Papers by Christina McCarthy
Journal of the Florida Mosquito Control Association, Mar 21, 2024
The incidence of numerous vector-borne diseases (VBDs) has recently increased alarmingly due to v... more The incidence of numerous vector-borne diseases (VBDs) has recently increased alarmingly due to various widespread factors, including unplanned urbanization, greater human mobility, environmental changes, vector resistance to insecticides, and evolving pathogens. In this context, the World Health Organization (WHO) has repositioned effective and sustainable vector control as a key approach to prevent and eliminate VBDs. It has been shown that the microbiome influences development, nutrition, and pathogen defense in disease-transmitting vectors such as mosquitoes, sandflies, tsetse flies, triatomine bugs, and ticks. Consequently, understanding the endogenous regulation of vector biology can aid in developing effective approaches for vector control. In this respect, a metatranscriptomic approach analyzes all the expressed RNAs in an environmental sample (meta-RNAs) and can thus reveal how the metabolic activities of the microbiome influence vector biology. This review includes an extensive analysis of available literature on microbial and viral studies for some of the major hematophagous disease-transmitting arthropods, with a focus on studies that used next generation sequencing (NGS) approaches. Since a consensus terminology for these "metasequencing analyses" has not yet been established, a definition of these terms is presented here to provide the framework for systematically sorting the available information for each of the VBDs analyzed here to single out metatranscriptomic analyses. Finally, key gaps in knowledge were identified for some of these hematophagous disease-transmitting arthropods which will prove very useful for driving future studies.
Table S3. Statistical features of 3904 bicodons in the human genome. The first column corresponds... more Table S3. Statistical features of 3904 bicodons in the human genome. The first column corresponds to the amino acid pair (∗ symbolizes stop codon), the second column to the corresponding bicodon, the third and fourth columns list the number of occurrences of bicodons in the low and high PA samples, respectively. The fifth and sixth columns correspond to the residual scores χ L 2 $\chi _{L}^{2}$ and χ H 2 $\chi _{H}^{2}$ computed over the low and high PA samples, respectively. The last two columns correspond to the signed log[p-value] and the pause propensity values of each bicodon, respectively. (XLS 587 kb)
Figure S2. Pause propensity heat map. The color of each cell is determined by the pause propensit... more Figure S2. Pause propensity heat map. The color of each cell is determined by the pause propensity Ď of the associated bicodons. Columns represent codons corresponding to P-site, while rows represent codons corresponding to A-site, so that each cell in the heat map represents a bicodon. Red cells indicate bicodons with the highest pause propensity value (low PA preference), while blue cells indicate bicodons with the lowest pause propensity value (high PA preference). Rows and columns have been clustered to improve visualization. (PDF 260 kb)
Table S2. Silent sSNPs. First and second columns correspond to benign sSNPs and their associated ... more Table S2. Silent sSNPs. First and second columns correspond to benign sSNPs and their associated genes respectively. Each SNP affects only one codon (third SNP row), but two bicodons, when the mutation is in the P-site (first SNP row), and when the mutation is in the A-site (second SNP row), but only one codon (third SNP row). Third, fourth, fifth and sixth columns, refer to the starting bicodon, list the bicodon sequence, the p-value, the total residual score ( χ 2 = χ L 2 + χ H 2 $\chi ^{2}=\chi _{L}^{2}+ \chi _{H}^{2}$ ) and π value, respectively. The following four columns, refer to the resulting bicodon, list the bicodon sequence, the log-transformed p-value, the χ 2 score value and π value, respectively. The last three columns enumerate the relative p-value, the pause propensity variation, Δ π, and the corresponding Z-score due to the synonymous bicodon variant. (XLS 66 kb)
Table S1. Genetic diseases and sSNPs. The first column corresponds to the disease or genetic trai... more Table S1. Genetic diseases and sSNPs. The first column corresponds to the disease or genetic trait. The second and third columns correspond to the associated gene and SNP (where both can be more than one), respectively. Each SNP affects only one codon (third SNP row), but two bicodons, when the mutation is in the P-site (first SNP row), and when the mutation is in the A-site (second SNP row). Fourth, fifth, sixth and seventh columns, refer to the starting bicodon, list the bicodon sequence, the p-value, the residual score ( χ 2 = χ L 2 + χ H 2 $\chi ^{2}=\chi _{L}^{2}+ \chi _{H}^{2}$ ) and π value, respectively. The following four columns, refer to the resulting bicodon, list the bicodon sequence, the log-transformed p-value, the residual score and π value, respectively. The following three columns enumerate the relative p-value, the pause propensity variation, Δ π, and the corresponding Z-score due to the synonymous bicodon variant. The last two columns contain a summarized result ...
