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.

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.

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.

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.

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.

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.

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.

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

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 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.

Identification of the centromeres of : revealing the hidden pieces.

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.

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.

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.

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.

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.

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.

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.