MOLECULAR STANDARDS FOR MICROBIAL PATHOGENS

Abstract

A method for constructing a consensus sequence from a sequence alignment. The consensus sequence may be used to generate molecular standards that may substitute for genomic DNA in various assays. Since a molecular standard cannot have unresolved bases, the method removes less informative sequences to resolve all positions in the alignment. Also includes several sequences from pathogenic waterborne species that were constructed according to the method.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and is a continuation of U.S. patent application Ser. No. 12/870,481; filed Aug. 27, 2010 and claims the benefit of U.S. Provisional Patent Application No. 61/237,933, filed on Aug. 28, 2009, the disclosures of which are expressly incorporated by reference herein in their entireties.

BACKGROUND

[0003] 1. Field of the Present Disclosure

[0004] The present disclosure provides a library of synthetic standard molecules for multiple species of microbial pathogens, including Cryptosporidium, Giardia, and microsporidia. Each of these standard molecules includes a bacterial plasmid molecule containing a specific DNA sequence insert that represents a consensus sequence of the 18s rRNA gene for a single species of interest. These standard molecules may be used by, for example, researchers, utility operators, and clinical laboratory technicians as a surrogate for native genomic DNA in a variety of situations.

[0005] 2. Related Art

[0006] Due to technological limitations, environmental and clinical laboratories are increasingly moving away from microscopic methods and towards molecular detection methods. Molecular methods typically use the polymerase chain reaction (PCR) to detect a specific DNA sequence in the genome of a target organism. Compared to microscopic methods, molecular methods offer increased speed, sensitivity, and reproducibility. Molecular methods can also provide supplementary data unattainable using microscopy, such as, for example, genotype identification.

[0007] One microbial pathogen of particular interest is Cryptosporidium. Fifteen waterborne Cryptosporidium outbreaks were reported in the United States between 1991 and 2002, affecting over 408,000 individuals. This makes Cryptosporidium the highest cause of waterborne disease by number of affected individuals. The most significant outbreak occurred in Milwaukee, Wis. in 1993. This well-studied case affected over 403,000 individuals and cost the region an estimated $96.2 million. This event, plus several major recreational outbreaks since then, underscores the importance of proper water monitoring.

[0008] Giardia contamination can cause outbreaks that result in similar disruptions.

[0009] Microsporidia, including Encephalitozoon intestinalis and Enterocytozoon bieneusi, cause microspridiosis, which is an opportunistic infection that can cause diarrhea and wasting in immunocompromised patients.

[0010] Unfortunately, the introduction of new molecular tools for targets such as, e.g., Cryptosporidium and Giardia has been restricted by the lack of standardized positive controls. Positive controls, typically purified genomic DNA from pathogens of interest, may have multiple roles in the development and validation of a molecular method. Two of the most important roles include:

[0011] As a sensitivity control, determining detection limits and quantifying target DNA; and

[0012] As a specificity control, resolving target genotypes.

[0013] It can be extremely challenging to obtain positive controls for microbial pathogens of environmental interest. Many such organisms are difficult to culture in vitro. The distribution of others is regulated by the Centers for Disease Control and/or the United States Department of Commerce. Researchers who wish to develop new tests for these pathogens must often perform their own isolations from clinical or environmental samples or obtain specimens from collaborators. These research stocks are often subject to inconsistent quality control, increase the risk of laboratory-associated infections, and are of insufficient quantity for industrial-scale development and validation. Until an alternative source can be developed, the limited availability of positive controls threatens to prevent the introduction of any new molecular tests for microbial pathogens into the market.

[0014] In the case of Cryptosporidium, limited amounts of positive control DNA are available from a handful of Biological Resource Centers (BRCs). In particular, Waterborne supplies purified C. parvum and C. muris oocysts, while AMERICAN TYPE CULTURE COLLECTION® (ATCC) can regularly provide researchers with genomic DNA from C. parvum (Iowa strain). A third commercial source, the Biodefense and Emerging Infections Research Resources Repository (BEI Resources), supplies genomic DNA and other reagents only to NIH-funded investigators. These supplies are insufficient for widespread method development, especially for assays aimed at distinguishing multiple genotypes. As a result, test development has been fragmented as research groups rely on various organism stocks of inconsistent quality.

[0015] Accordingly, there exists a pressing need for standardized positive controls for Cryptosporidium, Giardia, microsporidia, and other microbial pathogens that may be used to develop and validate molecular detection and genotyping methods.

SUMMARY OF THE PRESENT DISCLOSURE

[0016] The present disclosure meets the foregoing need and allows detection of pathogenic species using molecular methods, which results in a significant improvement in speed, sensitivity, and reproducibility and other advantages apparent from the discussion herein.

[0017] Accordingly, in one aspect of the present disclosure, a method is described for constructing a consensus sequence from an alignment of two or more nucleic acid sequences. The method includes iterating over each position in the alignment and taking the following actions at each position: (1) calculate the base frequencies and determine the base with the highest frequency; (2) if the frequency of the most common base with the highest frequency is greater than a specified frequency threshold, then the base is assigned to that position in the consensus sequence; and (3) if the frequency of the most common base is below the frequency threshold, then the base corresponding to the nucleic acid sequence with the lowest information score is removed and the process repeats from action (1).

[0018] The method may include generating a frequency matrix, which includes the frequency of each base at each position in the alignment; creating an information matrix, which includes the amount of information provided by each base at each position in the alignment; and calculating an information score for each nucleic acid sequence. As part of creating an information matrix, the method may calculate the decrease in Shannon uncertainty for each base at each position in the alignment. As part of calculating an information score, the method may sum the decreases in Shannon uncertainty for each base in each sequence. Insertions and Deletions may be removed from the multiple sequence alignment. The frequency threshold for actions (2) and (3) may be 0.7.

[0019] The method may be used to construct a consensus sequence. A restriction fragment length polymorphism (RFLP) fingerprint of the constructed consensus sequence may be compared to RFLP fingerprints of one or more of the nucleic acid sequences in the multiple sequence alignment. Binding of oligonucleotides to the consensus sequence and to sequences in the multiple sequence alignment may be compared on the basis of Gibb's free energy of hybridization, melting temperature of the heterodimer, and binding position. The consensus sequence may be used to synthesize a DNA construct or molecular standard. The DNA construct may be linear, or it may be circular, e.g., a plasmid.

[0020] According to another aspect of the present disclosure, a multiple sequence alignment, which includes a number of alignment positions, is used to construct a consensus sequence. As part of this method, a frequency matrix, which includes the frequency of each base at each alignment position, is generated. An information matrix, which includes the amount of information provided by each base at each alignment position, is also generated, and an information score is calculated for each sequence in the multiple sequence alignment. The method iterates over the alignment and at each alignment position, does the following: (1) determining which base at the alignment position has the highest frequency; (2) if the frequency of the highest frequency base is above a threshold value, the base is assigned to the consensus sequence; and (3) if the frequency of the highest frequency base is below the threshold, the base corresponding to the sequence with the lowest information score is removed, base frequencies are recalculated, and the procedure returns to action (1).

[0021] As part of creating an information matrix, the method may calculate the decrease in Shannon uncertainty for each base at each position in the alignment. As part of calculating an information score, the method may sum the decreases in Shannon uncertainty for each base in each sequence. Insertions and Deletions may be removed from the multiple sequence alignment. The frequency threshold for actions (2) and (3) may be 0.7.

[0022] The method may be used to construct a consensus sequence. A restriction fragment length polymorphism (RFLP) fingerprint of the constructed consensus sequence may be compared to RFLP fingerprints of one or more of the nucleic acid sequences in the multiple sequence alignment. Binding of oligonucleotides to the consensus sequence and to sequences in the multiple sequence alignment may be compared on the basis of Gibb's free energy of hybridization, melting temperature of the heterodimer, and binding position. The consensus sequence may be used to synthesize a DNA construct or molecular standard. The DNA construct may be linear, or it may be circular, e.g., a plasmid.

[0023] According to yet another aspect of the present disclosure, 18S rRNA consensus sequences are disclosed for Cryptosporidium andersoni, as shown in SEQ ID NO:1; Cryptosporidium baileyi, as shown in SEQ ID NO:2; Cryptosporidium bovis, as shown in SEQ ID NO:3; Cryptosporidium canis, as shown in SEQ ID NO:4; Cryptosporidium felis, as shown in SEQ ID NO:5; Cryptosporidium hominis, as shown in SEQ ID NO:6; Cryptosporidium meleagridis, as shown in SEQ ID NO:7; Cryptosporidium muris, as shown in SEQ ID NO:8; Cryptosporidium parvum, as shown in SEQ ID NO:9; Cryptosporidium serpentis, as shown in SEQ ID NO:10; Cryptosporidium wrairi, as shown in SEQ ID NO:11; Giardia intestinalis, as shown in SEQ ID NO:12; Encephalitozoon intestinalis, as shown in SEQ ID NO:13; and Enterocytozoon bieneusi, as shown in SEQ ID NO:14.

[0024] Additional features, advantages, and embodiments of the present disclosure may be set forth or apparent from consideration of the following detailed description, drawings, and claims. Moreover, it is to be understood that both the foregoing summary of the present disclosure and the following detailed description are exemplary and intended to provide further explanation without limiting the scope of the present disclosure as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which are included to provide a further understanding of the present disclosure, are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and together with the detailed description serve to explain the principles of the present disclosure. No attempt is made to show structural details of the present disclosure in more detail than may be necessary for a fundamental understanding of the present disclosure and the various ways in which it may be practiced. In the drawings:

[0026] FIG. 1 shows standard curves of C. hominis genomic and synthetic target DNA in real-time PCR assays;

[0027] FIG. 2 shows standard curves of C. meleagridis genomic and synthetic target DNA in real-time PCR assays;

[0028] FIG. 3 shows standard curves of C. parvum genomic and synthetic target DNA in real-time PCR assays;

[0029] FIG. 4 shows standard curves of C. muris genomic and synthetic target DNA in real-time PCR assays;

[0030] FIG. 5 shows standard curves of G. intestinalis genomic and synthetic target DNA in real-time PCR assays; and

[0031] FIG. 6 shows standard curves of C. felis synthetic target DNA in real-time PCR assays.

