outperformed single-view methods in terms of the area under the curve and balanced accuracy, confirming the increase in predictive power to be gained. The interpretation of these findings showed that age, gender, inflammation-related gene sets, and pre-existing vaccine-specific T-cells could be associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements clinical seroconversion and all single modalities. Importantly, this modeling could identify what features could predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify the key features regulating responsiveness.

Aneuploidy causes system-wide disruptions in the stochiometric balances of transcripts, proteins, and metabolites, often resulting in detrimental effects for the organism. The protozoan parasite Leishmania has an unusually high tolerance for aneuploidy, but the molecular and functional consequences for the pathogen remain poorly understood. Here, we addressed this question in vitro and present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. Our analyses unambiguously establish that aneuploidy in Leishmania proportionally impacts the average transcript- and protein abundance levels of affected chromosomes, ultimately correlating with the degree of metabolic differences between closely related aneuploid strains. This proportionality was present in both proliferative and non-proliferative in vitro promastigotes. However, as in other Eukaryotes, we observed attenuation of dosage effects for protein complex subunits and in addition, non-cytoplasmic proteins. Differentially expressed transcripts and proteins between aneuploid Leishmania strains also originated from non-aneuploid chromosomes. At protein level, these were enriched for proteins involved in protein metabolism, such as chaperones and chaperonins, peptidases, and heat-shock proteins. In conclusion, our results further support the view that aneuploidy in Leishmania can be adaptive. Additionally, we believe that the high karyotype diversity in vitro and absence of classical transcriptional regulation make Leishmania an attractive model to study processes of protein homeostasis in the context of aneuploidy and beyond.

The Plasmodium vivax reticulocyte invasion process is still poorly understood, with only a few receptor-ligand interactions identified to date. Individuals with the Southeast Asian ovalocytosis (SAO) phenotype have a deletion in the band 3 protein on the surface of erythrocytes, and are reported to have a lower incidence of clinical P. vivax malaria. Based on this observation, band 3 has been put forward as a receptor for P. vivax invasion, although direct proof is still lacking. In this study, we combined functional ex vivo invasion assays and transcriptome sequencing to uncover a band 3–mediated invasion pathway in P. vivax and potential band 3 ligands. Invasion by P. vivax field isolates was 67%-71% lower in SAO reticulocytes compared with non-SAO reticulocytes. Reticulocyte invasion was decreased by 40% and 27%-31% when blocking with an anti-band 3 polyclonal antibody and a PvTRAg38 peptide, respectively. To identify new band 3 receptor candidates, we mRNA-sequenced schizont-stage isolates used in the invasion assays, and observed high transcriptional variability in multigene and invasion-related families. Transcriptomes of isolates with low or high dependency on band 3 for invasion were compared by differential expression analysis, which produced a list of band 3 ligand candidates with high representation of PvTRAg genes. Our ex vivo invasion assays have demonstrated that band 3 is a P. vivax invasion receptor and confirm previous in vitro studies showing binding between PvTRAg38 and band 3, although the lower and variable inhibition levels observed suggest the involvement of other ligands. By coupling transcriptomes and invasion phenotypes from the same isolates, we identified a list of band 3 ligand candidates, of which the overrepresented PvTRAg genes are the most promising for future research.

Abstract Melanoma remains a deadly cancer despite significant advances in immune checkpoint blockade and targeted therapies. The incidence of melanoma is also growing worldwide, which highlights the need for novel treatment options and strategic combination of therapies. Here, we investigate non‐thermal plasma (NTP), an ionized gas, as a promising, therapeutic option. In a melanoma mouse model, direct treatment of tumors with NTP results in reduced tumor burden and prolonged survival. Physical characterization of NTP treatment in situ reveals the deposited NTP energy and temperature associated with therapy response, and whole transcriptome analysis of the tumor identified several modulated pathways. NTP treatment also enhances the cancer‐immunity cycle, as immune cells in both the tumor and tumor‐draining lymph nodes appear more stimulated to perform their anti‐cancer functions. Thus, our data suggest that local NTP therapy stimulates systemic, anti‐cancer immunity. We discuss, in detail, how these fundamental insights will help direct the translation of NTP technology into the clinic and inform rational combination strategies to address the challenges in melanoma therapy.

