Metabolic Profiling Early Post-Allogeneic Haematopoietic Cell Transplantation in the Context of CMV Infection.

Immune dysfunction resulting from allogeneic haematopoietic stem cell transplantation (aHSCT) predisposes one to an elevated risk of cytomegalovirus (CMV) infection. Changes in metabolism have been associated with adverse outcomes, and in this study, we explored the associations between metabolic profiles and post-transplantation CMV infection using plasma samples collected 7-33 days after aHSCT. We included 68 aHSCT recipients from Rigshospitalet, Denmark, 50% of whom experienced CMV infection between days 34-100 post-transplantation. First, we investigated whether 12 metabolites selected based on the literature were associated with an increased risk of post-transplantation CMV infection. Second, we conducted an exploratory network-based analysis of the complete metabolic and lipidomic profiles in relation to clinical phenotypes and biological pathways. Lower levels of trimethylamine N-oxide were associated with subsequent CMV infection (multivariable logistic regression: OR = 0.63; 95% CI = [0.41; 0.87]; p = 0.01). Explorative analysis revealed 12 clusters of metabolites or lipids, among which one was predictive of CMV infection, and the others were associated with conditioning regimens, age upon aHSCT, CMV serostatus, and/or sex. Our results provide evidence for an association between the metabolome and CMV infection post-aHSCT that is independent of known risk factors.

Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients.

BACKGROUND: At least a third of dengue patients develop plasma leakage with increased risk of life-threatening complications. Predicting plasma leakage using laboratory parameters obtained in early infection as means of triaging patients for hospital admission is important for resource-limited settings. METHODS: A Sri Lankan cohort including 4,768 instances of clinical data from N = 877 patients (60.3% patients with confirmed dengue infection) recorded in the first 96 hours of fever was considered. After excluding incomplete instances, the dataset was randomly split into a development and a test set with 374 (70%) and 172 (30%) patients, respectively. From the development set, five most informative features were selected using the minimum description length (MDL) algorithm. Random forest and light gradient boosting machine (LightGBM) were used to develop a classification model using the development set based on nested cross validation. An ensemble of the learners via average stacking was used as the final model to predict plasma leakage. RESULTS: Lymphocyte count, haemoglobin, haematocrit, age, and aspartate aminotransferase were the most informative features to predict plasma leakage. The final model achieved the area under the receiver operating characteristics curve, AUC = 0.80 with positive predictive value, PPV = 76.9%, negative predictive value, NPV = 72.5%, specificity = 87.9%, and sensitivity = 54.8% on the test set. CONCLUSION: The early predictors of plasma leakage identified in this study are similar to those identified in several prior studies that used non-machine learning based methods. However, our observations strengthen the evidence base for these predictors by showing their relevance even when individual data points, missing data and non-linear associations were considered. Testing the model on different populations using these low-cost observations would identify further strengths and limitations of the presented model.

Pre-Transplant Prediction of Acute Graft-versus-Host Disease Using the Gut Microbiome.

Gut microbiota is thought to influence host responses to allogeneic hematopoietic stem cell transplantation (aHSCT). Recent evidence points to this post-transplant for acute graft-versus-host disease (aGvHD). We asked whether any such association might be found pre-transplant and conducted a metagenome-wide association study (MWAS) to explore. Microbial abundance profiles were estimated using ensembles of Kaiju, Kraken2, and DeepMicrobes calls followed by dimensionality reduction. The area under the curve (AUC) was used to evaluate classification of the samples (aGvHD vs. none) using an elastic net to test the relevance of metagenomic data. Clinical data included the underlying disease (leukemia vs. other hematological malignancies), recipient age, and sex. Among 172 aHSCT patients of whom 42 developed aGVHD post transplantation, a total of 181 pre-transplant tool samples were analyzed. The top performing model predicting risk of aGVHD included a reduced species profile (AUC = 0.672). Beta diversity (37% in Jaccard's Nestedness by mean fold change, p < 0.05) was lower in those developing aGvHD. Ten bacterial species including Prevotella and Eggerthella genera were consistently found to associate with aGvHD in indicator species analysis, as well as relief and impurity-based algorithms. The findings support the hypothesis on potential associations between gut microbiota and aGvHD based on a data-driven approach to MWAS. This highlights the need and relevance of routine stool collection for the discovery of novel biomarkers.

Metabolic Potential of the Gut Microbiome Is Significantly Impacted by Conditioning Regimen in Allogeneic Hematopoietic Stem Cell Transplantation Recipients.