DETAILED DESCRIPTION

[0032] It is understood that the present disclosure is not limited to the particular methodology, protocols, and reagents, etc., described herein, as these may vary as the skilled artisan will recognize. It is also to be understood that the terminology used herein is used for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present disclosure. It also is be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include the plural reference unless the context clearly dictates otherwise. Thus, for example, a reference to "a capsule" is a reference to one or more capsules and equivalents thereof known to those skilled in the art.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which the present disclosure pertains. The embodiments of the present disclosure and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments and/or illustrated in the accompanying drawings and detailed in the following description. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale, and features of one embodiment may be employed with other embodiments as the skilled artisan would recognize, even if not explicitly stated herein.

[0034] Any numerical values recited herein include all values from the lower value to the upper value in increments of one unit provided that there is a separation of at least two units between any lower value and any higher value. As an example, if it is stated that the concentration of a component or value of a process variable such as, for example, size, temperature, pressure, time and the like, is, for example, from 1 to 90, specifically from 20 to 80, more specifically from 30 to 70, it is intended that values such as 15 to 85, 22 to 68, 43 to 51, 30 to 32 etc., are expressly enumerated in this specification. For values which are less than one, one unit is considered to be 0.0001, 0.001, 0.01 or 0.1 as appropriate. These are only examples of what is specifically intended and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application in a similar manner.

[0035] Moreover, provided immediately below is a "Definition" section, where certain terms related to the present disclosure are defined specifically. Particular methods, devices, and materials are described, although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure. All references referred to herein are incorporated by reference herein in their entirety.

1. DEFINITIONS

[0036] The terms "alignment" and "sequence alignment" as used herein refer to arrangement of two or more nucleic acid sequences that may be used to identify regions of similarity between the sequences. If the sequences are displayed horizontally, then the individual bases from different sequences are arranged in vertical columns, which may be referred to as "alignment positions".

[0037] The term "base" as used herein refers to a single monomer of a nucleic acid.

[0038] The term "base frequency" as used herein refers to the frequency with which a given base appears in a particular grouping of bases, such as a nucleic acid sequence or an alignment position.

[0039] The term "consensus sequence" as used herein refers to a representation of a sequence alignment that

[0040] The term "Cryptosporidium" as used herein by itself, not followed by a species name, means any species of Cryptosporidium which is known to cause disease, including, for example, C. parvum, C. felis, C. muris, C. meleagridis, C. suis, C. canis, and/or C. hominis.

[0041] The term "DNA construct" as used herein refers to an artificially constructed segment of nucleic acid.

[0042] The term "Giardia" as used herein by itself, not followed by a species name, means any species of Giardia which is known to cause disease. This may include, for example, G. lamblia, G. duodenalis, and/or G. intestinalis.

[0043] The term "microsporidia" as used herein refers to any species of microsporidia which is known to cause disease, including, e.g., E. intestinalis and/or E. bieneusi.

[0044] The term "nucleic acid," as used herein, may include an oligonucleotide, nucleotide, or polynucleotide, and fragments thereof. The term may refer to DNA or RNA of genomic or synthetic origin, which may be single- or double-stranded and may represent the sense or antisense strand. Additionally, the term may refer to peptide nucleic acid (PNA), to small interfering RNA (siRNA) molecule, or to any DNA-like or RNA-like material, natural or synthetic in origin.

[0045] The term "nucleic acid sequence" as used herein refers to the specific order of monomers in a nucleic acid molecule that includes two or more monomers.

[0046] The term "PCR" as used herein means the polymerase chain reaction, as is well-known in the art. The term includes all forms of PCR, such as, e.g., real-time PCR and quantitative PCR.

[0047] The term "plasmid" as used herein refers to a circular nucleic acid molecule that is separate from a cell's chromosome(s) and may replicate independently of the chromosome(s).

[0048] The terms "restriction fragment length polymorphism" and "RFLP" as used herein refer to a difference between two or more nucleic acid samples. Differences in sequence between the samples result in different endonuclease restriction (cutting) sites, which in turn produce fragments of different length after digestion by a particular endonuclease. The particular pattern of fragments that a sample produces may be referred to as a "RFLP fingerprint."

2. DESCRIPTION

[0049] Molecular methods are increasingly used for the detection of pathogens, due to superiority of these methods over traditional microscopic methods. Molecular tools for many pathogenic species, however, may be unavailable because the organisms are difficult to culture in vitro, resulting in a lack of standardized positive controls. Consequently, researchers often rely on their own isolations, which can vary dramatically in quality. Development of molecular methods is fragmented as different groups rely on organism stocks of inconsistent quality. Thus, there is need for standardized positive controls for these organisms, such as, e.g., Cryptosporidium, Giardia, and microsporidia, that can be used to develop and validate molecular detection and genotyping methods.

[0050] One promising solution may be found in the advancing field of synthetic biology. Synthetic biology involves using engineering tools to generate biological components de novo from DNA sequences. Much of this work relies on recent improvements in chemical DNA synthesis by third-party manufacturers.

[0051] One aspect of the present disclosure is directed to a robust workflow for designing synthetic positive controls. This workflow has been employed to produce consensus sequences for Cryptosporidium hominis, C. meleagridis, C. felis, C. parvum, C. muris, C. andersoni, C. baileyi, C. bovis, C. canis, C. serpentis, C. wrairi, Giardia intestinalis, Encephalitozoon intestinalis, and Enterocytozoon bieneusi. Additionally, molecular standards have been produced and tested for C. felis, C. parvum, C. muris, C. hominis, C. meleagridis, and G. intestinalis. Each of these molecular standards may include a bacterial plasmid molecule containing a synthetically-produced DNA insert, the sequence of which may represent the 18s rRNA gene of a single Cryptosporidium, Giardia, or microsporidia species. These molecular standards may be used as a surrogate for native genomic DNA in a variety of situations.

[0052] This approach has a number of advantages over traditional sources of positive control DNA, including, for example, the following:

[0053] The efficiency of chemical DNA synthesis allows rapid prototyping and validation.

[0054] Synthetic standards are extremely stable, extending storage life and ensuring high-quality analytical results.

[0055] Synthetic standards can be designed to allow use by multiple research teams to develop and validate their molecular assays.

[0056] The design and production of synthetic standards can be subjected to precise quality control.

[0057] Synthetic standards present a lower risk of laboratory contamination than live organism cultures, allowing easier distribution and use by academic, commercial, and educational groups.

[0058] The design workflow described in this disclosure includes six parts, including:

[0059] 1. Identify relevant reference sequences for the target gene.

[0060] 2. Align the reference sequences using one of several existing software applications.

[0061] 3. Reduce the multiple sequence alignment into a single consensus sequence using a novel algorithm.

[0062] 4. Computationally verify that the consensus sequence exhibits the same properties as one or more reference sequences.

[0063] 5. Synthesize the consensus sequence and incorporate it into a bacterial plasmid to create a molecular standard.

[0064] 6. Optimize PCR performance of the molecular standard.

[0065] Each of these parts will now be described in detail.

[0066] Part 1: Identify Relevant Reference Sequences.

[0067] For most pathogens of interest, there is a general consensus in the scientific literature as to which gene represents the most appropriate target for molecular assays. In the case of Cryptosporidium, Giardia, and microsporidia, this gene is 18s rRNA. However, alternative genes may be used in these or other species without departing from the spirit or scope of the present disclosure. The selected gene or genes may or may not include additional flanking sequences. The first step in the design workflow is to collect as many 18s rRNA reference sequences for the species of interest as possible. Many of these are available on public databases, such as, for example, GENBANK®. However, other sequences may be obtained as part of a private sequencing effort.

[0068] Some reference sequences may be more valuable than others. For example, sequences that were obtained many years ago may reflect incorrect naming conventions or include inaccurate information. These distinctions should be made to ensure that only the most accurate reference sequences are used to design the molecular standards. Ideally, at the end of Part 1, a list of reference sequences should have been determined. The list should represent the efforts of multiple sequencing groups, at different geographic locations, analyzing multiple strains or isolates.

[0069] Part 2: Align Reference Sequences Using One of Several Multiple Sequence Alignment Applications.

[0070] In order for the reference sequences to be useful, they should first be aligned to each other. Although each sequence may be from the same species, they all will likely be from different isolates or sub-species. Each of these sub-species may have subtle differences in the sequence of their 18s rRNA gene. Also, due to variability in sequencing equipment and protocols, each reference sequence may likely be of a unique length. Long sequences may be likely to contain many different patterns, while smaller sequences may be likely to contain only a subset of these patterns. By aligning these sequences, it is possible to identify regions of similarity between some or all of the sequences.

[0071] For example, an unaligned set of four sequences may be represented as:

TABLE-US-00001 SEQ ID NO: 15 ACTGGTAGCTAGCCTGGATCGATCGGGTGTAGTACTGA SEQ ID NO: 16 TAGCCTGGATCCATCG SEQ ID NO: 17 TATTACTGA SEQ ID NO: 18 TAGGTAGCCTGGATC

[0072] The alignment of these four sequences may be represented as:

TABLE-US-00002 SEQ ID NO: 19 ACTGGTAGCTAGCCTGGATCGATCGGGTGTAGTACTGA SEQ ID NO: 20 ---------TAGCCTGGATCCATCG------------- SEQ ID NO: 21 -----------------------------TATTACTGA SEQ ID NO: 22 -----TAGGTAGCCTGGATC------------------

[0073] Many third-party software applications exist to perform multiple sequence alignments, including, for example, MUSCLE, MAFFT, MACAW, T-Coffee, and CLUSTAL. Alignments used in the development of the present disclosure were obtained using CLUSTAL. The output of CLUSTAL included a text file containing a multiple sequence alignment of all reference sequences identified during Part 1.

[0074] Part 3: Reduce the Multiple Sequence Alignment to a Single Consensus Sequence.

[0075] The multiple sequence alignment obtained during Part 2 may represent all of the various sequences a researcher might obtain if he or she sequenced the 18s rRNA gene of the Cryptosporidium, Giardia, or microsporidia species of interest from a particular source. However, it may not be possible to determine which of the reference sequences may be the most representative of the researcher's particular isolate. Therefore, it may not be possible to simply select an arbitrary reference sequence and use it as the basis for a molecular standard because the arbitrarily selected sequence may not bear enough similarities to the researcher's isolate.

