Human papillomavirus (HPV) 58 accounts for a notable proportion of cervical cancers in East Asia and parts of Latin America, but it is uncommon elsewhere. The reason for such ethnogeographical predilection is unknown. In our study, nucleotide sequences of E6 and E7 genes of 401 HPV58 isolates collected from 15 countries/cities across four continents were examined. Phylogenetic relationship, geographical distribution and risk association of nucleotide sequence variations were analyzed. We found that the E6 genes of HPV58 variants were more conserved than E7. Thus, E6 is a more appropriate target for type-specific detection, whereas E7 is more appropriate for strain differentiation. The frequency of sequence variation varied geographically. Africa had significantly more isolates with E6-367A (D86E) but significantly less isolates with E6-203G, -245G, -367C (prototype-like) than other regions (p ≤ 0.003). E7-632T, -760A (T20I, G63S) was more frequently found in Asia, and E7-793G (T74A) was more frequent in Africa (p < 0.001). Variants with T20I and G63S substitutions at E7 conferred a significantly higher risk for cervical intraepithelial neoplasia grade III and invasive cervical cancer compared to other HPV58 variants (odds ratio = 4.44, p = 0.007). In conclusion, T20I and/or G63S substitution(s) at E7 of HPV58 is/are associated with a higher risk for cervical neoplasia. These substitutions are more commonly found in Asia and the Americas, which may account for the higher disease attribution of HPV58 in these areas.

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HPV-58 can be classified into 4 lineages that show some degree of ethnogeographic predilection in distribution. The evolutionary, epidemiological, and pathological characteristics of these lineages warrant further study.

More than 100 HPV types have been described, 13 of which are classified as high-risk due to their association with the development of cervical cancer. The intratype genomic diversity of HPV-16 and -18 has been studied extensively, while little data have been generated for other less common high-risk types. The present study explores the nucleotide variability and phylogeny of the high-risk HPV-31, -33, -35, -52, and -58, in samples from Central Brazil. For this purpose, the LCR and the E6 and L1 genes were sequenced. Several variants of these HPV types were detected, some of which have been detected in other parts of the world. Furthermore, new variants of all types examined were characterized in a total of 13 new variants. Based on the E6 and L1 sequences, variants were described comprising conservative and non-conservative amino acid changes. For phylogenetic tree construction, samples characterized in this study were compared to others described and submitted to GenBank previously. The phylogenetic analysis of HPV-31, -33, -35, and -58 isolates did not reveal ethnic or geographical clustering as observed previously for HPV-16 and -18. HPV-35 analysis showed a dichotomic branching characteristic of viral subtypes. Interestingly, four clusters relative to the analysis of HPV-52 isolates were identified, two of which could be classified as Asian and European branches. The genomic characterization of HPV variants is crucial for understanding the intrinsic geographical relatedness and biological differences of these viruses and contributes further to studies on their infectivity and pathogenicity.

BackgroundTriatomines are hematophagous insects that act as vectors of Chagas disease. Rhodnius neglectus is one of these kissing bugs found, contributing to the transmission of this American trypanosomiasis. The saliva of hematophagous arthropods contains bioactive molecules responsible for counteracting host haemostatic, inflammatory, and immune responses.Methods/Principal FindingsNext generation sequencing and mass spectrometry-based protein identification were performed to investigate the content of triatomine R. neglectus saliva. We deposited 4,230 coding DNA sequences (CDS) in GenBank. A set of 636 CDS of proteins of putative secretory nature was extracted from the assembled reads, 73 of them confirmed by proteomic analysis. The sialome of R. neglectus was characterized and serine protease transcripts detected. The presence of ubiquitous protein families was revealed, including lipocalins, serine protease inhibitors, and antigen-5. Metalloproteases, disintegrins, and odorant binding protein families were less abundant.Conclusions/SignificanceThe data presented improve our understanding of hematophagous arthropod sialomes, and aid in understanding hematophagy and the complex interplay among vectors and their vertebrate hosts.

Background The presence of Plasmodium vivax malaria parasites in the human bone marrow (BM) is still controversial. However, recent data from a clinical case and experimental infections in splenectomized nonhuman primates unequivocally demonstrated the presence of parasites in this tissue. Methods In the current study, we analyzed BM aspirates of 7 patients during the acute attack and 42 days after drug treatment. RNA extracted from CD71+ cell suspensions was used for sequencing and transcriptomic analysis. Results We demonstrated the presence of parasites in all patients during acute infections. To provide further insights, we purified CD71+ BM cells and demonstrated dyserythropoiesis and inefficient erythropoiesis in all patients. In addition, RNA sequencing from 3 patients showed that genes related to erythroid maturation were down-regulated during acute infections, whereas immune response genes were up-regulated. Conclusions This study thus shows that during P. vivax infections, parasites are always present in the BM and that such infections induced dyserythropoiesis and ineffective erythropoiesis. Moreover, infections induce transcriptional changes associated with such altered erythropoietic response, thus highlighting the importance of this hidden niche during natural infections.

A common theme across multiple fungal pathogens is their ability to impair the establishment of a protective immune response. Although early inflammation is beneficial in containing the infection, an uncontrolled inflammatory response is detrimental and may eventually oppose disease eradication. Chromoblastomycosis (CBM), a cutaneous and subcutaneous mycosis, caused by dematiaceous fungi, is capable of inducing a chronic inflammatory response. Muriform cells, the parasitic form of Fonsecaea pedrosoi, are highly prevalent in infected tissues, especially in long-standing lesions. In this study we show that hyphae and muriform cells are able to establish a murine CBM with skin lesions and histopathological aspects similar to that found in humans, with muriform cells being the most persistent fungal form, whereas mice infected with conidia do not reach the chronic phase of the disease. Moreover, in injured tissue the presence of hyphae and especially muriform cells, but not conidia, is correlated with intense production of pro-inflammatory cytokines in vivo. High-throughput RNA sequencing analysis (RNA-Seq) performed at early time points showed a strong up-regulation of genes related to fungal recognition, cell migration, inflammation, apoptosis and phagocytosis in macrophages exposed in vitro to muriform cells, but not conidia. We also demonstrate that only muriform cells required FcγR and Dectin-1 recognition to be internalized in vitro, and this is the main fungal form responsible for the intense inflammatory pattern observed in CBM, clarifying the chronic inflammatory reaction observed in most patients. Furthermore, our findings reveal two different fungal-host interaction strategies according to fungal morphotype, highlighting fungal dimorphism as an important key in understanding the bipolar nature of inflammatory response in fungal infections.

BackgroundIn recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.MethodsThe proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs.ResultsThis method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively.ConclusionsThe SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-4178-4) contains supplementary materi...