Stabilization of the human cytomegalovirus UL136p33 reactivation determinant overcomes the requirement for UL135 for replication in hematopoietic cells.

Human cytomegalovirus (HCMV) is a beta herpesvirus that persists indefinitely in the human host through a latent infection. The polycistronic UL133-UL138 gene locus of HCMV encodes genes regulating latency and reactivation. While UL138 is pro-latency, restricting virus replication in CD34+ hematopoietic progenitor cells (HPCs), UL135 overcomes this restriction and is required for reactivation. By contrast, UL136 is expressed with later kinetics and encodes multiple proteins with differential roles in latency and reactivation. Like UL135, the largest UL136 isoform, UL136p33, is required for reactivation from latency in HPCs; viruses failing to express either protein are unresponsive to reactivation stimuli. Furthermore, UL136p33 is unstable, and its instability is important for the establishment of latency, and sufficient accumulation of UL136p33 is a checkpoint for reactivation. We hypothesized that stabilizing UL136p33 might overcome the requirement of UL135 for replication. We generated recombinant viruses lacking UL135 that expressed a stabilized variant of UL136p33. Stabilizing UL136p33 did not impact the replication of the UL135 mutant virus in fibroblasts. However, in the context of infection in HPCs, stabilization of UL136p33 strikingly compensated for the loss of UL135, resulting in increased replication in CD34+ HPCs and in humanized NOD-scid IL2Rγcnull (huNSG) mice. This finding suggests that while UL135 is essential for replication in HPCs, it functions largely at steps preceding the accumulation of UL136p33, and that stabilized expression of UL136p33 largely overcomes the requirement for UL135. Taken together, our genetic evidence indicates an epistatic relationship between UL136p33 and UL135, whereby UL135 may initiate events early in reactivation that drive the accumulation of UL136p33 to a threshold required for productive reactivation. IMPORTANCE Human cytomegalovirus (HCMV) is one of nine human herpesviruses and a significant human pathogen. While HCMV establishes a lifelong latent infection that is typically asymptomatic in healthy individuals, its reactivation from latency can have devastating consequences in the immunocompromised. Defining viral genes important in the establishment of or reactivation from latency is important to defining the molecular basis of latent and replicative states and in controlling infection and CMV disease. Here we define a genetic relationship between two viral genes in controlling virus reactivation from latency using primary human hematopoietic progenitor cells and humanized mouse models.

Phosphorus and Serendipita indica synergism augments arsenic stress tolerance in rice by regulating secondary metabolism related enzymatic activity and root metabolic patterns.

The multifarious problems created by arsenic (As), for collective environment and human health, serve a cogent case for searching integrative agricultural approaches to attain food security. Rice (Oryza sativa L.) acts as a sponge for heavy metal(loid)s accretion, specifically As, due to anaerobic flooded growth conditions facilitating its uptake. Acclaimed for their positive impact on plant growth, development and phosphorus (P) nutrition, 'mycorrhizas' are able to promote stress tolerance. Albeit, the metabolic alterations underlying Serendipita indica (S. indica; S.i) symbiosis-mediated amelioration of As stress along with nutritional management of P are still understudied. By using biochemical, RT-qPCR and LC-MS/MS based untargeted metabolomics approach, rice roots of ZZY-1 and GD-6 colonized by S. indica, which were later treated with As (10 µM) and P (50 µM), were compared with non-colonized roots under the same treatments with a set of control plants. The responses of secondary metabolism related enzymes, especially polyphenol oxidase (PPO) activities in the foliage of ZZY-1 and GD-6 were enhanced 8.5 and 12-fold, respectively, compared to their respective control counterparts. The current study identified 360 cationic and 287 anionic metabolites in rice roots, and the commonly enriched pathway annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was biosynthesis of phenylalanine, tyrosine and tryptophan, which validated the results of biochemical and gene expression analyses associated with secondary metabolic enzymes. Particularly under As+S.i+P comparison, both genotypes exhibited an upregulation of key detoxification and defense related metabolites, including fumaric acid, L-malic acid, choline, 3,4-dihydroxybenzoic acid, to name a few. The results of this study provided the novel insights into the promising role of exogenous P and S. indica in alleviating As stress.

Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease.

INTRODUCTION: Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. METHODS: Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. RESULTS: Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients. DISCUSSION: These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.

Genome-Resolved Characterization of Structure and Potential Functions of the Zebrafish Stool Microbiome.