Allogeneic hematopoietic stem cell transplantation (aHSCT) is a putative curative treatment for malignant hematologic disorders. During transplantation, the immune system is suppressed/eradicated through a conditioning regimen (non-myeloablative or myeloablative) and replaced with a donor immune system. In our previous study, we showed changes in gut taxonomic profiles and a decrease in bacterial diversity post-transplant. In this study, we expand the cohort with 114 patients and focus on the impact of the conditioning regimens on taxonomic features and the metabolic functions of the gut bacteria. This is, to our knowledge, the first study to examine the metabolic potential of the gut microbiome in this patient group. Adult aHSCT recipients with shotgun sequenced stool samples collected day -30 to +28 relative to aHSCT were included. One sample was selected per patient per period: pre-aHSCT (day -30-0) and post-aHSCT (day 1-28). In total, 254 patients and 365 samples were included. Species richness, alpha diversity, gene richness and metabolic richness were all lower post-aHSCT than pre-aHSCT and the decline was more pronounced for the myeloablative group. The myeloablative group showed a decline in 36 genera and an increase in 15 genera. For the non-myeloablative group, 30 genera decreased and 16 increased with lower fold changes than observed in the myeloablative group. For the myeloablative group, 32 bacterial metabolic functions decreased, and one function increased. For the non-myeloablative group, three functions decreased, and two functions increased. Hence, the changes in taxonomy post-aHSCT caused a profound decline in bacterial metabolic functions especially in the myeloablative group, thus providing new evidence for associations of myeloablative conditioning and gut dysbiosis from a functional perspective.

Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning.

Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.

Machine learning can identify newly diagnosed patients with CLL at high risk of infection.

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors.

Association Between Single-Nucleotide Polymorphisms in HLA Alleles and Human Immunodeficiency Virus Type 1 Viral Load in Demographically Diverse, Antiretroviral Therapy-Naive Participants From the Strategic Timing of AntiRetroviral Treatment Trial.

The impact of variation in host genetics on replication of human immunodeficiency virus type 1 (HIV-1) in demographically diverse populations remains uncertain. In the current study, we performed a genome-wide screen for associations of single-nucleotide polymorphisms (SNPs) to viral load (VL) in antiretroviral therapy-naive participants (n = 2440) with varying demographics from the Strategic Timing of AntiRetroviral Treatment (START) trial. Associations were assessed using genotypic data generated by a customized SNP array, imputed HLA alleles, and multiple linear regression. Genome-wide significant associations between SNPs and VL were observed in the major histocompatibility complex class I region (MHC I), with effect sizes ranging between 0.14 and 0.39 log10 VL (copies/mL). Supporting the SNP findings, we identified several HLA alleles significantly associated with VL, extending prior observations that the (MHC I) is a major host determinant of HIV-1 control with shared genetic variants across diverse populations and underscoring the limitations of genome-wide association studies as being merely a screening tool.

Clipped histone H3 is integrated into nucleosomes of DNA replication genes in the human malaria parasite Plasmodium falciparum.

Post-translational modifications of histone H3 N-terminal tails are key epigenetic regulators of virulence gene expression and sexual commitment in the human malaria parasite Plasmodium falciparum Here, we identify proteolytic clipping of the N-terminal tail of nucleosome-associated histone H3 at amino acid position 21 as a new chromatin modification. A cathepsin C-like proteolytic clipping activity is observed in nuclear parasite extracts. Notably, an ectopically expressed version of clipped histone H3, PfH3p-HA, is targeted to the nucleus and integrates into mononucleosomes. Furthermore, chromatin immunoprecipitation and next-generation sequencing analysis identified PfH3p-HA as being highly enriched in the upstream region of six genes that play a key role in DNA replication and repair: In these genes, PfH3p-HA demarcates a specific 1.5 kb chromatin island adjacent to the open reading frame. Our results indicate that, in P. falciparum, the process of histone clipping may precede chromatin integration hinting at preferential targeting of pre-assembled PfH3p-containing nucleosomes to specific genomic regions. The discovery of a protease-directed mode of chromatin organization in P. falciparum opens up new avenues to develop new anti-malarials.

Human thymopoiesis is influenced by a common genetic variant within the TCRA-TCRD locus.

The thymus is the primary lymphoid organ where naïve T cells are generated; however, with the exception of age, the parameters that govern its function in healthy humans remain unknown. We characterized the variability of thymic function among 1000 age- and sex-stratified healthy adults of the Milieu Intérieur cohort, using quantification of T cell receptor excision circles (TRECs) in peripheral blood T cells as a surrogate marker of thymopoiesis. Age and sex were the only nonheritable factors identified that affect thymic function. TREC amounts decreased with age and were higher in women compared to men. In addition, a genome-wide association study revealed a common variant (rs2204985) within the T cell receptor TCRA-TCRD locus, between the DD2 and DD3 gene segments, which associated with TREC amounts. Strikingly, transplantation of human hematopoietic stem cells with the rs2204985 GG genotype into immunodeficient mice led to thymopoiesis with higher TRECs, increased thymocyte counts, and a higher TCR repertoire diversity. Our population immunology approach revealed a genetic locus that influences thymopoiesis in healthy adults, with potentially broad implications in precision medicine.