Overexpression of the transmembrane mucin MUC13, as seen in inflammatory bowel diseases (IBD), could potentially impact barrier function. This study aimed to explore how inflammation-induced MUC13 disrupts epithelial barrier integrity by affecting junctional protein expression in IBD, thereby also considering the involvement of MUC1. RNA sequencing and permeability assays were performed using LS513 cells transfected with MUC1 and MUC13 siRNA and subsequently stimulated with IL-22. In vivo intestinal permeability and MUC13-related signaling pathways affecting barrier function were investigated in acute and chronic DSS-induced colitis wildtype and Muc13−/− mice. Finally, the expression of MUC13, its regulators and other barrier mediators were studied in IBD and control patients. Mucin knockdown in intestinal epithelial cells affected gene expression of several barrier mediators in the presence/absence of inflammation. IL-22-induced MUC13 expression impacted barrier function by modulating the JAK1/STAT3, SNAI1/ZEB1 and ROCK2/MAPK signaling pathways, with a cooperating role for MUC1. In response to DSS, MUC13 was protective during the acute phase whereas it caused more harm upon chronic colitis. The pathways accounting for the MUC13-mediated barrier dysfunction were also altered upon inflammation in IBD patients. These novel findings indicate an active role for aberrant MUC13 signaling inducing intestinal barrier dysfunction upon inflammation with MUC1 as collaborating partner.

MotivationConvolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction.ResultsExperiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.

Antigen recognition through the T cell receptor (TCR) αβ heterodimer is one of the primary determinants of the adaptive immune response. Vaccines activate naïve T cells with high specificity to expand and differentiate into memory T cells. However, antigen-specific memory CD4 T cells exist in unexposed antigen-naïve hosts. In this study, we use high-throughput sequencing of memory CD4 TCRβ repertoire and machine learning to show that individuals with preexisting vaccine-reactive memory CD4 T cell clonotypes elicited earlier and higher antibody titers and mounted a more robust CD4 T cell response to hepatitis B vaccine. In addition, integration of TCRβ sequence patterns into a hepatitis B epitope-specific annotation model can predict which individuals will have an early and more vigorous vaccine-elicited immunity. Thus, the presence of preexisting memory T cell clonotypes has a significant impact on immunity and can be used to predict immune responses to vaccination.

Varicella-zoster virus (VZV) infection of neuronal cells and the activation of cell-intrinsic antiviral responses upon infection are still poorly understood mainly due to the scarcity of suitable human in vitro models that are available to study VZV. We developed a compartmentalized human-induced pluripotent stem cell (hiPSC)-derived neuronal culture model that allows axonal VZV infection of the neurons, thereby mimicking the natural route of infection. Using this model, we showed that hiPSC-neurons do not mount an effective interferon-mediated antiviral response following VZV infection. Indeed, in contrast to infection with Sendai virus, VZV infection of the hiPSC-neurons does not result in the upregulation of interferon-stimulated genes (ISGs) that have direct antiviral functions. Furthermore, the hiPSC-neurons do not produce interferon-α (IFNα), a major cytokine that is involved in the innate antiviral response, even upon its stimulation with strong synthetic inducers. In contrast, we showed that exogenous IFNα effectively limits VZV spread in the neuronal cell body compartment and demonstrated that ISGs are efficiently upregulated in these VZV-infected neuronal cultures that are treated with IFNα. Thus, whereas the cultured hiPSC neurons seem to be poor IFNα producers, they are good IFNα responders. This could suggest an important role for other cells such as satellite glial cells or macrophages to produce IFNα for VZV infection control.

Abstract Background Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure. Methods In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3′ mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile. Results Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples. Conclusions Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.

Nitric oxide (NO) is a small gaseous signaling molecule responsible for maintaining homeostasis in a myriad of tissues and molecular pathways in neurology and the cardiovasculature. In recent years, there has been increasing interest in the potential interaction between arterial stiffness (AS), an independent cardiovascular risk factor, and neurodegenerative syndromes given increasingly epidemiological study reports. For this reason, we previously investigated the mechanistic convergence between AS and neurodegeneration via the progressive non-selective inhibition of all nitric oxide synthase (NOS) isoforms with N(G)-nitro-L-arginine methyl ester (L-NAME) in C57BL/6 mice. Our previous results showed progressively increased AS in vivo and impaired visuospatial learning and memory in L-NAME-treated C57BL/6 mice. In the current study, we sought to further investigate the progressive molecular signatures in hippocampal tissue via LC–MS/MS proteomic analysis. Our data implicate mitochondrial dysfunction due to progressive L-NAME treatment. Two weeks of L-NAME treatment implicates altered G-protein-coupled-receptor signaling in the nerve synapse and associated presence of seizures and altered emotional behavior. Furthermore, molecular signatures implicate the cerebral presence of seizure-related hyperexcitability after short-term (8 weeks) treatment followed by ribosomal dysfunction and tauopathy after long-term (16 weeks) treatment.