[0076] Instead, a single consensus sequence may be generated from the multiple sequence alignment. This consensus sequence may not be identical to any of the reference sequences. However, it may be closely similar to all of them. In this way, it may be possible to ensure that the molecular standard may be useable by all researchers working with the species of interest, regardless of their particular isolate.

[0077] The consensus sequence determination may be made using a multi-part computational algorithm:

[0078] Part 3a: Remove Base Pair Inserts and Deletions.

[0079] During the alignment of the DNA sequences as outlined in Part 2, the alignment software may discover an insert (an unnecessary base) or a deletion (a missing base) in one or more sequences. These inserts and deletions should be removed before further analysis. For example, consider a sequence alignments represented as:

TABLE-US-00003 SEQ ID NO: 23 TATCAACAT_CCTTCCTATTATATTTCT SEQ ID NO: 24 TATCAACAT_CCTTCCTATTATAT_TCT SEQ ID NO: 25 TATCAACATTCCTTCCTATTATATTTCT SEQ ID NO: 26 TATCGACAT_CCTTCCTATTATATATCT

[0080] At position 10 of the example alignment, sequence 3 has a base insert that does not exist in any other sequence. In a method according to the present disclosure, this inserted base may be removed. Sequence 2 has a deletion that occurs at position 25. Due to the existence of a larger number of bases at that position, these bases may not be removed.

[0081] Part 3b: Create Frequency Matrix.

[0082] A frequency matrix may be generated, containing the frequency of each base at each alignment position. For example, consider 5 reference sequences aligned in the following way:

TABLE-US-00004 Position # 1 2 3 Sequence 1 A C T Sequence 2 A C A Sequence 3 T G C Sequence 4 A G C Sequence 5 A T G

[0083] In this case, the frequency matrix would be:

TABLE-US-00005 Position # 1 2 3 f(A): 0.8 0 0.2 f(T): 0.2 0.2 0.2 f(C): 0 0.4 0.4 f(G): 0 0.4 0.2

where f(A) is the frequency of Adenine (A) at that alignment position, f(T) the frequency of Thymine (T), etc.

[0084] Part 3c: Create Information Matrix.

[0085] Using the frequency matrix, an information matrix may be created containing the amount of "information" provided by a given base i at each alignment position j. In this case, information may be defined as the decrease in Shannon uncertainty, calculated as:

I_i,j=2+log₂(p_i,j)

where p_i,j is the frequency of base i at alignment position j.

[0086] In the instant example, the following information matrix may be obtained:

TABLE-US-00006 Position # 1 2 3 f(A): 0.8 0 0.2 f(T): 0.2 0.2 0.2 f(C): 0 0.4 0.4 f(G): 0 0.4 0.2 I(A): 1.68 ∞ -0.32 I(T): -0.32 -0.32 -0.32 I(C): ∞ 0.68 0.68 I(G): ∞ 0.68 -0.32

[0087] Part 3d: Determine an Information Score for Each Reference Sequence.

[0088] The information matrix determined in Part 3c describes the amount of information provided by a given nucleotide at each alignment position. By summing these information contributions along the entire length of a given sequence, an information score may be determined. This score may represent the total amount of information encoded into the sequence. Sequences with high scores may contain many bases that are shared with other aligned sequences at that position. Sequences with low scores may be regarded as "unusual", containing low-frequency bases at many alignment positions.

[0089] In the instant example, Sequence 1 may be scored in the following fashion:

TABLE-US-00007 Sequence 1 A C T I_A: 1.68 ∞ -0.32 I_T: -0.32 0.32 -0.32 I_C: ∞ 0.68 0.68 I_G: ∞ 0.68 -0.32

Thus, I_sequence 1=1.68+0.68+(-0.32)=2.04 bits of information

[0090] Since the aligned sequences may be of different lengths, the information content of each sequence may be normalized by the number of by it contains:

_sequence 1=2.04/3=0.68 bits/by

By applying this logic to the entire multiple sequence alignment, it may be possible to determine which of the reference sequences are most relevant to the consensus sequence.

[0091] The concept of sequence information has been used by others to identify sequence motifs, specific sequences conserved across many genomes that may indicate undiscovered genes, protein-binding sites, or other biochemical or structural information.

[0092] Part 3e: Determine Consensus Sequence.

[0093] Once information scores have been generated for each reference sequence, the multiple sequence alignment may be reduced to a single consensus sequence. There are many ways to determine a consensus sequence, as known by those having ordinary skill in the relevant art. For instance, a popular way is to select the most frequent base at each position in an alignment. The literature describes a method for determining a consensus sequence where the most frequent base is selected for each position if its frequency is ≧0.875. However, if the frequency is less than that, the consensus base is left undefined for that position. Undefined bases, however, are not suitable for synthesis and incorporation into a molecular standard because there must be a base at every position in the sequence. Thus, the present disclosure uses a novel process.

[0094] To determine a consensus sequence, analysis may begin at the first alignment position and move toward the last alignment position. At each alignment position, the following decisions may be made, including:

[0095] i. Is the highest frequency of any base greater than 0.7? If yes, assign that base to the consensus sequence and move to the next position. If no, go to step ii.

[0096] ii. Look at the alignment of bases at that position. Which one comes from the reference sequence with the lowest T score? Remove it from consideration and go to step iii.

[0097] iii. After one instance of a base has been removed, recalculate the base frequencies. Go back to step i. A method for determining a consensus sequence may use the most common base at a given position, even if that base is only slightly more common than the others, e.g. a frequency of 0.26. The cutoff frequency of 0.7 may be selected to balance the consideration given to less common bases. A lower cutoff may give such outliers too much weight while a higher cutoff may give them too little weight.

[0098] Part 4: Computationally Verify the Consensus Sequence.

[0099] Prior to synthesis, computational tools may be used to predict whether the consensus sequence will behave similarly to one or more of the reference sequences.

[0100] Part 4a: In Silico RFLP Digest.

[0101] Restriction Fragment Length Polymorphism (RFLP) analysis is a common molecular biology technique for identifying differences between multiple DNA samples. During RFLP analysis, one or more restriction endonucleases may be used to digest the DNA samples of interest. A restriction endonuclease may include an enzyme that cuts DNA at specific recognition sites. For example, the EcoRI enzyme may cut a double-stranded DNA recognition sequence in the following fashion (`|` indicates a cut point):

TABLE-US-00008 5' G|AATT C 3' 3' C TTAA|G 5'

After digestion, the fragments of each DNA sample may be separated by size using, for example, gel electrophoresis, producing a "fingerprint" that can be used to identify small sequence differences between the DNA samples. During in silico RFLP digest, a sequence may be computationally searched for a set of known restriction endonuclease recognition sites, and the number of bases in between the sites may be counted. With this information, a model RFLP fingerprint may be created for that sequence. Ideally, the consensus sequence and reference sequences should have the same number of RFLP fragments. Also, the corresponding fragments for each sequence should be approximately the same length. If this is not the case, the consensus sequence may need to be redesigned.

[0102] Part 4b: Primer/Probe Binding Simulation.

[0103] To determine if the consensus sequence is a good surrogate for the reference sequences, the behavior of these sequences in previously-published molecular assays may be simulated. Most molecular assays rely upon the use of short pieces of DNA referred to as oligonucleotides. Oligonucleotides may function as PCR primers, guiding the amplification of certain genetic regions. Oligonucleotides may also function as probes, binding to specific regions of the target DNA and emitting a signal of some sort. In both cases, assay performance may be dependent upon the oligonucleotides binding tightly to the target DNA in the correct location.

[0104] During computational verification, the way that the oligonucleotides described in previously-published molecular assays bind to the consensus and reference sequences may be simulated. A software tool, such as, for example, VisualOMP, may be used to perform these simulations, although other such tools exist. Ideally, each oligonucleotide should bind to the consensus and reference sequences with the same strength (indicated by the Gibb's free energy of hybridization and melting temperature of the heterodimer) and binding position. If this is not the case, the consensus sequence may need to be redesigned.

[0105] Part 5. Synthesize the Molecular Standard.

[0106] Once a consensus sequence has been identified and computationally verified, it may be synthesized and incorporated into a molecular standard. The resulting molecular standard may include a circular DNA plasmid containing the consensus sequence as an insert. Other types of constructs are contemplated and within the scope of the present disclosure. The use of a plasmid carrier molecular provides stability to the molecular standard, extending shelf life and allowing use in a variety of molecular assays. Creation of the molecular standard may be accomplished by any means known to those skilled in the art, including ordering the standard from a third party manufacturer. One such third party manufacturer is Blue Heron Biotechnology, although there are others.

[0107] Part 6. Optimize the PCR Performance of the Molecular Standard.

[0108] Even though a molecular standard may share an identical insert sequence as a sample of native genomic DNA, the two templates may not behave identically during molecular analysis. Due to the small size of each plasmid, a vial of molecular standard may contain many more copies of the insert per mass of DNA than the genomic material. Genomic material may also contain many orders more non-target DNA than the molecular standards. For these reasons, molecular standards may produce much clearer results than native genomic DNA. In the case of PCR-based assays, this means that the genomic DNA may amplify with lower efficiency. This performance bias may be unacceptable if the molecular standards are to serve as surrogates for native genomic material.

[0109] To ensure that the molecular standards demonstrate PCR efficiency similar to native genomic material, it may be necessary to introduce PCR inhibitors to the standard solutions. Many such inhibitors exist, such as, e.g., non-target DNA, humic acids, polysaccharides, bile salts, immunoglobin G, heme, CTAB, SDS, alcohol, sodium acetate, sodium chloride, EDTA, collagen, melanin, eumelanin, myoglobin, proteinases, calcium ions, urea, lactoferrin, indigo dye, and the like. By introducing one or more of these substances into solution with the molecular standards, the PCR efficiency of the standards may be optimized to match that of native genomic DNA. This process of using PCR inhibitors to adjust the amplification efficiency of the molecular standards may be a key component of guaranteeing their utility.