Identification of the centromeres of Leishmania major: revealing the hidden pieces.

Leishmania affects millions of people worldwide. Its genome undergoes constitutive mosaic aneuploidy, a type of genomic plasticity that may serve as an adaptive strategy to survive distinct host environments. We previously found high rates of asymmetric chromosome allotments during mitosis that lead to the generation of such ploidy. However, the underlying molecular events remain elusive. Centromeres and kinetochores most likely play a key role in this process, yet their identification has failed using classical methods. Our analysis of the unconventional kinetochore complex recently discovered in Trypanosoma brucei (KKTs) leads to the identification of a Leishmania KKT gene candidate (LmKKT1). The GFP-tagged LmKKT1 displays "kinetochore-like" dynamics of intranuclear localization throughout the cell cycle. By ChIP-Seq assay, one major peak per chromosome is revealed, covering a region of 4 ±2 kb. We find two largely conserved motifs mapping to 14 of 36 chromosomes while a higher density of retroposons are observed in 27 of 36 centromeres. The identification of centromeres and of a kinetochore component of Leishmania chromosomes opens avenues to explore their role in mosaic aneuploidy.

Nutrient sensing modulates malaria parasite virulence.

The lifestyle of intracellular pathogens, such as malaria parasites, is intimately connected to that of their host, primarily for nutrient supply. Nutrients act not only as primary sources of energy but also as regulators of gene expression, metabolism and growth, through various signalling networks that enable cells to sense and adapt to varying environmental conditions. Canonical nutrient-sensing pathways are presumed to be absent from the causative agent of malaria, Plasmodium, thus raising the question of whether these parasites can sense and cope with fluctuations in host nutrient levels. Here we show that Plasmodium blood-stage parasites actively respond to host dietary calorie alterations through rearrangement of their transcriptome accompanied by substantial adjustment of their multiplication rate. A kinome analysis combined with chemical and genetic approaches identified KIN as a critical regulator that mediates sensing of nutrients and controls a transcriptional response to the host nutritional status. KIN shares homology with SNF1/AMPKα, and yeast complementation studies suggest that it is part of a functionally conserved cellular energy-sensing pathway. Overall, these findings reveal a key parasite nutrient-sensing mechanism that is critical for modulating parasite replication and virulence.

The PfAlba1 RNA-binding protein is an important regulator of translational timing in Plasmodium falciparum blood stages.

BACKGROUND: Transcriptome-wide ribosome occupancy studies have suggested that during the intra-erythrocytic lifecycle of Plasmodium falciparum, select mRNAs are post-transcriptionally regulated. A subset of these encodes parasite virulence factors required for invading host erythrocytes, and are currently being developed as vaccine candidates. However, the molecular mechanisms that govern post-transcriptional regulation are currently unknown. RESULTS: We explore the previously identified DNA/RNA-binding protein PfAlba1, which localizes to multiple foci in the cytoplasm of P. falciparum trophozoites. We establish that PfAlba1 is essential for asexual proliferation, and subsequently investigate parasites overexpressing epitope-tagged PfAlba1 to identify its RNA targets and effects on mRNA homeostasis and translational regulation. Using deep sequencing of affinity-purified PfAlba1-associated RNAs, we identify 1193 transcripts that directly bind to PfAlba1 in trophozoites. For 105 such transcripts, 43 % of which are uncharacterized and 13 % of which encode erythrocyte invasion components, the steady state levels significantly change at this stage, evidencing a role for PfAlba1 in maintaining mRNA homeostasis. Additionally, we discover that binding of PfAlba1 to four erythrocyte invasion mRNAs, Rap1, RhopH3, CDPK1, and AMA1, is linked to translation repression in trophozoites whereas release of these mRNAs from a PfAlba1 complex in mature stages correlates with protein synthesis. CONCLUSIONS: We show that PfAlba1 binds to a sub-population of asexual stage mRNAs and fine-tunes the timing of translation. This mode of post-transcriptional regulation may be especially important for P. falciparum erythrocyte invasion components that have to be assembled into apical secretory organelles in a highly time-dependent manner towards the end of the parasite's asexual lifecycle.

First efficient CRISPR-Cas9-mediated genome editing in Leishmania parasites.

Protozoan pathogens that cause leishmaniasis in humans are relatively refractory to genetic manipulation. In this work, we implemented the CRISPR-Cas9 system in Leishmania parasites and demonstrated its efficient use for genome editing. The Cas9 endonuclease was expressed under the control of the Dihydrofolate Reductase-Thymidylate Synthase (DHFR-TS) promoter and the single guide RNA was produced under the control of the U6snRNA promoter and terminator. As a proof of concept, we chose to knockout a tandemly repeated gene family, the paraflagellar rod-2 locus. We were able to obtain null mutants in a single round of transfection. In addition, we confirmed the absence of off-target editions by whole genome sequencing of two independent clones. Our work demonstrates that CRISPR-Cas9-mediated gene knockout represents a major improvement in comparison with existing methods. Beyond gene knockout, this genome editing tool opens avenues for a multitude of functional studies to speed up research on leishmaniasis.