Auranofin (AF) is an FDA-approved antirheumatic drug with anticancer properties that acts as a thioredoxin reductase 1 (TrxR) inhibitor. The exact mechanisms through which AF targets cancer cells remain elusive. To shed light on the mode of action, this study provides an in-depth analysis on the molecular mechanisms and immunogenicity of AF-mediated cytotoxicity in the non-small cell lung cancer (NSCLC) cell line NCI–H1299 (p53 Null) and its two isogenic derivates with mutant p53 R175H or R273H accumulation.TrxR is highly expressed in a panel of 72 NSCLC patients, making it a valid druggable target in NSCLC for AF. The presence of mutant p53 overexpression was identified as an important sensitizer for AF in (isogenic) NSCLC cells as it was correlated with reduced thioredoxin (Trx) levels in vitro. Transcriptome analysis revealed dysregulation of genes involved in oxidative stress response, DNA damage, granzyme A (GZMA) signaling and ferroptosis. Although functionally AF appeared a potent inhibitor of GPX4 in all NCI–H1299 cell lines, the induction of lipid peroxidation and consequently ferroptosis was limited to the p53 R273H expressing cells. In the p53 R175H cells, AF mainly induced large-scale DNA damage and replication stress, leading to the induction of apoptotic cell death rather than ferroptosis. Importantly, all cell death types were immunogenic since the release of danger signals (ecto-calreticulin, ATP and HMGB1) and dendritic cell maturation occurred irrespective of (mutant) p53 expression. Finally, we show that AF sensitized cancer cells to caspase-independent natural killer cell-mediated killing by downregulation of several key targets of GZMA.Our data provides novel insights on AF as a potent, clinically available, off-patent cancer drug by targeting mutant p53 cancer cells through distinct cell death mechanisms (apoptosis and ferroptosis). In addition, AF improves the innate immune response at both cytostatic (natural killer cell-mediated killing) and cytotoxic concentrations (dendritic cell maturation).

Background. Prolonged shedding of SARS-CoV-2 in immunocompromised patients has been described. Furthermore, an accumulation of mutations of the SARS-CoV-2 genome in these patients has been observed. Methods. We describe the viral evolution, immunologic response and clinical course of a patient with a lymphoma in complete remission who had received therapy with rituximab and remained SARS-CoV-2 RT-qPCR positive for 161 days. Results. The patient remained hospitalised for 10 days, after which he fully recovered and remained asymptomatic. A progressive increase in Ct-value, coinciding with a progressive rise in lymphocyte count, was seen from day 137 onward. Culture of a nasopharyngeal swab on day 67 showed growth of SARS-CoV-2. Whole genome sequencing (WGS) demonstrated that the virus belonged to the wildtype SARS-CoV-2 clade 20B/GR, but rapidly accumulated a high number of mutations as well as deletions in the N-terminal domain of its spike protein. Conclusion. SARS-CoV-2 persistence in immunocompromised individuals has important clinical implications, but halting immunosuppressive therapy might result in a favourable clinical course. The long-term shedding of viable virus necessitates customized infection prevention measures in these individuals. The observed accelerated accumulation of mutations of the SARS-CoV-2 genome in these patients might facilitate the origin of new VOCs that might subsequently spread in the general community.

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.

Abstract Searching for interesting common subgraphs in graph data is a well-studied problem in data mining. Subgraph mining techniques focus on the discovery of patterns in graphs that exhibit a specific network structure that is deemed interesting within these data sets. The definition of which subgraphs are interesting and which are not is highly dependent on the application. These techniques have seen numerous applications and are able to tackle a range of biological research questions, spanning from the detection of common substructures in sets of biomolecular compounds, to the discovery of network motifs in large-scale molecular interaction networks. Thus far, information about the bioinformatics application of subgraph mining remains scattered over heterogeneous literature. In this review, we provide an introduction to subgraph mining for life scientists. We give an overview of various subgraph mining algorithms from a bioinformatics perspective and present several of their potential biomedical applications.