Text. Additional details for each disease or clinical trait listed in Table 1, such as basic back... more Text. Additional details for each disease or clinical trait listed in Table 1, such as basic background, references, accession number and local sequence context analysis. (PDF 88 kb)
Figure S1. Z-score function associated with the differential RSCU change of all the synonymous co... more Figure S1. Z-score function associated with the differential RSCU change of all the synonymous codon variants. The red region indicates the highest 10% pause propensity variation, i.e., those sSNPs with a variation larger than 0.28. (PDF 13 kb)
<p>This figure integrates data from the function categories identified in these wild EVL an... more <p>This figure integrates data from the function categories identified in these wild EVL and NEVL sand flies with sampling site characteristics and taxa previously found in these same samples <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058645#pone.0058645-McCarthy1" target="_blank">[22]</a>. Figures are only schematic and not an exact representation of either the sampling sites, phlebotomine sand flies or identified taxa. A) Shows the function categories that the transcripts were assigned to in all the samples and the number of transcripts assigned to each function category for each sample. Values are expressed on a logarithmic scale and indicated for each sample on the corresponding bar. Significant differences in the number of transcripts in each category between samples (Fisher's Exact Test; p<0.05) are indicated as: a, significantly overrepresented with respect to PP1; b, significantly overrepresented with respect to PP2; c, significantly overrepresented with respect to SS1; and d, significantly overrepresented with respect to SS2. B) The top part shows a schematic of the sandflies (female or male) from both locations and of the taxa we previously identified in all four samples <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058645#pone.0058645-McCarthy1" target="_blank">[22]</a>. Barrels group the taxa found in each sample. Previously identified taxa in SS1: bacteria, protists, metazoans (human) and plants; SS2: protists, metazoans (human and chicken) and plants; PP1: bacteria, fungi, metazoans (human, chicken and lizard) and plants; and PP2: bacteria, fungi, metazoans (human) and plants. Taxa are represented schematically and the particular species identified for each taxonomical group are not shown, except in the case of metazoans. The bottom part shows the most significant ecological characteristics of both capture site locations in Argentina and Brazil, Posadas and Lapinha Cave, respectively. Only those animal species confirmed in the sampling sites in both locations at the time of sampling were represented schematically. EVL sampling site (Posadas, Argentina): human, dog, cat and chicken; NEVL sampling site (Lapinha Cave, Brazil): human and chicken. SS1 (indicated in yellow): EVL adult female <i>Lu. longipalpis</i> (Posadas, Argentina); SS2 (indicated in orange): EVL adult male <i>Lu. longipalpis</i> (Posadas, Argentina); PP1 (indicated in green): NEVL adult female <i>Lu. longipalpis</i> (Lapinha Cave, Brazil); PP2 (indicated in pale blue): NEVL adult male <i>Lu. longipalpis</i> (Lapinha Cave, Brazil).</p
Journal of Applied Entomology
Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their... more Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their small size is generally an issue in terms of yield, efficiency, and purity, for large-scale individual sand fly DNA extractions when using traditional methods. Even though this can be circumvented with commercial kits, these are generally cost-prohibitive for developing countries. We encountered these limitations when analysing parasite infection in Lutzomyia spp. by PCR [1] and, for this reason, we evaluated various modifications on a previously published protocol ([2] and Acardi personal communication). The most significant variation was the use of a different lysis buffer [3] to which added Ca2+ (buffer TESCa), because this ion protects proteinase K against autolysis, increases its thermal stability, and could have a regulatory function for its substrate-binding site [4]. Individual sand fly DNA extraction success was confirmed by amplification reactions using internal control primer...