Genome editing in the human malaria parasite Plasmodium falciparum using the CRISPR-Cas9 system.

Genome manipulation in the malaria parasite Plasmodium falciparum remains largely intractable and improved genomic tools are needed to further understand pathogenesis and drug resistance. We demonstrated the CRISPR-Cas9 system for use in P. falciparum by disrupting chromosomal loci and generating marker-free, single-nucleotide substitutions with high efficiency. Additionally, an artemisinin-resistant strain was generated by introducing a previously implicated polymorphism, thus illustrating the value of efficient genome editing in malaria research.

Identification of estrogen responsive genes using esophageal squamous cell carcinoma (ESCC) as a model.

BACKGROUND: Estrogen therapy has positively impact the treatment of several cancers, such as prostate, lung and breast cancers. Moreover, several groups have reported the importance of estrogen induced gene regulation in esophageal cancer (EC). This suggests that there could be a potential for estrogen therapy for EC. The efficient design of estrogen therapies requires as complete as possible list of genes responsive to estrogen. Our study develops a systems biology methodology using esophageal squamous cell carcinoma (ESCC) as a model to identify estrogen responsive genes. These genes, on the other hand, could be affected by estrogen therapy in ESCC. RESULTS: Based on different sources of information we identified 418 genes implicated in ESCC. Putative estrogen responsive elements (EREs) mapped to the promoter region of the ESCC genes were used to initially identify candidate estrogen responsive genes. EREs mapped to the promoter sequence of 30.62% (128/418) of ESCC genes of which 43.75% (56/128) are known to be estrogen responsive, while 56.25% (72/128) are new candidate estrogen responsive genes. EREs did not map to 290 ESCC genes. Of these 290 genes, 50.34% (146/290) are known to be estrogen responsive. By analyzing transcription factor binding sites (TFBSs) in the promoters of the 202 (56+146) known estrogen responsive ESCC genes under study, we found that their regulatory potential may be characterized by 44 significantly over-represented co-localized TFBSs (cTFBSs). We were able to map these cTFBSs to promoters of 32 of the 72 new candidate estrogen responsive ESCC genes, thereby increasing confidence that these 32 ESCC genes are responsive to estrogen since their promoters contain both: a/mapped EREs, and b/at least four cTFBSs characteristic of ESCC genes that are responsive to estrogen. Recent publications confirm that 47% (15/32) of these 32 predicted genes are indeed responsive to estrogen. CONCLUSION: To the best of our knowledge our study is the first to use a cancer disease model as the framework to identify hormone responsive genes. Although we used ESCC as the disease model and estrogen as the hormone, the methodology can be extended analogously to other diseases as the model and other hormones. We believe that our results provide useful information for those interested in genes responsive to hormones and in the design of hormone-based therapies.

The response and recovery of the Arabidopsis thaliana transcriptome to phosphate starvation.

BACKGROUND: Over application of phosphate fertilizers in modern agriculture contaminates waterways and disrupts natural ecosystems. Nevertheless, this is a common practice among farmers, especially in developing countries as abundant fertilizers are believed to boost crop yields. The study of plant phosphate metabolism and its underlying genetic pathways is key to discovering methods of efficient fertilizer usage. The work presented here describes a genome-wide resource on the molecular dynamics underpinning the response and recovery in roots and shoots of Arabidopsis thaliana to phosphate-starvation. RESULTS: Genome-wide profiling by micro- and tiling-arrays (accessible from GEO: GSE34004) revealed minimal overlap between root and shoot transcriptomes suggesting two independent phosphate-starvation regulons. Novel gene expression patterns were detected for over 1000 candidates and were classified as either initial, persistent, or latent responders. Comparative analysis to AtGenExpress identified cohorts of genes co-regulated across multiple stimuli. The hormone ABA displayed a dominant role in regulating many phosphate-responsive candidates. Analysis of co-regulation enabled the determination of specific versus generic members of closely related gene families with respect to phosphate-starvation. Thus, among others, we showed that PHR1-regulated members of closely related phosphate-responsive families (PHT1;1, PHT1;7-9, SPX1-3, and PHO1;H1) display greater specificity to phosphate-starvation than their more generic counterparts. CONCLUSION: Our results uncover much larger, staged responses to phosphate-starvation than previously described. To our knowledge, this work describes the most complete genome-wide data on plant nutrient stress to-date.

An atlas of combinatorial transcriptional regulation in mouse and man.

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.