Abstract High throughput sequencing techniques are poorly adapted for in vivo studies of parasites, which require prior in vitro culturing and purification. Trypanosomatids, a group of kinetoplastid protozoans, possess a distinctive feature in their transcriptional mechanism whereby a specific Spliced Leader (SL) sequence is added to the 5′end of each mRNA by trans-splicing. This allows to discriminate Trypansomatid RNA from mammalian RNA and forms the basis of our new multiplexed protocol for high-throughput, selective RNA-sequencing called SL-seq. We provided a proof-of-concept of SL-seq in Leishmania donovani, the main causative agent of visceral leishmaniasis in humans, and successfully applied the method to sequence Leishmania mRNA directly from infected macrophages and from highly diluted mixes with human RNA. mRNA profiles obtained with SL-seq corresponded largely to those obtained from conventional poly-A tail purification methods, indicating both enumerate the same mRNA pool. However, SL-seq offers additional advantages, including lower sequencing depth requirements, fast and simple library prep and high resolution splice site detection. SL-seq is therefore ideal for fast and massive parallel sequencing of parasite transcriptomes directly from host tissues. Since SLs are also present in Nematodes, Cnidaria and primitive chordates, this method could also have high potential for transcriptomics studies in other organisms.

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

Susceptibility for leishmaniasis is largely dependent on host genetic and immune factors. Despite the previously described association of human leukocyte antigen (HLA) gene cluster variants as genetic susceptibility factors for leishmaniasis, little is known regarding the mechanisms that underpin these associations. To better understand this underlying functionality, we first collected all known leishmaniasis-associated HLA variants in a thorough literature review. Next, we aligned and compared the protection- and risk-associated HLA-DRB1 allele sequences. This identified several amino acid polymorphisms that distinguish protection- from risk-associated HLA-DRB1 alleles. Subsequently, T cell epitope binding predictions were carried out across these alleles to map the impact of these polymorphisms on the epitope binding repertoires. For these predictions, we used epitopes derived from entire proteomes of multiple Leishmania species. Epitopes binding to protection-associated HLA-DRB1 alleles shared common binding core motifs, mapping to the identified HLA-DRB1 amino acid polymorphisms. These results strongly suggest that HLA polymorphism, resulting in differential antigen presentation, affects the association between HLA and leishmaniasis disease development. Finally, we established a valuable open-access resource of putative epitopes. A set of 14 HLA-unrestricted strong-binding epitopes, conserved across species, was prioritized for further epitope discovery in the search for novel subunit-based vaccines.

Cytosine C5 methylation is an important epigenetic control mechanism in a wide array of eukaryotic organisms and generally carried out by proteins of the C-5 DNA methyltransferase family (DNMTs). In several protozoans, the status of this mechanism remains elusive, such as in Leishmania, the causative agent of the disease leishmaniasis in humans and a wide array of vertebrate animals. In this work, we showed that the Leishmania donovani genome contains a C-5 DNA methyltransferase (DNMT) from the DNMT6 subfamily, whose function is still unclear, and verified its expression at the RNA level. We created viable overexpressor and knock-out lines of this enzyme and characterized their genome-wide methylation patterns using whole-genome bisulfite sequencing, together with promastigote and amastigote control lines. Interestingly, despite the DNMT6 presence, we found that methylation levels were equal to or lower than 0.0003% at CpG sites, 0.0005% at CHG sites, and 0.0126% at CHH sites at the genomic scale. As none of the methylated sites were retained after manual verification, we conclude that there is no evidence for DNA methylation in this species. We demonstrated that this difference in DNA methylation between the parasite (no detectable DNA methylation) and the vertebrate host (DNA methylation) allowed enrichment of parasite vs. host DNA using methyl-CpG-binding domain columns, readily available in commercial kits. As such, we depleted methylated DNA from mixes of Leishmania promastigote and amastigote DNA with human DNA, resulting in average Leishmania:human enrichments from 62× up to 263×. These results open a promising avenue for unmethylated DNA enrichment as a pre-enrichment step before sequencing Leishmania clinical samples.