[0110] The process described above results in standardized positive controls for microbial pathogens that may be used to develop and validate molecular detection and genotyping methods. Historically, most efforts in this area have focused on developing PCR inhibition positive controls, also known as internal controls. These typically involve generating either a linear or plasmid DNA template containing a short (<1,000 bp) insert, usually a PCR amplicon, flanked by specific primer recognition sites. These molecules are then spiked into analytical reactions to distinguish "no target" samples (internal control will amplify) from false negatives (internal control will not amplify, typically due to matrix inhibition). This approach has been used in assays for various organisms, including HSV 1 and 2, avian influenza, pestiviruses, Agrobacterium tumefaciens, and others.

[0111] Due to small insert size and targeted design, traditional internal controls, such as those listed above, share the same limitation: they are necessarily assay-specific and cannot be used with alternative primer or probe sets. Therefore, research groups developing new detection or genotyping assays must also generate their own internal controls from scratch. In contrast, the molecular standards disclosed herein involve significant portions of target genes, if not the entire sequence. In this way, any test developer targeting a selected gene can use the molecular standards, regardless of their assay's specific primer or probe sequences.

[0112] Molecular standards developed according to the disclosure may be used as surrogates for native genomic DNA in a number of applications for various users. Molecular assay developers may use the molecular standards to determine the suitability of PCR primers and probes during development, to determine limit-of-detection specifications during development, or to verify the species-specificity of PCR primers and probes during development. End-users of a molecular assay may use the molecular standards to generate standard curves when quantifying the amount of target in a sample during qPCR, as a positive control to determine if a particular PCR reaction mixture was created and pipetted correctly, as a spike-in control to determine the amount of PCR inhibition present in a sample matrix, as a spike-in control to determine the recovery of a particular DNA extraction technique, or as a genotype control to compare against during genotyping assays, such as RFLP.

EXPERIMENTS

[0113] The above process was used to generate consensus sequences for 11 species of Cryptosporidium (C. felis, C. parvum, C. muris, C. hominis, C. meleagridis, C. andersoni, C. baileyi, C. bovis, C. canis, C. serpentis, and C. wrairi), 1 species of Giardia (G. intestinalis), and 2 species of microsporidia (Encephalitozoon intestinalis and Enterocytozoon bieneusi). Additionally, the above process was used to synthesize standards for the 18s rRNA genes of 5 species of Cryptosporidium (C. hominis, C. parvum, C. muris, C. meleagridis, and C. felis) and 1 species of Giardia (G. intestinalis). The PCR performance of these standards was then compared against that of genomic DNA from the target organisms using real-time PCR assays.

[0114] Prior to PCR amplification, synthetic standards were manufactured by a third-party. Native C. parvum, C. hominis, C. meleagridis, C. muris and G. intestinalis DNA was purchased from either ATCC or BEI Resources. Sample DNA concentrations (ng/μl) were converted to CN concentrations (CN/μl) based upon the CN densities calculated previously. Synthetic and native DNA samples were serially diluted in TE (10 mM Tris-HCl, 1 mM EDTA, pH 8.0) to obtain concentrations between 107 and 100 CN/μl.

[0115] PCR amplification was performed in triplicate using a real-time PCR assay optimized for the LightCycler 2.0. PCR amplification was confirmed by gel electrophoresis using the Invitrogen E-Gel® Ex (2% agarose) system.

[0116] FIGS. 1-6 show standard curves for each species. The standard curves were calculated by plotting PCR threshold cycle values (CO versus log₁₀ (CN/r×n) for each DNA type. Linearity (R²) and efficiency were calculated over at least 4 orders of magnitude using linear regression.

[0117] Standard curves generated using synthetic or native DNA demonstrated nearly identical R² values of approximately 0.99. For each species, the synthetic and native DNA demonstrated substantially equivalent PCR performance (Table 1), demonstrating that either could be used to construct an accurate standard curve for target quantification.

[0118] While the present disclosure has been described in terms of exemplary embodiments, those skilled in the art will recognize that the present disclosure can be practiced with (or without) modifications in the spirit and scope of the appended claims. The examples disclosed herein are merely illustrative and are not meant to be an exhaustive list of all possible designs, embodiments, applications or modifications of the present disclosure.

TABLE-US-00009 TABLE 1 Limit of Detection, Efficiency, and Linearity LOD (CN/ Efficiency Δ Δ Organisms Type rxn) (%) R² Eff Tm C. hominis Genomic 10 101.82% 0.99229 2.07% 0.09 Synthetic 10 103.89% 0.99466 C. parvum Genomic 10 95.34% 0.99397 6.57% 0.31 Synthetic 10 101.91% 0.99355 C. meleagridis Genomic 10 97.31% 0.99513 1.83% -0.15 Synthetic 10 95.49% 0.99677 C. muris Genomic N/A 115.78% 0.99239 7.04% N/A Synthetic 10 108.73% 0.99074 C. felis Synthetic 10 107.90% 0.99691 N/A N/A G. lamblia Genomic 5500 135.89% 0.98598 8.11% Synthetic 5500 127.79% 0.98832

