Phylogenetic tree of selected ERG3/ERG25 orthologs.

<p>Seleced orthologs from kinetoplastids, plants, vertebrates, invertebrates and fungi were aligned with t_coffee. A phylogenetic reconstruction was calculated using PhyML (LG model, bootstrap resampling with N = 1000). For clarity, highly similar genes/alleles were not included in the final tree. Organism abbreviations are: lbra  =  <i>L. braziliensis</i>; lmex  =  <i>L. mexicana</i>; linf  =  <i>L. infantum</i>; lmaj  =  <i>L. major</i>; tcru  =  <i>T. cruzi</i>; tviv  =  <i>T. vivax</i>; tcon  =  <i>T. congolense</i>; tbru  =  <i>T. brucei</i>; agam  =  <i>Anopheles gambiae</i>; aaeg  =  <i>Aedes aegypti</i>; cpip  =  <i>Culex pipiens</i>; hsap  =  <i>Homo sapiens</i>; calb  =  <i>Candida albicans</i>; scer  =  <i>S. cerevisiae</i>; spom  =  <i>Schizosaccharomyces pombe</i>; atha  =  <i>A. thaliana</i>; drer  =  <i>Danio rerio</i>; trub  =  <i>Takifugu rubripes</i>; tnig  =  <i>Tetraodon nigroviridis</i>.</p

Comparative analysis of the quantity, density and type of SNPs identified in the sterol biosynthesis pathway of <i>Trypanosoma cruzi</i>.

<p>SNP counts marked with * indicate totals that do not result from the sum of synonymous + non-synonymous substitutions, in some cases because of the presence of two substitutions in the same codon, and in other cases, because some SNPs fall in the non-coding region of the gene.</p

The <i>T. cruzi</i> sterol biosynthesis pathway.

<p>The figure shows the metabolic steps leading from farnesyl-diphosphate to ergosterol (in yeast, and in <i>T. cruzi</i> epimastigotes) or to different 24-alkylsterols (in <i>T. cruzi</i> amastigotes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone.0096762-Liendo1" target="_blank">[64]</a>), and the corresponding yeast and <i>T. cruzi</i> enzymes that catalyze these steps. The two methyl groups at C4 are removed in two rounds of successive C4-oxidation, C4-decarboxylation and C3-ketoreduction (<b>*</b>). Gene names used are those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone-0096762-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone-0096762-t002" target="_blank">2</a>. Unknown/hypothetical assignments are shown with question marks.</p

Alignment of <i>T. cruzi</i>, <i>T. brucei</i>, <i>M. tuberculosis</i> and human Lanosterol 14-<i>α</i> demethylases, showing the non-synonymous changes identified in this work (red arrows).

<p>Important residues either in Tc14DM or in the CYP51 family are noted <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone.0096762-Lepesheva4" target="_blank">[100]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone.0096762-Lepesheva5" target="_blank">[101]</a>, as well as residues associated with resistance to azoles in <i>C. albicans</i> and <i>U. necator </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone.0096762-Kelly1" target="_blank">[56]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone.0096762-Dlye1" target="_blank">[57]</a>. PS00086 is the Prosite Cytochrome 450 motif (cysteine heme-iron ligand signature).</p

The <i>T. cruzi</i> isoprenoid biosynthesis pathway.

<p>The figure shows the metabolic steps leading to farnesyl-diphosphate, from acetyl-CoA, and the corresponding yeast and <i>T. cruzi</i> enzymes that catalyze these steps. Gene names used are those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone-0096762-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone-0096762-t002" target="_blank">2</a>.</p

Genes in the <i>Trypanosoma cruzi</i> sterol biosynthesis pathway.

<p>The table lists <i>T. cruzi</i> genes mapped to either the corresponding KEGG maps, or the yeast SBPs (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096762#pone-0096762-t001" target="_blank">Table 1</a>). OrthoMCL identifiers are from the OrthoMCL Database version 4. <i>T. cruzi</i> gene identifiers are those currently available at the TriTrypDB resource. The fifth column shows whether the gene was mapped to the corresponding KEGG metabolic map at the time of this writing. The last column shows the nomenclature used in this work. Some of these gene names were previously used in the literature, and others are used here for the first time, based on the gene names of relevant orthologs. In the case of the TcIDI gene, the two listed alleles are truncated copies of the gene. The full-length gene is deposited in GenBank as AJ866772. While in the case of the TcNSDHL gene, the copy annotated by the genome project is truncated due to genome assembly problems. The full-length gene is deposited in GenBank as JN050853 (this work).</p

Comparative analysis of the genetic diversity present in the sterol biosynthesis pathway of different kinetoplastids.