Herniaria hirsuta L. (Caryophyllaceae) is used for treatment of urinary stones and as a diuretic. Little is known about the active compounds and the mechanism of action. The phytochemical composition of H. hirsuta was comprehensively characterized using UHPLC-UV-HRMS (Ultrahigh-Performance Liquid Chromatography-Ultraviolet-High Resolution Mass Spectrometry) data. An in vitro gastrointestinal model was used to simulate biotransformation, which allowed the monitoring of the relative abundances of individual compounds over time. To analyze the longitudinal multiclass LC−MS data, XCMS, a platform that enables online metabolomics data processing and interpretation, and EDGE, a statistical method for time series data, were used to extract significant differential profiles from the raw data. An interactive Shiny app in R was used to rate the quality of the resulting features. These ratings were used to train a random forest model. The most abundant aglycone after gastrointestinal biotransformation was subjected to hepatic biotransformation using human S9 fractions. A diversity of compounds was detected, mainly saponins and flavonoids. Besides the known saponins, 15 new saponins were tentatively identified as glycosides of medicagenic acid, acetylated medicagenic acid and zanhic acid. It is suggested that metabolites of phytochemicals present in H. hirsuta, most likely saponins, are responsible for the pharmaceutical effects. It was observed that the relative abundance of saponin aglycones increased, indicating loss of sugar moieties during colonic biotransformation, with medicagenic acid as the most abundant aglycone. Hepatic biotransformation of this aglycone resulted in different metabolites formed by phase I and II reactions.

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. Meanwhile its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping, thus avoiding the binning step. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using a simulated dataset and two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).

BackgroundAfter transplantation, cell-free DNA derived from the donor organ (ddcfDNA) can be detected in the recipient's circulation. We aimed to quantify ddcfDNA levels in plasma of kidney transplant recipients thereby investigating the kinetics of this biomarker after transplantation and determining biological variables that influence ddcfDNA kinetics in stable and non-stable patients.Materials and methodsFrom 107 kidney transplant recipients, plasma samples were collected longitudinally after transplantation (day 1-3 months) within a multicenter set-up. Cell-free DNA from the donor was quantified in plasma as a fraction of the total cell-free DNA by next generation sequencing using a targeted, multiplex PCR-based method for the analysis of single nucleotide polymorphisms. A subgroup of stable renal transplant recipients was identified to determine a ddcfDNA threshold value.ResultsIn stable transplant recipients, plasma ddcfDNA% decreased to a mean (SD) ddcfDNA% of 0.46% (± 0.21%) which was reached 9.85 (± 5.6) days after transplantation. A ddcfDNA threshold value of 0.88% (mean + 2SD) was determined in kidney transplant recipients. Recipients that did not reach this threshold ddcfDNA value within 10 days after transplantation showed a higher ddcfDNA% on the first day after transplantation and demonstrated a higher individual baseline ddcfDNA%.ConclusionIn conclusion, plasma ddcfDNA fractions decreased exponentially within 10 days after transplantation to a ddcfDNA threshold value of 0.88% or less. To investigate the role of ddcfDNA for rejection monitoring of the graft, future research is needed to determine causes of ddcfDNA% increases above this threshold value.

Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on ‘The Future of Proteomics in ELIXIR’ that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR’s existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.

Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium.

Metabolites represent the most downstream information of the cellular organisation. Hence, metabolomics experiments are extremely valuable to unravel the endogenous pathways involved in a toxicological mode of action. However, every external stimulus can introduce alterations in the cell homeostasis, thereby obscuring the involved endogenous pathways, biasing the interpretation of the results. Here we report on sodium saccharin, which is considered to be not hepatotoxic and therefore can serve as a reference compound to detect metabolic alterations that are not related to liver toxicity. Exposure of HepaRG cells to high levels of sodium saccharin (>10 mM) induced cell death, probably due to an increase in the osmotic pressure. Yet, a low number (n = 15) of significantly altered metabolites were also observed in the lipidome, including a slight decrease in phospholipids and an increase in triacylglycerols, upon daily exposure to 5 mM sodium saccharin for 72 h. The observation that a non-hepatotoxic compound can affect the metabolome underpins the importance of correct experimental design and data interpretation when investigating toxicological modes of action via metabolomics.

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

One of the most significant challenges in the comparative analysis of Nuclear Magnetic Resonance (NMR) metabolome profiles is the occurrence of shifts between peaks across different spectra, for example caused by fluctuations in pH, temperature, instrument factors and ion content. Proper alignment of spectral peaks is therefore often a crucial preprocessing step prior to downstream quantitative analysis. Various alignment methods have been developed specifically for this purpose. Other methods were originally developed to align other data types (GC, LC, SELDI-MS, etc.), but can also be applied to NMR data. This review discusses the available methods, as well as related problems such as reference determination or the evaluation of alignment quality. We present a generic alignment framework that allows for comparison and classification of different alignment approaches according to their algorithmic principles, and we discuss their performance.