Sequence CWU 1

1

2611745DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium andersoni 1aacctggttg atcctgcaag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataagc ttttaaacgg cgaaactgcg aatggctcat taaaaaagtt attatttact 120tgataatcca aaactacatg gataaccgtg gtaattctag agctaataca tgcgaaaaaa 180cccaacttcg cggaagggtt gtatttatta gataaagaac caatgagctt ggtgattcat 240aataacttta cggatcgcat ctctgatgcg acatatcatt caagtttctg acctatcagc 300tttagacggt agggtattgg cctaccgtgg ctatgacggg taacggggaa ttagggttcg 360attccggaga gggagcctga gaaacggcta ccacatctaa ggaaggcagc aggcgcgcaa 420attacccaat cctgacacag ggaggtagtg acaagaaata acaatacagg gcctaacggt 480cttgtaattg gaatgagtga agtataaacc cctttacgag tatcaattgg agggcaagtc 540tggtgccagc agccgcggta attccagctc caatagcgta tattaaagtt gttgcagtta 600aaaagctcgt agttggattt ctgttgtata attttataat attaccaagg taattattat 660attatcaaca tccttcctat tatattctaa atatatagga aattttactt tgagaaaatt 720agagtgctta aagcaggcaa ctgccttgaa tactccagca tggaataata agtaaggact 780tttgtctttc ttattggttc taggacaaaa gtaatggtta atagggacag ttgggggcat 840tcgtatttaa cagccagagg tgaaattctt agatttgtta aagacgaact actgcgaaag 900catttgccaa ggatgttttc attaatcaag aacgaaagtt aggggatcga agacgatcag 960ataccgtcgt agtcttaacc ataaactatg ccgactagag attggaggtt gttccttact 1020ccttcagcac cttatgagaa atcaaagttt ttgggttctg gggggagtat ggtcgcaagg 1080ctgaaactta aaggaattga cggaagggca ccaccaggag tggagcctgc ggcttaattt 1140gactcaacac gggaaaactc accaggtcca gacataggaa ggattgacag attgatagct 1200ctttcttgat tctatgggtg gtggtgcatg gccgttctta gttggtggag tgatttgtct 1260ggttaattcc gttaacgaac gagaccttaa cctgctaaat aggtaataga aattttattt 1320ctatcttatc ttcttagagg gactttgcgt gcctaacgcg aggaagtttg aggcaataac 1380aggtctgtga tgcccttaga tgtcctgggc cgcacgcgcg ctacactgat gcatccagcg 1440agtatatatc ctgtttcgaa ggaaatgggt aatcttatga gtatgcatcg tgatggggat 1500agatcattgc aattattgat ctttaacgag gaattcctag taagcgcaag tcatcagctt 1560gcgctgatta cgtccctgcc ctttgtacac accgcccgtc gctcctaccg attgagtgat 1620ccggtgaata attcggacca tgctacagta gcaaatacat agcaagggaa gtttcgtaaa 1680ccttatcact tagaggaagg agaagtcgta acaaggtttc cgtaggtgaa cctgcagaag 1740gatca 174521733DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium baileyi 2aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataagc ttctatacgg ctaaactgcg aatggctcat tataacagtt atagtttact 120tgataatcct tactacatgg ataaccgtgg taattctaga gctaatacat gcgaaaagac 180ccgacttctc ggaagggttg tatttattag ataaagaacc aatactcttg gtgactcata 240ataactttac ggatcacatt tatgtgacat atcattcaag tttctgacct atcagcttta 300gacggtaggg tattggccta ccgtggctat gacgggtaac ggggaattag ggttcgattc 360cggagaggga gcctgagaaa cggctaccac atctaaggaa ggcagcaggc gcgcaaatta 420cccaatcctg acacagggag gtagtgacaa gaaataacaa tacagggcct aacggtcttg 480taattggaat gagttaagta taaacccctt tacaagtagc aattggaggg caagtctggt 540gccagcagcc gcggtaattc cagctccaat agcgtatatt aaagttgttg cagttaaaaa 600gctcgtagtt ggatttctgt taatacttat atacaatacc acggtattta tataacatta 660acataattca cattacttat ttaaagtatg tgaaacttta ctttgagaaa attagagtgc 720ttaaagcagg ctattgcctt gaatactcca gcatggaata atattaaaga tttttatctt 780tcttattggt tctaggataa aaataatgat taatagggac agttgggggc atttgtattt 840aacagtcaga ggtgaaattc ttagatttgt taaagacaaa ctactgcgaa agcatttgcc 900aaggatgttt tcattaatca agaacgaaag ttaggggatc gaagacgatc agataccgtc 960gtagtcttaa ccataaacta tgccgactag agattggagg ttgttcctta ctccttcagc 1020accttatgag aaatcaaagt ctttgggttc tggggggagt atggtcgcaa ggctgaaact 1080taaaggaatt gacggaaggg caccaccagg agtggagcct gcggcttaat ttgactcaac 1140acgggaaaac tcaccaggtc cagacatagg aaggattgac agattgatag ctctttcttg 1200attctatggg tggtggtgca tggccgttct tagttggtgg agtgatttgt ctggttaatt 1260ccgttaacga acgagacctt aacctgctaa atagacataa gaaaattatt tcttatctgt 1320cttcttagag ggactttgtg tgtttaacac gaggaagttt taggcaataa caggtctgtg 1380atgcccttag atgtcctggg ccgcgcgcgc gctacactga tgtatccatc aagtattctc 1440ctgtttcgaa ggaaatgggt aatcttatga atatacatcg tgatggggat agatcattgc 1500aattattgat cttcaacgag gaattcctag taagcgcaag tcatcagctt gcgctgatta 1560cgtccctgcc ctttgtacac accgcccgtc gctcctaccg attgagtgat ccggtgaatt 1620attcggacca tacataagta gcaatacatg taaggaaagt tttgtaaacc ttatcactta 1680gaggaaggag aagtcgtaac aaggtttccg taggtgaacc tgcagaagga tca 173331729DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium bovis 3aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataagc ttttatacag ctaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt tactacatgg ataaccgtgg taattctaga gctaatacat gcgaaaaaac 180ccgacttctt ggaagggttg tatttattag ataaagaacc aatatttttg gtgactcata 240ataactttac ggatcacatt atgtgacata tcattcaagt ttctgaccta tcagctttag 300acggtagggt attggcctac cgtggctatg acgggtaacg gggaattagg gttcgattcc 360ggagagggag cctgagaaac ggctaccaca tctaaggaag gcagcaggcg cgcaaattac 420ccaatcctaa tacagggagg tagtgacaag aaataacaat acagaacctt acggttttgt 480aattggaatg agttaagtat aaacccctta acaagtatca attggagggc aagtctggtg 540ccagcagccg cggtaattcc agctccaata gcgtatatta aagttgttgc agttaaaaag 600ctcgtagtta atcttctgtt aatttttata tataatatca cgatatttat ataatattaa 660cataattcat attacttttt agtatatgaa actttacttt gagaaaatta gagtgcttaa 720agcaggctat tgccttgaat actccagcat ggaataatat taaggatttt tattcttctt 780attggttcta gaataaaaat gatgattaat aggaacagtt gggggcattt gtatttaaca 840gtcagaggtg aaattcttag atttgttaaa gacaaactac tgcgaaagca tttgccaagg 900atgttttcat taatcaagaa cgaaagttag gggatcgaag acgatcagat accgtcgtag 960tcttaaccat aaactatgcc aactagagat tggaggttgt tccttactcc ttcagcacct 1020tatgagaaat caaagtcttt gggttctggg gggagtatgg tcgcaaggct gaaacttaaa 1080ggaattgacg gaagggcacc accaggagtg gagcctgcgg cttaatttga ctcaacacgg 1140gaaaactcac caggtccaga cataggaagg attgacagat tgatagctct ttcttgattc 1200tatgggtggt ggtgcatggc cgttcttagt tggtggagtg atttgtctgg ttaattccgt 1260taacgaacga gaccttaacc tgctaaatag acgtaaaaaa ttcgtttttt acctgtcttc 1320ttagagggac tttgtgtgtt taacacgagg aagttttagg caataacagg tctgtgatgc 1380ccttagatgt cctgggccgc gcgcgcgcta cactgatgca tccatcaagt tttctcctgc 1440ttcgaaggaa gtgggtaatc ttttgaatat gcatcgtgat ggggatagaa cattgcaatt 1500attgttcttc aacgaggaat tcctagtaag cgcaagtcat cagcttgcgc tgattacgtc 1560cctgcccttt gtacacaccg cccgtcgctc ctaccgattg agtgatccgg tgaattattc 1620ggaccatact ttgtagccaa tacatgtaag gaaagttttg taaaccttat cacttagagg 1680aaggagaagt cgtaacaagg tttccgtagg tgaacctgca gaaggatca 172941742DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium canis 4aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataagc ttttatacgg ttaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt tacttacatg gataaccgtg gtaattctag agctaataca tgcgaaaaaa 180cctgactttt tggaaaggtt gtatttatta gataaagaac caatattttt ggtgattcat 240aataacttta cggatcacat tttatgtgac atatcattca agtttctgac ctatcagctt 300tagacggtag ggtattggcc taccgtggca atgacgggta acggggaatt agggttcgat 360tccggagagg gagcctgaga aacggctacc acatctaagg aaggcagcag gcgcgcaaat 420tacccaatcc taatacaggg aggtagtgac aagaaataac aatacaggac tttaacagtt 480ttgtaattgg aatgagttga gtataaaccc ctttacaagt atcaattgga gggcaagtct 540ggtgccagca gccgcggtaa ttccagctcc aatagcgtat attaaagttg ttgcagttaa 600aaagctcgta gttggatttc tgttaataat ttatatataa tatttaacat atttatataa 660tattaacata attcatatta ctatttatag tatatgaaac tttactttga gaaaattaga 720gtgcttaaag caggcttttg ccttgaatac tagagcatgg aataatatta aagattttta 780tctttcttat tggttctaag atagaaataa tgattaatag ggacagttgg gggcatttgt 840atttaacagt tagaggtgaa attcttagat ttgttaaaga caaactaatg cgaaagcatt 900tgccaaggat gttttcatta atcaagaacg aaagttaggg gatcgaagac gatcagatac 960cgtcgtagtc ttaaccataa actatgccaa ctagagattg gaggttgttc ttttactcct 1020tcagcacctt atgagaaatc aaagtctttg ggttctgggg ggagtatggt cgcaaggctg 1080aaacttaaag gaattgacgg aagggcacca ccaggagtgg agcctgcggc ttaatttgac 1140tcaacacggg aaaactcacc aggtccagac ataggaagga ttgacagatt gatagctctt 1200tcttgattct atgggtggtg gtgcatggcc gttcttagtt ggtggagtga tttgtctggt 1260taattccgtt aacgaacgag accttaacct gctaaataga catttgaaat atttttattt 1320cttatttgtc ttcttagagg gactttgtat gtttaataca gggaagtttt aggcaataac 1380aggtctgtga tgcccttaga tgtcctgggc cgcgcgcgcg ctacactgat gcatccatca 1440agtttttttc ctgtttcgaa ggaaatgggt aatcttttga atatgcatcg tgatggggat 1500agatcattgc aattattgat cttgaacgag gaattcctag taagcgcaag tcatcagctt 1560gcgctgatta cgtccctgcc ctttgtacac accgcccgtc gctcctaccg attgaatgat 1620ccggtgaatt attcggacca tacgttgtag caatacatgt agggaaagtt tcgtaaacct 1680tatcatttag aggaaggaga agtcgtaaca aggtttccgt aggtgaacct gcagaaggat 1740ca 174251781DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium felis 5aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataaac ttttatacgg ttaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt tttactacat ggataaccgt ggtaattcta gagctaatac atgcggaaag 180accctacttt atggaaaggt cgtatttatt agataaagaa ccaatatttt tttttggtga 240ctcataataa ctttacggat cacaataatt tattttgtga catatcattc aagtttctga 300cctatcagct ttagacggta gggtattggc ctaccgtggc tatgacgggt aacggggaat 360tagggttcga ttccggagag ggagcctgag aaacggctac cacatctaag gaaggcagca 420ggcgcgcaaa ttacccaatc ctaatacagg gaggtagtga caagaaataa caatacagga 480ctttacggtt ttgtaattgg aatgagttaa gtataaaccc ctttacaagt atcaattgga 540gggcaagtct ggtgccagca gccgcggtaa ttccagctcc aatagcgtat attaaagttg 600ttgcagttaa aaagctcgta gttggatttc tgttaatacc ttatatataa tatttttttt 660taaatattat tatgtaagat taacataatt catatttttt aagactgaat ttttagtttt 720gataatatga aattttactt tgagaaaatt agagtgctta aagcaggctt ttgccttgaa 780tactccagca tggaataata ataaaagatt tttatctttt ttttattggt tctaagataa 840aaataatgat taatagggac agttgggggc atttgtattt aacagtcaga ggtgatattc 900ttagatttgt taaagacaaa ctaatgcgaa agcatttgcc aaggatgttt tcattaatca 960agaacgaaag ttaggggatc gaagacgatc agataccgtc gtagtcttaa ccataaacta 1020tgccaactag agattggagg ttgttcctta ctccttcagc accttatgag aaatcaaagt 1080ctttgggttc tggggggagt atggtcgcaa ggctgaaact taaaggaatt gacggaaggg 1140caccaccagg agtggagcct gcggcttaat ttgactcaac acgggaaaac tcaccaggtc 1200cagacatagg aaggattgac agattgatag ctctttcttg attctatggg tggtggtgca 1260tggccgttct tagttggtgg agtgatttgt ctggttaatt ccgttaacga acgagacctt 1320aacctgctaa atagacataa gaaatatatt aatatttttt atttgtcttc ttagagggac 1380tttgtatgtt taatacaggg aagttttagg caataacagg tctgtgatgc ccttagatgt 1440cctgggccgc gcgcgcgcta cactgatgca tccgtcaagt atatttatcc tgtttcgaag 1500gaaatgggta atcttttgaa tatgcatcgt gatggggata gatcattgca attattgatc 1560tttaacgagg aattcctagt aagcgcaagt catcagcttg cgctgattac gtccctgccc 1620tttgtacaca ccgcccgtcg ctcctaccga ttgaatgatc cggtgaatta ttcggaccat 1680acaatgtagc aatacatgta aggaaagttt cgtaaacctt atcatttaga ggaaggagaa 1740gtcgtaacaa ggtttccgta ggtgaacctg cggaaggatc a 178161750DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium hominis 6aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataaac ttttatacgg ttaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt ttacttacat ggataaccgt ggtaattcta gagctaatac atgcgaaaaa 180actcgacttt atggaagggt tgtatttatt agataaagaa ccaatataat tggtgactca 240taataacttt acggatcaca attaatgtga catatcattc aagtttctga cctatcagct 300ttagacggta gggtattggc ctaccgtggc aatgacgggt aacggggaat tagggttcga 360ttccggagag ggagcctgag aaacggctac cacatctaag gaaggcagca ggcgcgcaaa 420ttacccaatc ctaatacagg gaggtagtga caagaaataa caatacagga ctttttggtt 480ttgtaattgg aatgagttaa gtataaaccc ctttacaagt atcaattgga gggcaagtct 540ggtgccagca gccgcggtaa ttccagctcc aatagcgtat attaaagttg ttgcagttaa 600aaagctcgta gttggatttc tgttaataat ttatataaaa tattttgatg aatatttata 660taatattaac ataattcata ttactatttt tttttttagt atatgaaatt ttactttgag 720aaaattagag tgcttaaagc aggcatatgc cttgaatact ccagcatgga ataatattaa 780agatttttat cttttttatt ggttctaaga taagaataat gattaatagg gacagttggg 840ggcatttgta tttaacagtc agaggtgaaa ttcttagatt tgttaaagac aaactaatgc 900gaaagcattt gccaaggatg ttttcattaa tcaagaacga aagttagggg atcgaagacg 960atcagatacc gtcgtagtct taaccataaa ctatgccaac tagagattgg aggttgttcc 1020ttactccttc agcaccttat gagaaatcaa agtctttggg ttctgggggg agtatggtcg 1080caaggctgaa acttaaagga attgacggaa gggcaccacc aggagtggag cctgcggctt 1140aatttgactc aacacgggaa aactcaccag gtccagacat aggaaggatt gacagattga 1200tagctctttc ttgattctat gggtggtggt gcatggccgt tcttagttgg tggagtgatt 1260tgtctggtta attccgttaa cgaacgagac cttaacctgc taaatagaca taagaaatat 1320tatatttttt atctgtcttc ttagagggac tttgtatgtt taatacaggg aagttttagg 1380caataacagg tctgtgatgc ccttagatgt cctgggccgc gcgcgcgcta cactgatgca 1440tccatcaagt atatattcct gtttcgaagg aaatgggtaa tcttttgaat atgcatcgtg 1500atggggatag atcattgcaa ttattgatct tgaacgagga attcctagta agcgcaagtc 1560atcagcttgc gctgattacg tccctgccct ttgtacacac cgcccgtcgc tcctaccgat 1620tgaatgatcc ggtgaattat tcggaccata ctttgtagca atacatgtaa ggaaagtttc 1680gtaaacctta tcatttagag gaaggagaag tcgtaacaag gtttccgtag gtgaacctgc 1740agaaggatca 175071744DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium meleagridis 7aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataaac ttttatacgg ttaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt tacttacatg gataaccgtg gtaattctag agctaataca tgcgaaaaaa 180cctgacttaa tggaagggtt gtatttatta gataaagaac caatataatt ggtgactcat 240aataacttta cggatcacaa tttatgtgac atatcattca agtttctgac ctatcagctt 300tagacggtag ggtattggcc taccgtggca atgacgggta acggggaatt agggttcgat 360tccggagagg gagcctgaga aacggctacc acatctaagg aaggcagcag gcgcgcaaat 420tacccaatcc taatacaggg aggtagtgac aagaaataac aatacaggac tttttggttt 480tgtaattgga atgagttaag tataaacccc tttacaagta tcaattggag ggcaagtctg 540gtgccagcag ccgcggtaat tccagctcca atagcgtata ttaaagttgt tgcagttaaa 600aagctcgtag ttggatttct gttaataatt tatatataat atttgattaa tatttatata 660atattaacat aattcatatt actaaattta ttagtatatg aaattttact ttgagaaaat 720tagagtgctt aaagcaggca tatgccttga atactccagc atggaataat attaaagatt 780tttatctttc ttattggttc taagataaaa ataatgatta atagggacag ttgggggcat 840ttgtatttaa cagtcagagg tgaaattctt agatttgtta aagacaaact aatgcgaaag 900catttgccaa ggatgttttc attaatcaag aacgaaagtt aggggatcga agacgatcag 960ataccgtcgt agtcttaacc ataaactatg ccaactagag attggaggtt gttccttact 1020ccttcagcac cttatgagaa atcaaagtct ttgggttctg gggggagtat ggtcgcaagg 1080ctgaaactta aaggaattga cggaagggca ccaccaggag tggagcctgc ggcttaattt 1140gactcaacac gggaaaactc accaggtcca gacataggaa ggattgacag attgatagct 1200ctttcttgat tctatgggtg gtggtgcatg gccgttctta gttggtggag tgatttgtct 1260ggttaattcc gttaacgaac gagaccttaa cctgctaaat agacataaga aatattatat 1320tttttatttg tcttcttaga gggactttgt atgtttaata cagggaagtt ttaggcaata 1380acaggtctgt gatgccctta gatgtcctgg gccgcgcgcg cgctacactg atgcatccat 1440caagtaataa tcctgtttcg aaggaaatgg gtaatctttt gaatatgcat cgtgatgggg 1500atagatcatt gcaattattg atcttgaacg aggaattcct agtaagcgca agtcatcagc 1560ttgcgctgat tacgtccctg ccctttgtac acaccgcccg tcgctcctac cgattgaatg 1620atccggtgaa ttattcggac catactttgt agcaatacat gtaaggaaag tttcgtaaac 1680cttatcattt agaggaagga gaagtcgtaa caaggtttcc gtaggtgaac ctgcagaagg 1740atca 174481746DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium muris 8aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataagc ttttatacgg cgaaactgcg aatggctcat taaaacagtt atagtttact 120tgataatcaa aactacatgg ataaccgtgg taattctaga gctaatacat gcgaaaaaac 180ccaactttgc ggaagggttg tatttattag ataaagaacc aatgagcttg gtgattcata 240ataactttac ggatcgcatc tctgatgcga catatcattc aagtttctga cctatcagct 300ttagacggta gggtattggc ctaccgtggc tatgacgggt aacggggaat tagggttcga 360ttccggagag ggagcctgag aaacggctac cacatctaag gaaggcagca ggcgcgcaaa 420ttacccaatc ctgacacagg gaggtagtga caagaaataa caatacaggg cctaacggtc 480ttgtaattgg aatgagtgaa gtataaaccc ctttacgagt atcaattgga gggcaagtct 540ggtgccagca gccgcggtaa ttccagctcc aatagcgtat attaaagttg ttgcagttaa 600aaagctcgta gttggatttc tgttgtataa tctataatat tactaaggta tatattatat 660tatcaacatc cttcctatta tatttctaaa tatataggaa actttacttt gagaaaatta 720gagtgcttaa agcaggcaac tgccttgaat actccagcat ggaataataa gtaaggactt 780ttgtctttct tattggttct aggacaaaag taatggttaa tagggacagt tgggggcatt 840cgtatttaac agccagaggt gaaattctta gatttgttaa agacgaacta ctgcgaaagc 900atttgccaag gatgttttca ttaatcaaga acgaaagtta ggggatcgaa gacgatcaga 960taccgtcgta gtcttaacca taaactatgc cgactagaga ttggaggttg ttccttactc 1020cttcagcacc ttatgagaaa tcaaagtctt tgggttctgg ggggagtatg gtcgcaaggc 1080tgaaacttaa aggaattgac ggaagggcac caccaggagt ggagcctgcg gcttaatttg 1140actcaacacg ggaaaactca ccaggtccag acataggaag gattgacaga ttgatagctc 1200tttcttgatt ctatgggtgg tggtgcatgg ccgttcttag ttggtggagt gatttgtctg 1260gttaattccg ttaacgaacg agaccttaac ctgctaaata ggtaatagaa atttattttt 1320ctatattatc ttcttagagg gactttgcgt gtctaacgcg aggaagtttg aggcaataac 1380aggtctgtga tgcccttaga tgtcctgggc cgcacgcgcg ctacactgat gcatccaacg 1440agtatatatc ctgcttcgaa ggaagtgggt aatcttatga gtatgcatcg tgatggggat 1500agatcattgt aattattgat ctttaacgag gaattcctag taagcgcaag tcatcagctt 1560gcgctgatta cgtccctgcc ctttgtacac accgcccgtc gctcctaccg attgagtgat 1620ccggtgaata attcggacca tgctacgagt agcaaataca tagcaaggaa agtttcgtaa 1680accttatcac ttagaggaag gagaagtcgt aacaaggttt ccgtaggtga acctgcggaa 1740ggatca 174691748DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium parvum 9aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct

60aagtataaac ttttatacgg ttaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt ttacttacat ggataaccgt ggtaattcta gagctaatac atgcgaaaaa 180actcgacttt atggaagggt tgtatttatt agataaagaa ccaatataat tggtgactca 240taataacttt acggatcaca ttaaatgtga catatcattc aagtttctga cctatcagct 300ttagacggta gggtattggc ctaccgtggc aatgacgggt aacggggaat tagggttcga 360ttccggagag ggagcctgag aaacggctac cacatctaag gaaggcagca ggcgcgcaaa 420ttacccaatc ctaatacagg gaggtagtga caagaaataa caatacagga ctttttggtt 480ttgtaattgg aatgagttaa gtataaaccc ctttacaagt atcaattgga gggcaagtct 540ggtgccagca gccgcggtaa ttccagctcc aatagcgtat attaaagttg ttgcagttaa 600aaagctcgta gttggatttc tgttaataat ttatataaaa tattttgatg aatatttata 660taatattaac ataattcata ttactatata tttttagtat atgaaatttt actttgagaa 720aattagagtg cttaaagcag gcatatgcct tgaatactcc agcatggaat aatattaaag 780atttttatct ttcttattgg ttctaagata agaataatga ttaataggga cagttggggg 840catttgtatt taacagtcag aggtgaaatt cttagatttg ttaaagacaa actaatgcga 900aagcatttgc caaggatgtt ttcattaatc aagaacgaaa gttaggggat cgaagacgat 960cagataccgt cgtagtctta accataaact atgccaacta gagattggag gttgttcctt 1020actccttcag caccttatga gaaatcaaag tctttgggtt ctggggggag tatggtcgca 1080aggctgaaac ttaaaggaat tgacggaagg gcaccaccag gagtggagcc tgcggcttaa 1140tttgactcaa cacgggaaaa ctcaccaggt ccagacatag gaaggattga cagattgata 1200gctctttctt gattctatgg gtggtggtgc atggccgttc ttagttggtg gagtgatttg 1260tctggttaat tccgttaacg aacgagacct taacctgcta aatagacata agaaatatta 1320tattttttat ctgtcttctt agagggactt tgtatgttta atacagggaa gttttaggca 1380ataacaggtc tgtgatgccc ttagatgtcc tgggccgcgc gcgcgctaca ctgatgcatc 1440catcaagtat atattcctgt ttcgaaggaa atgggtaatc ttttgaatat gcatcgtgat 1500ggggatagat cattgcaatt attgatcttg aacgaggaat tcctagtaag cgcaagtcat 1560cagcttgcgc tgattacgtc cctgcccttt gtacacaccg cccgtcgctc ctaccgattg 1620aatgatccgg tgaattattc ggaccatact ttgtagcaat acatgtaagg aaagtttcgt 1680aaaccttatc atttagagga aggagaagtc gtaacaaggt ctccgtaggt gaacctgcag 1740aaggatca 1748101745DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium serpentis 10aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataagc ttttatacgg cgaaactgcg aatggctcat taaaacagtt atagtttact 120tgataatcaa aactacatgg ataaccgtgg taattctaga gctaatacat gcgaaaaggc 180ccgacttttt ggaagggttg tatttattag ataaagaacc aatatttttg gtgattcata 240ataactttac ggatcgcatc tctgatgcga catatcattc aagtttctga cctatcagct 300ttagacggta gggtattggc ctaccgtggc tatgacgggt aacggggaat tagggttcga 360ttccggagag ggagcctgag aaacggctac cacatctaag gaaggcagca ggcgcgcaaa 420ttacccaatc ctgacacagg gaggtagtga caagaaataa caatacaggg cctaacggtc 480ttgtaattgg aatgagtgaa gtataaaccc ctttacaagt atcaattgga gggcaagtct 540ggtgccagca gccgcggtaa ttccagctcc aatagcgtat attaaagttg ttgcagttaa 600aaagctcgta gttggatttc tgttgtattt ttataatatt attaaggtaa tatttataat 660atcaacatcc ttcctattat atttttaata tataggaaat tttactttga gaaaattaga 720gtgcttaaag caggcaactg ccttgaatac tccagcatgg aataataagt aaggactttt 780gtctttcttg ttggttctag gataaaagta atggttaata gggacagttg ggggcattcg 840tatttaacag tcagaggtga aattcttaga tttgttaaag acgaactact gcgaaagcat 900ttgccaagga tgttttcatt aatcaagaac gaaagttagg ggatcgaaga cgatcagata 960ccgtcgtagt cttaaccata aactatgccg actagagatt ggaggtgttg ttccttactc 1020cttcagcacc ttatgagaaa tcaaagtctt tgggttctgg ggggagtatg gtcgcaaggc 1080tgaaacttaa aggaattgac ggaagggcac caccaggagt ggagcctgcg gcttaatttg 1140actcaacacg ggaaaactca ccaggtccag acataggaag gattgacaga ttgatagctc 1200tttcttgatt ctatgggtgg tggtgcatgg ccgttcttag ttggtggagt gatttgtctg 1260gttaattccg ttaacgaacg agaccttaac ctgctaaata gataataaaa atttattttt 1320ttattttatc ttcttagagg gactttgcgt gtctaacgcg aggaagtttg aggcaataac 1380aggtctgtga tgcccttaga tgtcctgggc cgcacgcgcg ctacactgat gcatccagcg 1440agtatatatc ctgtttcgaa ggaaatgggt aatcttgtga gtatgcatcg tgatggggat 1500agatcattgc aattattgat ctttaacgag gaattcctag taagcgcaag tcatcagctt 1560gcgctgatta cgtccctgcc ctttgtacac accgcccgtc gctcctaccg attgagtgat 1620ccggtgaata attcggacca tgctatagta gcaaatacat agtaaggaaa gtttcgtaaa 1680ccttatcact tagaggaagg agaagtcgta acaaggtttc cgtaggtgaa cctgcagaag 1740gatca 1745111746DNAArtificial Sequence18S rRNA Consensus Sequence for Cryptosporidium wrairi 11aacctggttg atcctgccag tagtcatatg cttgtctcaa agattaagcc atgcatgtct 60aagtataaac ttttatacgg ttaaactgcg aatggctcat tataacagtt atagtttact 120tgataatctt tacttacatg gataaccgtg gtaattctag agctaataca tgcgaaaagg 180cccgacttta tggaagggtt gtatttatta gataaagaac caatataatt ggtgactcat 240aataacttta cggatcacat aaattgtgac atatcattca agtttctgac ctatcagctt 300tagacggtag ggtattggcc taccgtggca atgacgggta acggggaatt agggttcgat 360tccggagagg gagcctgaga aacggctacc acatctaagg aaggcagcag gcgcgcaaat 420tacccaatcc taatacaggg aggtagtgac aagaaataac aatacaggac tttttggttt 480tgtaattgga atgagttaag tataaacccc tttacaagta tcaattggag ggcaagtctg 540gtgccagcag ccgcggtaat tccagctcca atagcgtata ttaaagttgt tgcagttaaa 600aagctcgtag ttggatttct gttaataatt tatatataat attttgaaaa tatttatata 660atattaacat aattcatatt actatatatt tttagtatat gaaattttac tttgagaaaa 720ttagagtgct taaagcaggc atatgccttg aatactccag catggaataa tattaaagat 780ttttatcttt cttattggtt ctaagataag aataatgatt aatagggaca gttgggggca 840tttgtattta acagtcagag gtgaaattct tagatttgtt aaagacaaac tagtgcgaaa 900gcatttgcca aggatgtttt cattaatcaa gaacgaaagt taggggatcg aagacgatca 960gataccgtcg tagtcttaac cataaactat gccaactaga gattggaggt tgttccttac 1020tccttcagca ccttatgaga aatcaaagtc tttgggttct ggggggagta tggtcgcaag 1080gctgaaactt aaaggaattg acggaagggc accaccagga gtggagcctg cggcttaatt 1140tgactcaaca cgggaaaact caccaggtcc agacatagga aggattgaca gattgatagc 1200tctttcttga ttctatgggt ggtggtgcat ggccgttctt agttggtgga gtgatttgtc 1260tggttaattc cgttaacgaa cgagacctta acctgctaaa tagacataag aaatattata 1320ttttttatct gtcttcttag agggactttg tatgtttaat acagggaagt tttaggcaat 1380aacaggtctg tgatgccctt agatgtcctg ggccgcgcgc gcgctacact gatgcatcca 1440tcaagtatat attcctgttt cgaaggaaat gggtaatctt ttgaatatgc atcgtgatgg 1500ggatagatca ttgcaattat tgatcttgaa cgaggaattc ctagtaagcg caagtcatca 1560gcttgcgctg attacgtccc tgccctttgt acacaccgcc cgtcgctcct accgattgaa 1620tgatccggtg aattattcgg accatacttt gtagcaatac atgtaaggaa agtttcgtaa 1680accttatcat ttagaggaag gagaagtcgt aacaaggttt ccgtaggtga acctgcagaa 1740ggatca 1746121783DNAArtificial Sequence18S rRNA Consensus Sequence for Giardia intestinalis 12ataaatggac tcccgccgcc gtacgggctc gggcctccgt cacgcactat ataggacaag 60gtgctttatc tcgccgaggc cggttttttg gactggccca agagtcccca agggaatcca 120aaaaaatgtg gtgcagacgg actcccgccg ccgtgcgggc gccgccctgc gtcccgcact 180ataggggacc ccccgacccg gaccgcgccg cgagggggcg gcccccgtgc cccgcggcgg 240cgcccgcgca gggcggcccg caggccccgg cgcccgcgcc cggccatccg gtcgatcctg 300ccggaatccg acgctctccc caaggacaaa gccatgcatg cccgcgcacc cgggaggcgg 360cggacggctc aggacaacgg ttgcaccccc cgcggcggtc cctgctagcc ggacaccgct 420ggcaacccgg cgccaagacg tgcgcgcaag ggcgggcgcc cgcgggcgag cagcgtgacg 480cagcgacggc ccgcccgggc ttccggggca tcacccggtc ggcgcggtcg cggcgcgccg 540agggcccgac gcctggcgga gaatcagggt tcgactccgg agagcgggcc tgagagacgg 600cccgcacatc caaggacggc agcaggcgcg gaacttgccc aatgcgcggc gcgcgaggca 660gcgacgggga gtgcgcgagc gaggcgggcc cacagccccc gccgcggagc cgagggcaag 720gtctggtgcc agcagccgcg gtaattccag ctcggcgggc gtcgcgtggc gctgctgcag 780ttaaaacgcc cgtagttggc cccccgccgc cacgaggaaa cgggagcgct ccaggcaggc 840ccgttggacc cgccgcgtgg gaccgcgcag cgggcgcggc gcgccgcggc agccccgagg 900agagcgggcg ggggcaccgg taccggccgg ggacgggtga aacaggatga tcccgccgag 960accgccggcc gcgcaggcgc ctgccaagac cgcctctgtc aatcaagggc gaaggccggg 1020ggctagaagg cgatcagaca ccaccgtatt cccggccgta aacggtgccg ccccgcggcc 1080ggcgcgcgcg tcccgccggc cgcccaggga aaccgggagg ctccgggctc tggggggagt 1140atggccgcaa ggctgaaact tgaaggcatt gacggagggg taccaccaga cgtggagtct 1200gcggctcaat ctgactcaac gcgcgcacct caccaggccc ggacgcgcgg aggaccgaca 1260gccgggcgcg ctttcgcgat cgcgcgggcg gtggtgcatg gccgctccca gcccgtggcg 1320cgagccgtct gctccattgc gacaacgagc gagaccccgg ccgcgggcgc cgcgggacgg 1380cccgcgcgag cgggaggacg gcggggcgat agcaggtctg tgatgccctc agacgccctg 1440ggccgcacgc gcgctacact ggcggggcca gccggcgccc gcgaggacgc gcggagcccc 1500cgccgtggcc gggaccgcgg gctggaacgc ccccgcgcac caggaatgtc ttgtaggcgc 1560gcgcccccac cgcgcgccgg acgcgtccct gccccttgta cacaccgccc gtcgctccta 1620ccgactgggc gcggcggcga gcgccccgga cgcgcgaagg gccgcgagcc cccgcgcctg 1680gaggaaggag aagtcgtaac aaggtatccg taggtgaacc tgcggatgga tccctcgcgc 1740gcgcggcgtg cgtccccgcg gcccggtcgg cacgcgagcc ccg 1783131300DNAArtificial Sequence18S rRNA Consensus Sequence for Encephalitozoon intestinalis 13tcaccaggtt gattctgcct gacgtggatg ctattctctg ggactaagcc atgcatgttg 60atgaaccttg tgggggattg acggacggct cagtgatagt acgatgattt ggttggcggg 120agagctgtaa ctgcgggaaa ctgcaggtag ggggctagga gtgtttttga cacgagccaa 180gtaagttgta ggcctatcag ctggtagtta gggtaatggc ctaactaggc ggagacggga 240gacgggggat cggggtttga ttccggagag ggagcctgag agatggctac tacgtccaag 300gatggcagca ggcgcgaaac ttgcctaatc ctttggggag gcggttatga gaagtgagtt 360tttttcgagt gtaaaggagt cgagattgat tggagggcaa gtcgggtgcc agcagccgcg 420gtaatacctg ctccaatagt gtctatggtg aatgctgcag ttaaaaagtc cgtagtcttt 480tgtatgtctt tgtttggggg attatgtcct gatgtggatg taagaggttt ggcagaggac 540gaggggcacc ggatagttgg gcgaggggtg aaatacgaag accctgactg gacggacaga 600agcgaaggct gtgctcttgg acttatgtga cgatgaagga cgaaggctag aggatcgaaa 660tcgattagat accgttttag ttctagcagt aaacgatgcc gactggacgg gactatatag 720tgttgtgcat gagaaatctt gagtatgtgg gttctgggga tagtatgctc gcaagagtga 780aacttgaaga gattgacgga aggacaccac aaggagtgga gtgtgcggct taatttgact 840caacgcgggg caacttaccg gttctgaagc gggcaggaga acgaggacgg gatgcgcgcg 900gcggtggtgc atggccgttt gaaatggatg gcgtgagctt tggattaagt tgcgtaagat 960gtgagaccct ttgacagtgc tctttggggc aagggaggaa tggaacagaa caggtccgtt 1020atgccctgag atgaagcggg cggcacgcgc actacgatag atggcgaggg agcctgctgt 1080gagggatgaa gctgtgtaat gggcttctga acgtggaatt cctagtaata acgattgaac 1140aagttgtttt gaatgggtcc ctgtcctttg tacacaccgc ccgtcgctat ctaagatgac 1200gcagtggacg aagattggaa ggtctgagtc cttcgtgtta gataagatat aagtcgtaac 1260atggctgctg ttggagaacc agcagcagga tcagtatttg 1300141298DNAArtificial Sequence18S rRNA Consensus Sequence for Enterocytozoon bieneusi 14gcattaggtt gattctgcct aacgtagatg ctagtctctg agattaagcc atgcatgtca 60gtgaagcctt acggtggaac ggcgaacggc tcagtaatgt tgcggtaatt tggtctctgt 120gtgtaaacta accacggtaa cctgtggcta aaagcgagaa taaggcgcaa ccctatcagc 180ttgttggtag tgtaaaggac taccaaggcc atgacgggta acgggaaatc agggtttgat 240tccggagagg gagcctgaga gatggctccc acgtccaagg acggcagcag gcgcgaaact 300tgtccactcc ttacggggga gacagtcatg agacgtgagt ataagacctg agtgtaaaga 360ccttagggtg aagcaattgg agggcaagct ttggtgccag cagccgcggt aactccaact 420ccaagagtgt ctatggtgga tgctgcagtt aaagggtccg tagtcgtgaa tgcaattaaa 480tgtcgttgtt caatagcgat gagtttgcta atgtttgcgg aacggatagg gagtgtagta 540tagactggcg aagaatgaaa tctcaagacc cagtttggac taacggaggc gaaggcgaca 600ctcttagacg tatcttagga tcaaggacga aggcaggagt atcgaaagtg attagacacc 660gctgtagttc ctgcagtaaa ctatgccgac agcctgtgtg tgagaatacg tgggcgggag 720aaatcttagt gttcgggctc tggggatagt acgctcgcaa gggtgaaact taaagcgaaa 780ttgacggaag gacactacca ggagtggatt gtgctgctta atttaactca acgcgggaaa 840acttaccagg gtcaagtcat tcgttgatcg aatacgtgag aatggcagga gtggtgcatg 900gccgttggaa attgatgggg cgacctttag cttaaatgct taaaccagtg agacctcctt 960gacaggtgtt ctgtaacaca ggagggtgga ggctataaca ggtccgtgat gcccttagat 1020atcctgggca gcaagcgcaa tacaatatct cttcagtaga caaagtgatt tgagatgagt 1080aggatctacg tttgtaaata cgtagtgaat aaggaattcc tagtaacggt gcctcatcaa 1140ggcatggtga atgtgtccct gttctttgta cacaccgccc gtcactattt cagatggtca 1200tagggatgaa gagcttcggc tctgaatatc tatggctaga taaagtacaa gtcgtaacaa 1260ggtttcagtt ggagaaccag ctgaaggatc attttcag 12981538DNAArtificial Sequence18s rRNA gene 15actggtagct agcctggatc gatcgggtgt agtactga 381616DNAArtificial Sequence18s rRNA gene 16tagcctggat ccatcg 16179DNAArtificial Sequence18s rRNA gene 17tattactga 91815DNAArtificial Sequence18s rRNA gene 18taggtagcct ggatc 151938DNAArtificial Sequence18s rRNA gene 19actggtagct agcctggatc gatcgggtgt agtactga 382016DNAArtificial Sequence18s rRNA gene 20tagcctggat ccatcg 16219DNAArtificial Sequence18s rRNA gene 21tattactga 92215DNAArtificial Sequence18s rRNA gene 22taggtagcct ggatc 152327DNAArtificial Sequence18s rRNA gene 23tatcaacatc cttcctatta tatttct 272426DNAArtificial Sequence18s rRNA gene 24tatcaacatc cttcctatta tattct 262528DNAArtificial Sequence18s rRNA gene 25tatcaacatt ccttcctatt atatttct 282627DNAArtificial Sequence18s rRNA gene 26tatcgacatc cttcctatta tatatct 27

