Comparative genomics of Giardia duodenalis sub-assemblage AI beaver (Be-2) and human (WB-C6) strains show remarkable homozygosity, sequence similarity, and conservation of VSP genes.

Giardia duodenalis, a major cause of waterborne infection, infects a wide range of mammalian hosts and is subdivided into eight genetically well-defined assemblages named A through H. However, fragmented genomes and a lack of comparative analysis within and between the assemblages render unclear the molecular mechanisms controlling host specificity and differential disease outcomes. To address this, we generated a near-complete de novo genome of AI assemblage using the Oxford Nanopore platform by sequencing the Be-2 genome. We generated 148,144 long-reads with quality scores of > 7. The final genome assembly consists of only nine contigs with an N50 of 3,045,186 bp. This assembly agrees closely with the assembly of another strain in the AI assemblage (WB-C6). However, a critical difference is that a region previously placed in the five-prime region of Chr5 belongs to Chr4 of Be-2. We find a high degree of conservation in the ploidy, homozygosity, and the presence of cysteine-rich variant-specific surface proteins (VSPs) within the AI assemblage. Our assembly provides a nearly complete genome of a member of the AI assemblage of G. duodenalis, aiding population genomic studies capable of elucidating Giardia transmission, host range, and pathogenicity.

Global selective sweep of a highly inbred genome of the cattle parasite Neospora caninum.

Neospora caninum, a cyst-forming apicomplexan parasite, is a leading cause of neuromuscular diseases in dogs as well as fetal abortion in cattle worldwide. The importance of the domestic and sylvatic life cycles of Neospora, and the role of vertical transmission in the expansion and transmission of infection in cattle, is not sufficiently understood. To elucidate the population genomics of Neospora, we genotyped 50 isolates collected worldwide from a wide range of hosts using 19 linked and unlinked genetic markers. Phylogenetic analysis and genetic distance indices resolved a single genotype of N. caninum Whole-genome sequencing of 7 isolates from 2 different continents identified high linkage disequilibrium, significant structural variation, but only limited polymorphism genome-wide, with only 5,766 biallelic single nucleotide polymorphisms (SNPs) total. Greater than half of these SNPs (∼3,000) clustered into 6 distinct haploblocks and each block possessed limited allelic diversity (with only 4 to 6 haplotypes resolved at each cluster). Importantly, the alleles at each haploblock had independently segregated across the strains sequenced, supporting a unisexual expansion model that is mosaic at 6 genomic blocks. Integrating seroprevalence data from African cattle, our data support a global selective sweep of a highly inbred livestock pathogen that originated within European dairy stock and expanded transcontinentally via unisexual mating and vertical transmission very recently, likely the result of human activities, including recurrent migration, domestication, and breed development of bovid and canid hosts within similar proximities.

Tilting the balance between RNA interference and replication eradicates Leishmania RNA virus 1 and mitigates the inflammatory response.

Many Leishmania (Viannia) parasites harbor the double-stranded RNA virus Leishmania RNA virus 1 (LRV1), which has been associated with increased disease severity in animal models and humans and with drug treatment failures in humans. Remarkably, LRV1 survives in the presence of an active RNAi pathway, which in many organisms controls RNA viruses. We found significant levels (0.4 to 2.5%) of small RNAs derived from LRV1 in both Leishmania braziliensis and Leishmania guyanensis, mapping across both strands and with properties consistent with Dicer-mediated cleavage of the dsRNA genome. LRV1 lacks cis- or trans-acting RNAi inhibitory activities, suggesting that virus retention must be maintained by a balance between RNAi activity and LRV1 replication. To tilt this balance toward elimination, we targeted LRV1 using long-hairpin/stem-loop constructs similar to those effective against chromosomal genes. LRV1 was completely eliminated, at high efficiency, accompanied by a massive overproduction of LRV1-specific siRNAs, representing as much as 87% of the total. For both L. braziliensis and L. guyanensis, RNAi-derived LRV1-negative lines were no longer able to induce a Toll-like receptor 3-dependent hyperinflammatory cytokine response in infected macrophages. We demonstrate in vitro a role for LRV1 in virulence of L. braziliensis, the Leishmania species responsible for the vast majority of mucocutaneous leishmaniasis cases. These findings establish a targeted method for elimination of LRV1, and potentially of other Leishmania viruses, which will facilitate mechanistic dissection of the role of LRV1-mediated virulence. Moreover, our data establish a third paradigm for RNAi-viral relationships in evolution: one of balance rather than elimination.

A unified framework for finding differentially expressed genes from microarray experiments.

BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.