ABSTRACT African trypanosomes, except Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense, which cause human African trypanosomiasis, are lysed by the human serum protein apolipoprotein L1 (ApoL1). These two subspecies can resist human ApoL1 because they express the serum resistance proteins T. b. gambiense glycoprotein (TgsGP) and serum resistance-associated protein (SRA), respectively. Whereas in T. b. rhodesiense, SRA is necessary and sufficient to inhibit ApoL1, in T. b. gambiense, TgsGP cannot protect against high ApoL1 uptake, so different additional mechanisms contribute to limit this uptake. Here we report a complex interplay between trypanosomes and an ApoL1 variant, revealing important insights into innate human immunity against these parasites. Using whole-genome sequencing, we characterized an atypical T. b. gambiense infection in a patient in Ghana. We show that the infecting trypanosome has diverged from the classical T. b. gambiense strains and lacks the TgsGP defense mechanism against human serum. By sequencing the ApoL1 gene of the patient and subsequent in vitro mutagenesis experiments, we demonstrate that a homozygous missense substitution (N264K) in the membrane-addressing domain of this ApoL1 variant knocks down the trypanolytic activity, allowing the trypanosome to avoid ApoL1-mediated immunity. IMPORTANCE Most African trypanosomes are lysed by the ApoL1 protein in human serum. Only the subspecies Trypanosoma b. gambiense and T. b. rhodesiense can resist lysis by ApoL1 because they express specific serum resistance proteins. We here report a complex interplay between trypanosomes and an ApoL1 variant characterized by a homozygous missense substitution (N264K) in the domain that we hypothesize interacts with the endolysosomal membranes of trypanosomes. The N264K substitution knocks down the lytic activity of ApoL1 against T. b. gambiense strains lacking the TgsGP defense mechanism and against T. b. rhodesiense if N264K is accompanied by additional substitutions in the SRA-interacting domain. Our data suggest that populations with high frequencies of the homozygous N264K ApoL1 variant may be at increased risk of contracting human African trypanosomiasis.

Abstract Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up‐ and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in‐depth biological interpretation demonstrated the hypothesis‐generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin‐dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant‐specific B‐type CDKs were discovered and the anaphase‐promoting complex was characterized and extended. Important conclusions were that mitotic A‐ and B‐type cyclins form complexes with the plant‐specific B‐type CDKs and not with CDKA;1, and that D‐type cyclins and S‐phase‐specific A‐type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.

We report on the early response of Arabidopsis thaliana to the obligate biotrophic pathogen Plasmodiophora brassicae at the hormone and proteome level. Using a CYCB1;1::GUS construct, the re-initiation of infection-related cell division is shown from 4 days after inoculation on. Sensitivity to cytokinins and auxins as well as the endogenous hormone levels are evaluated. Both an enhanced cytokinin gene response and an accumulation of isopentenyl adenine and adenosine precede this re-initiation of cell division, whereas an enhanced auxin gene response is observed from 6 days after inoculation on. The alh1 mutant, impaired in the cross talk between ethylene and auxins, is resistant to P. brassicae. A differential protein analysis of infected versus noninfected roots and hypocotyls was performed using two-dimensional gel electrophoresis and quantitative image analysis, coupled to matrix-assisted laser desorption ionization time of flight-time of flight mass spectrometry-based protein identification. Of the visualized proteins, 12% show altered abundance compared with the nonin-fected plants, including proteins involved in metabolism, cell defense, cell differentiation, and detoxification. Combining the hormone and proteome data, we postulate that, at the very first stages of Plasmodiophora infection, plasmodial-produced cytokinins trigger a local re-initiation of cell division in the root cortex. Consequently, a de novo meristematic area is established that acts as a sink for host-derived indole-3-acetic acid, carbohydrates, nitrogen, and energy to maintain the pathogen and to trigger gall development.