Claims

1. A synthetic nucleic acid molecule having a consensus sequence constructed from an alignment of a plurality of nucleic acid sequences from microbial pathogens, the consensus sequence constructed by a method comprising: generating a frequency matrix comprising the frequency of each base at each position in the alignment; creating an information matrix comprising the amount of information provided by each base at each position in the alignment; and calculating an information score for each of the plurality of nucleic acid sequences; iterating over each position in the alignment and for each position: calculating base frequencies and determining a highest frequency base; if the highest frequency base's frequency is higher than a frequency threshold, assigning the highest frequency base to the consensus sequence; and if the highest frequency base's frequency is lower than the frequency threshold, removing a base corresponding to a nucleic acid sequence with a lowest information score and returning to (a); and performing a restriction fragment length polymorphism (RFLP) fingerprint of the constructed consensus sequence in silico.

2. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium andersoni shown in SEQ ID NO:1.

3. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium baileyi shown in SEQ ID NO:2.

4. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium bovis shown in SEQ ID NO:3.

5. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium canis shown in SEQ ID NO:4.

6. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium felis shown in SEQ ID NO:5.

7. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium hominis shown in SEQ ID NO:6.

8. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium meleagridis shown in SEQ ID NO:7.

9. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium muris shown in SEQ ID NO:8.

10. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium parvum shown in SEQ ID NO:9.

11. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium serpentis shown in SEQ ID NO:10.

12. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Cryptosporidium wrairi shown in SEQ ID NO:11.

13. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Giardia intestinalis shown in SEQ ID NO:12.

14. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Encephalitozoon intestinalis shown in SEQ ID NO:13.

15. The synthetic nucleic acid molecule according to claim 1, comprising the 18S rRNA consensus sequence for Enterocytozoon bieneusi shown in SEQ ID NO:14.

16. A synthetic nucleic acid molecule having a consensus sequence selected from the group consisting of: an 18S rRNA consensus sequence for Cryptosporidium andersoni, as shown in SEQ ID NO:1; an 18S rRNA consensus sequence for Cryptosporidium baileyi, as shown in SEQ ID NO:2; an 18S rRNA consensus sequence for Cryptosporidium bovis, as shown in SEQ ID NO:3; an 18S rRNA consensus sequence for Cryptosporidium canis, as shown in SEQ ID NO:4; an 18S rRNA consensus sequence for Cryptosporidium felis, as shown in SEQ ID NO:5; an 18S rRNA consensus sequence for Cryptosporidium hominis, as shown in SEQ ID NO:6; an 18S rRNA consensus sequence for Cryptosporidium meleagridis, as shown in SEQ ID NO:7; an 18S rRNA consensus sequence for Cryptosporidium muris, as shown in SEQ ID NO:8; an 18S rRNA consensus sequence for Cryptosporidium parvum, as shown in SEQ ID NO:9; an 18S rRNA consensus sequence for Cryptosporidium serpentis, as shown in SEQ ID NO:10; an 18S rRNA consensus sequence for Cryptosporidium wrairi, as shown in SEQ ID NO:11; an 18S rRNA consensus sequence for Giardia intestinalis, as shown in SEQ ID NO:12; an 18S rRNA consensus sequence for Encephalitozoon intestinalis, as shown in SEQ ID NO:13; and an 18S rRNA consensus sequence for Enterocytozoon bieneusi, as shown in SEQ ID NO:14.

17. A synthetic nucleic acid construct containing the synthetic nucleic acid having a consensus sequence as in claim 16.

18. The synthetic nucleic acid construct of claim 17, wherein the construct is linear.

19. The synthetic nucleic acid construct of claim 17, wherein the construct is a circular plasmid.

20. A microbial pathogen test kit comprising: a synthetic nucleic acid molecule having a consensus sequence constructed from an alignment of a plurality of nucleic acid sequences from microbial pathogens, the consensus sequence constructed by a method comprising: generating a frequency matrix comprising the frequency of each base at each position in the alignment; creating an information matrix comprising the amount of information provided by each base at each position in the alignment; and calculating an information score for each of the plurality of nucleic acid sequences; iterating over each position in the alignment and for each position: calculating base frequencies and determining a highest frequency base; if the highest frequency base's frequency is higher than a frequency threshold, assigning the highest frequency base to the consensus sequence; and if the highest frequency base's frequency is lower than the frequency threshold, removing a base corresponding to a nucleic acid sequence with a lowest information score and returning to (a); and performing a restriction fragment length polymorphism (RFLP) fingerprint of the constructed consensus sequence in silico.

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