Data generated by metagenomic and metatranscriptomic experiments is both enormous and inherently ... more Data generated by metagenomic and metatranscriptomic experiments is both enormous and inherently noisy [1]. When using taxonomy-dependent alignment-based methods to classify and label reads, such as MEGAN [2], the first step consists in performing homology searches against sequence databases. To obtain the most information from the samples, nucleotide sequences are usually compared to various databases (i.e., nucleotide and protein) using local sequence aligners such as BLASTN and BLASTX [3]. Nevertheless, the analysis and integration of these results can be problematic because the outputs from these searches usually show differences, which can be notorious when working with RNA-seq (Personal observation; Graphical abstract). These inconsistencies led us to develop the HoSeIn workflow to determine the unequivocal taxonomic and functional profile of environmental samples, based on the assumption that the sequences that correspond to a certain taxon are composed of (Graphical abstract...
BMC Genomics
Background: For a long time synonymous single nucleotide polymorphisms were considered as silent ... more Background: For a long time synonymous single nucleotide polymorphisms were considered as silent mutations. However, nowadays it is well known that they can affect protein conformation and function, leading to altered disease susceptibilities, differential prognosis and/or drug responses, among other clinically relevant genetic traits. This occurs through different mechanisms: by disrupting the splicing signals of precursor mRNAs, affecting regulatory binding-sites of transcription factors and miRNAs, or by modifying the secondary structure of mRNAs. Results: In this paper we considered 22 human genetic diseases or traits, linked to 35 synonymous single nucleotide polymorphisms in 27 different genes. We performed a local sequence context analysis in terms of the ribosomal pause propensity affected by synonymous single nucleotide polymorphisms. We found that synonymous mutations related to the above mentioned mechanisms presented small pause propensity changes, whereas synonymous mutations that were not related to those mechanisms presented large pause propensity changes. On the other hand, we did not observe large variations in the codon usage of codons associated with these mutations. Furthermore, we showed that the changes in the pause propensity associated with benign sSNPs are significantly lower than the pause propensity changes related to sSNPs associated to diseases. Conclusions: These results suggest that the genetic diseases or traits related to synonymous mutations with large pause propensity changes, could be the consequence of another mechanism underlying non-silent synonymous mutations. Namely, alternative protein configuration related, in turn, to alterations in the ribosome-mediated translational attenuation program encoded by pairs of consecutive codons, not codons. These findings shed light on the latter mechanism based on the perturbation of the co-translational folding process.
Revista Mexicana De Biodiversidad, Aug 16, 2012
Methods and Protocols
Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their... more Numerous protocols have been published for extracting DNA from phlebotomines. Nevertheless, their small size is generally an issue in terms of yield, efficiency, and purity, for large-scale individual sand fly DNA extractions when using traditional methods. Even though this can be circumvented with commercial kits, these are generally cost-prohibitive for developing countries. We encountered these limitations when analyzing field-collected Lutzomyia spp. by polymerase chain reaction (PCR) and, for this reason, we evaluated various modifications on a previously published protocol, the most significant of which was a different lysis buffer that contained Ca2+ (buffer TESCa). This ion protects proteinase K against autolysis, increases its thermal stability, and could have a regulatory function for its substrate-binding site. Individual sand fly DNA extraction success was confirmed by amplification reactions using internal control primers that amplify a fragment of the cacophony gene. T...
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Papers by Christina McCarthy