<p>The table shows the dN/dS (<i>ω</i>) ratio observed in the african trypanosome lineage (Af Tryps) (<i>T. brucei brucei</i>, <i>T. brucei gambiense</i>, <i>T. evansi</i>, <i>T. congolense</i>, <i>T. vivax</i>), in the <i>Leishmania</i> lineage (Leish) (<i>L. major</i>, <i>L. infantum</i>, <i>L. braziliensis</i>, and <i>L. mexicana</i>), and in the <i>T. cruzi</i> lineage (Tcr, sequences from TcI{TcVI DTUs). The values that are above (†) or under (*) a standard deviation from the average of each column are marked.</p

Changes in the Microbiota from Fresh to Spoiled Meat, Determined by Culture and 16S rRNA Analysis

Growth of meat microbiota usually results in spoilage of meat that can be perceived by consumers due to sensory changes. However, a high bacterial load does not necessarily result in sensory deviation of meat; nevertheless, this meat is considered unfit for human consumption. Therefore, the aims of this study were to investigate changes in the microbiota from fresh to spoiled meat and whether the proportions of certain bacteria can probably be used to indicate the hygiene status of meat. For this purpose, 12 fresh pork samples were divided into two groups, and simultaneously aerobically stored at 4°C and 22°C. At each time-temperature point (fresh meat, days 6, 13, and 20 at 4°C, and days 1, 2, 3, and 6 at 22°C), 12 meat subsamples were investigated. Sequences obtained from next-generation sequencing (NGS) were further analyzed down to species level. Plate counting of six bacterial groups and NGS results showed that Pseudomonas spp. and lactic acid bacteria (LAB) were found in a high proportion in all stored meat samples and can therefore be considered as important “spoilage indicator bacteria”. On the contrary, sequences belonging to Staphylococcus epidermidis were found in a relatively high proportion in almost all fresh meat samples but were less common in stored meat. In this context, they can be considered as “hygiene indicator bacteria” of meat. Based on these findings, the proportion of the “hygiene indicator bacteria” in relation to the “spoilage indicator bacteria” was calculated to determine a “hygiene index” of meat. This index has a moderate to strong correlation to bacterial loads obtained from culture (p < 0.05), specifically to Pseudomonas spp., LAB and total viable counts (TVCs). Knowledge of the proportions of hygiene and spoilage indicator bacteria obtained by NGS could help to determine the hygiene status even of (heat-) processed composite meat products for the first time, thus enhancing food quality assurance and consumer protection

Changes in the microbiota from fresh to spoiled meat, determined by culture and 16S rRNA analysis

Growth of meat microbiota usually results in spoilage of meat that can be perceived by consumers due to sensory changes. However, a high bacterial load does not necessarily result in sensory deviation of meat; nevertheless, this meat is considered unfit for human consumption. Therefore, the aims of this study were to investigate changes in the microbiota from fresh to spoiled meat and whether the proportions of certain bacteria can probably be used to indicate the hygiene status of meat. For this purpose, 12 fresh pork samples were divided into two groups, and simultaneously aerobically stored at 4°C and 22°C. At each time-temperature point (fresh meat, days 6, 13, and 20 at 4°C, and days 1, 2, 3, and 6 at 22°C), 12 meat subsamples were investigated. Sequences obtained from next-generation sequencing (NGS) were further analyzed down to species level. Plate counting of six bacterial groups and NGS results showed that Pseudomonas spp. and lactic acid bacteria (LAB) were found in a high proportion in all stored meat samples and can therefore be considered as important "spoilage indicator bacteria". On the contrary, sequences belonging to Staphylococcus epidermidis were found in a relatively high proportion in almost all fresh meat samples but were less common in stored meat. In this context, they can be considered as "hygiene indicator bacteria" of meat. Based on these findings, the proportion of the "hygiene indicator bacteria" in relation to the "spoilage indicator bacteria" was calculated to determine a "hygiene index" of meat. This index has a moderate to strong correlation to bacterial loads obtained from culture (p < 0.05), specifically to Pseudomonas spp., LAB and total viable counts (TVCs). Knowledge of the proportions of hygiene and spoilage indicator bacteria obtained by NGS could help to determine the hygiene status even of (heat-) processed composite meat products for the first time, thus enhancing food quality assurance and consumer protection

Genome organization and DNA accessibility control antigenic variation in trypanosomes.

Many evolutionarily distant pathogenic organisms have evolved similar survival strategies to evade the immune responses of their hosts. These include antigenic variation, through which an infecting organism prevents clearance by periodically altering the identity of proteins that are visible to the immune system of the host1. Antigenic variation requires large reservoirs of immunologically diverse antigen genes, which are often generated through homologous recombination, as well as mechanisms to ensure the expression of one or very few antigens at any given time. Both homologous recombination and gene expression are affected by three-dimensional genome architecture and local DNA accessibility2,3. Factors that link three-dimensional genome architecture, local chromatin conformation and antigenic variation have, to our knowledge, not yet been identified in any organism. One of the major obstacles to studying the role of genome architecture in antigenic variation has been the highly repetitive nature and heterozygosity of antigen-gene arrays, which has precluded complete genome assembly in many pathogens. Here we report the de novo haplotype-specific assembly and scaffolding of the long antigen-gene arrays of the model protozoan parasite Trypanosoma brucei, using long-read sequencing technology and conserved features of chromosome folding4. Genome-wide chromosome conformation capture (Hi-C) reveals a distinct partitioning of the genome, with antigen-encoding subtelomeric regions that are folded into distinct, highly compact compartments. In addition, we performed a range of analyses—Hi-C, fluorescence in situ hybridization, assays for transposase-accessible chromatin using sequencing and single-cell RNA sequencing—that showed that deletion of the histone variants H3.V and H4.V increases antigen-gene clustering, DNA accessibility across sites of antigen expression and switching of the expressed antigen isoform, via homologous recombination. Our analyses identify histone variants as a molecular link between global genome architecture, local chromatin conformation and antigenic variation