Zebrafish have been used as a model organism for more than 50 years and are considered an excellent model for studying host-microbiome interactions. However, this largely depends on our understanding of the zebrafish gut microbiome itself. Despite advances in sequencing and data analysis methods, the zebrafish gut microbiome remains highly understudied. This study performed the de novo metagenome assembly and recovery of the metagenome-assembled genomes (MAGs) through genome binning (and refinement) of the contigs assembled from the zebrafish stool. The results indicate that majority of the MAGs had excellent quality i.e. high completeness (≥90%) and low contamination levels (≤5%). MAGs mainly belong to the taxa that are known to be members of the core zebrafish stool microbiome, including the phylum Proteobacteria, Fusobacteriota, and Actinobacteriota. However, most of the MAGs remained unclassified at the species level and reflected previously unexplored microbial taxa and their potential novelty. These MAGs also contained genes with predicted functions associated with diverse metabolic pathways that included carbohydrate, amino acid, and lipid metabolism pathways. Lastly, we performed a comparative analysis of Paucibacter MAGs and reference genomes that highlighted the presence of novel Paucibacter species and enriched metabolic potential in the recovered MAGs.

Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons.

BACKGROUND: Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions. RESULTS: In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101). CONCLUSION: With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

Characterization of the Gastrointestinal and Reproductive Tract Microbiota in Fertile and Infertile Pakistani Couples.

The human microbiota is recognized as a vital "virtual" organ of the human body that influences human health, metabolism, and physiology. While the microbiomes of the gut, oral cavity, and skin have been extensively studied in the literature, relatively little work has been done on characterizing the microbiota of the human reproductive tract organs, and specifically on investigating its association to fertility. Here, we implemented a 16S ribosomal RNA (rRNA) amplicon sequencing approach to sequence and characterize the gut and genital tract microbiomes from several married Pakistani couples. The recruited individuals included 31 fertile and 35 infertile individuals, with ages ranging from 19-45 years. We identified several fluctuations in the diversity and composition of the gut and genital microbiota among fertile and infertile samples. For example, measures of α-diversity varied significantly between the genital samples donated by fertile and infertile men and there was overall greater between-sample variability in genital samples regardless of gender. In terms of taxonomic composition, Actinobacteria, Bacteroidetes, and Firmicutes fluctuated significantly between the gut microbiomes of fertile and infertile samples. Finally, biomarker analyses identified features (genera and molecular functions and pathways) that differed significantly between the fertile and infertile samples and in the past have been associated with bacterial vaginosis. However, we emphasize that 16S amplicon data alone has no bearing on individual health and is merely representative of microbial taxonomic differences that could also arise due to multiple other factors. Our findings, however, represent the first effort to characterize the microbiome associated with fertile and infertile couples in Pakistan and will hopefully pave the way for more comprehensive and broad-scale investigations in the future.

Surgically confirmed cases of cystic echinococcosis from Baluchistan Province, Pakistan for the years 2011-2018.

INTRODUCTION: Cystic echinococcosis (CE) is a parasitic zoonotic disease caused by the larval stage of Echinococcus granulosus tapeworms. It has a worldwide geographic distribution and can threaten the livestock industry and human health in endemic areas, including Pakistan. CE prevalence is high in Pakistan due to lack of local knowledge about disease transmission and a lack of control measures. The Pakistan province of Baluchistan shares a border with Iran and Afghanistan and is largely agricultural. However, little is known about E. granulosus transmission in this region. METHODS: Information on surgically confirmed cases of CE in Baluchistan Province was obtained through evaluation of paraffin fixed cyst samples and patient records obtained from three local hospitals for the years 2011-2018. RESULTS: A total of 22 paraffin fixed samples were collected during the study period. The majority of cysts were obtained from the liver (9/22; 40.9%), with anatomical location not available for two of the cysts. Demographic information was available for 18 cases. Females made up 61.1% (11/18) of the cases. The largest numbers of cases were found in the 31-40 years age group (5/18; 22.7%). DISCUSSION: This study shows that Echinococcus spp. parasites are circulating in the study area. In order to control the disease, a comprehensive regional surveillance and control program is needed.

Computational operon prediction in whole-genomes and metagenomes.

Microbial diversity in unique environmental settings enables abrupt responses catalysed by altering the gene regulation and formation of gene clusters called operons. Operons increases bacterial adaptability, which in turn increases their survival. This review article presents the emergence of computational operon prediction methods for whole microbial genomes and metagenomes, and discusses their strengths and limitations. Most of the whole-genome operon prediction methods struggle to generalize on unrelated genomes. The applicability of universal whole-genome operon prediction methods to metagenomic data is an interesting yet less investigated question. We have evaluated the potential of various operon prediction features for genomic and metagenomic data. Most of operon prediction methods with high accuracy have been compiled into databases. Despite of the high predictive performance, the data among many databases are not completely consistent for similar species. We performed a correlation analysis between the computationally predicted operon databases and experimentally validated data for Escherichia coli, Bacillus subtilis and Mycobacterium tuberculosis. Operon prediction for most of the less characterized microbes cannot be verified due to absence of experimentally validated operons. The generation of validated information for other microbes would test the authenticity of operon databases for other less annotated microbes as well. Advances in sequencing technologies and development of better analysis methods will help researchers to overcome the technological hurdles (such as long sequencing reads and improved contig size) and further improve operon predictions and better utilize operonic information.