The structural properties of the DNA molecule are known to play a critical role in transcription. In this paper, the structural profiles of promoter regions were studied within the context of their diversity and their function for eleven prokaryotic species; Escherichia coli, Klebsiella pneumoniae, Salmonella Typhimurium, Pseudomonas auroginosa, Geobacter sulfurreducens Helicobacter pylori, Chlamydophila pneumoniae, Synechocystis sp., Synechoccocus elongates, Bacillus anthracis, and the archaea Sulfolobus solfataricus. The main anchor point for these promoter regions were transcription start sites identified through high-throughput experiments or collected within large curated databases. Prokaryotic promoter regions were found to be less stable and less flexible than the genomic mean across all studied species. However, direct comparison between species revealed differences in their structural profiles that can not solely be explained by the difference in genomic GC content. In addition, comparison with functional data revealed that there are patterns in the promoter structural profiles that can be linked to specific functional loci, such as sigma factor regulation or transcription factor binding. Interestingly, a novel structural element clearly visible near the transcription start site was found in genes associated with essential cellular functions and growth in several species. Our analyses reveals the great diversity in promoter structural profiles both between and within prokaryotic species. We observed relationships between structural diversity and functional features that are interesting prospects for further research to yet uncharacterized functional loci defined by DNA structural properties.

In a recent intervention study, the daily supplementation with 200 mg monomeric and oligomeric flavanols (MOF) from grape seeds for 8 weeks revealed a vascular health benefit in male smokers. The objective of the present study was to determine the impact of MOF consumption on the gene expression profile of leukocytes and to assess changes in DNA methylation.Gene expression profiles were determined using whole genome microarrays (Agilent) and DNA methylation was assessed using HumanMethylation450 BeadChips (Illumina). MOF significantly modulated the expression of 864 genes. The majority of the affected genes are involved in chemotaxis, cell adhesion, cell infiltration or cytoskeleton organisation, suggesting lower immune cell adhesion to endothelial cells. This was corroborated by in vitro experiments showing that MOF exposure of monocytes attenuates their adhesion to TNF-α-stimulated endothelial cells. Nuclear factor kappa B (NF-κB) reporter gene assays confirmed that MOF decrease the activity of NF-κB. Strong inter-individual variability in the leukocytes' DNA methylation was observed. As a consequence, on group level, changes due to MOF supplementation could not be found.Our study revealed that an 8 week daily supplementation with 200 mg MOF modulates the expression of genes associated with cardiovascular disease pathways without major changes of their DNA methylation state. However, strong inter-individual variation in leukocyte DNA methylation may obscure the subtle epigenetic response to dietary flavanols. Despite the lack of significant changes in DNA methylation, the modulation of gene expression appears to contribute to the observed vascular health effect of MOF in humans.

Glucocorticoids (GCs) selectively trigger cell death in the multiple myeloma cell line MM1S which express NR3C1/Glucocorticoid Receptor (GR) protein, but fail to kill MM1R cells which lack GR protein. Given recent demonstrations of altered microRNA profiles in a diverse range of haematological malignancies and drug resistance, we characterized GC inducible mRNA and microRNA transcription profiles in GC sensitive MM1S as compared to GC resistant MM1R cells. Transcriptome analysis revealed that GCs regulate expression of multiple genes involved in cell cycle control, cell organization, cell death and immunological disease in MM1S cells, which remain unaffected in MM1R cells. With respect to microRNAs, mir-150-5p was identified as the most time persistent GC regulated microRNA, out of 5 QPCR validated microRNAs (mir-26b, mir-125a-5p, mir-146-5p, mir-150-5p, and mir-184), which are GC inducible in MM1S but not in MM1R cells. Functional studies further revealed that ectopic transfection of a synthetic mir-150-5p mimics GR dependent gene expression changes involved in cell death and cell proliferation pathways. Remarkably, despite the gene expression changes observed, overexpression of mir-150-5p in absence of GCs did not trigger significant cytotoxicity in MM1S or MM1R cells. This suggests the requirement of additional steps in GC induced cell death, which can not be mimicked by mir-150-5p overexpression alone. Interestingly, a combination of mir-150-5p transfection with low doses GC in MM1S cells was found to sensitize therapy response, whereas opposite effects could be observed with a mir-150-5p specific antagomir. Although mir-150-5p overexpression did not substantially change GR expression levels, it was found that mir-150-5p evokes GR specific effects through indirect mRNA regulation of GR interacting transcription factors and hormone receptors, GR chaperones, as well as various effectors of unfolded protein stress and chemokine signalling. Altogether GC-inducible mir-150-5p adds another level of regulation to GC specific therapeutic responses in multiple myeloma.