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WO2024220571A2 - Procédés et compositions pour détecter une croissance microbienne dans des environnements bâtis - Google Patents

Procédés et compositions pour détecter une croissance microbienne dans des environnements bâtis Download PDF

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Publication number
WO2024220571A2
WO2024220571A2 PCT/US2024/025034 US2024025034W WO2024220571A2 WO 2024220571 A2 WO2024220571 A2 WO 2024220571A2 US 2024025034 W US2024025034 W US 2024025034W WO 2024220571 A2 WO2024220571 A2 WO 2024220571A2
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WIPO (PCT)
Prior art keywords
gene
kit
fungal
erh
product
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WO2024220571A3 (fr
Inventor
Karen DANNEMILLER
Ashleigh BOPE
Neeraja BALASUBRAHMANIAM
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Ohio State Innovation Foundation
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Ohio State Innovation Foundation
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/18—Testing for antimicrobial activity of a material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria

Definitions

  • the present invention relates to methods and kits and the use thereof to detect microbial growth in indoor environments that may contribute to poor indoor air quality and poor health outcomes.
  • the present disclosure addresses at least a portion of the problems described above through the use of the method of detection and using the inventive kit for detection of microbial growth in a built environment.
  • the present invention provides a method of inhibiting or reducing microbial growth in a built environment in at least one sample collected from the built environment, wherein the microbial growth is associated with damp conditions.
  • the built environment is selected from the group comprising: a laboratory, a hospital, a manufacturing plant, an airport, an airplane, a school, an office, a vehicle, an apartment complex, a dormitory, a barrack, a prison, a spacecraft, or a home.
  • the sample is a dust sample, a surface sample, an air sample, a water sample, and/or a combination of environmental samples.
  • the microbial growth is identified by detecting one or more gene(s) and product(s) thereof which are associated with a bio-process of sporulation or other growth processes in one or more microbe(s) in at least one sample collected from the built environment.
  • the one or more microbe(s) is a bacterium and/or fungus or protozoa.
  • the fungus is mold.
  • the fungus is Aspergillus, Neurospora, Myxococcus, Saccharomyces, or Penicillium or any other fungal taxa.
  • the one or more gene(s) or product(s) thereof can be related to morphological change, secondary metabolism, stress response, mitochondria or any process associated with microbial growth and are selected from genes in Table 2.
  • the one or more gene(s) or product(s) thereof can be selected from a group comprising ALTA7, atfB, catA, hsp30, nuo-49, rodA, wA, arpl, arp2, gel2, gna-3, mpgl, mtlD, pkaR, tpsA, velB, vosA, wetA, Canx homolog, tpiA, cam A, crzA, ecm33, hexl, bimG, mdmlO, cap2, catB, chsA, gpaA, nudA, rhoC, steA, candA-C, candA-N, ccg-8, dopl, fib A, fluG, laeA
  • the one or more genes can be bacterial or fungal. In some embodiments, at least one fungal gene and/or at least one bacterial gene is selected. In some embodiments, the one or more gene(s) and product(s) thereof are associated with a functional annotation related to fungal (mold) growth and sporulation, measured using Gene Ontology or GO.
  • the one or more gene(s) or product(s) thereof are measured by identifying a protein, a metabolite, a volatile organic compound, a chemical product, or a nucleic acid in the sample.
  • the nucleic acid is DNA and/or RNA.
  • the method of identifying the one or more gene(s) or product(s) thereof comprises quantitative polymerase chain reaction (qPCR), mass spectrometry, liquid chromatography, lateral flow chromatography, colorimetric dye, fluorescent dye, Biuret, Bradford, bicinchoninic, Folin-Lowry, Kjeldahl, antibody binding, ultraviolet light absorbance, gel electrophoresis, capillary electrophoresis, diphenylamine, polymerase chain reaction, RFLP analysis, protein detection methods and/or a combination thereof.
  • qPCR quantitative polymerase chain reaction
  • the one or more gene(s) or product(s) thereof is identified by detecting and/or quantifying the one or more gene(s) or product(s) using RNA Sequencing (RNA-Seq).
  • the product(s) are identified by detecting and/or quantifying the product(s) by lateral flow chromatography.
  • the present invention provides a method of inhibiting or reducing microbial growth by treating the built environment with a microbial growth inhibitor, once, daily for at least a week, using a microbial growth inhibition technique.
  • the microbial growth inhibitor can be a dehumidifier, an exhaust fan, an antimicrobial compound, a hydrophobic paint, or a combination thereof.
  • the present invention provides a kit for the detection of microbial growth in a built environment comprising identifying and quantifying expression of one or more gene(s) or product(s) thereof are selected from genes in Table 2.
  • the one or more gene(s) or product(s) thereof can be selected from a group comprising ALTA7, atfB, catA, hsp30, nuo-49, rodA, wA, arpl, arp2, gel2, gna-3, mpgl, mtlD, pkaR, tpsA, velB, vosA, wetA, Canx homolog, tpiA, camA, crzA, ecm33, hexl, bimG, mdmlO, cap2, catB, chsA, gpaA, nudA, rhoC, steA, candA-C, candA-N, ccg-8, dopl, fib A
  • the kit comprises a sample collection device.
  • the sample collection device is selected from a group comprising of a swab, a brush, tubes with lids, a pair of forceps, a vacuum cleaner with a collection bag, a canister, a zip-top bag, or a combination thereof.
  • the kit comprises a glass chamber, salt solution or distilled water to maintain relative humidity, a dew point water activity meter, nucleic acid extraction reagents, one or more control sample(s), a nucleic acid detection probe, DNA or RNA polymerase and a thermocycler.
  • the nucleic acid detection probe is a pair of forward and reverse primers.
  • the kit comprises of protein extraction reagents, a protein detection probe, and a lateral flow chromatography device.
  • the lateral flow chromatography device comprises of a protein lysate loading well, protein detection probe bound to a nitrocellulose membrane and a sample running buffer.
  • the protein detection probe is an antibody.
  • the kit comprises a sample resuspension buffer, a lysis buffer, a wash buffer, a phenol, and chloroform for extraction of the nucleic acid or proteins.
  • the expression of the one or more gene(s) is identified and quantified by quantitative polymerase chain reaction (qPCR), and product(s) thereof is detected by lateral flow chromatography.
  • qPCR quantitative polymerase chain reaction
  • the qPCR results can be read via a smart phone-based application, i.e., and the quantity of the product(s) thereof detected on the lateral flow chromatography device can be quantified via a smart phone-based application.
  • the expression of the one or more gene(s) or quantity of the product(s) thereof is compared to a control with a threshold value, database value, normalized value, relative value, validated value, or a combination thereof.
  • an increase in the expression of the one or more gene(s) or product(s) thereof compared to the control indicates microbial growth and a decrease in the expression of the one or more gene(s) or the quantity of product(s) thereof compared to the control indicates a lack of microbial growth.
  • any one gene is used to normalize the expression of the one or more gene(s) or the quantity of product(s) thereof.
  • Figure 1 shows an overview of methods.
  • Figure 2 shows locations of participating homes that donated dust to the study. Dust from 9 homes were collected and used for experiments, sequencing, and downstream bioinformatics analysis. Dust from one home (located in TX, marked ‘X’) was collected but excluded from sequencing due to low quality of extracted RNA.
  • Figures 3A-3D show PCA plots of gene expression.
  • Figure 3A shows a PCA plot of gene expression in house dust grouped by ERH.
  • PCoA of the relative abundance of fungal species in house dust grouped by ERH is shown in Figure 3B.
  • Figure 3C shows a PCA plot of gene expression by site and
  • Figure 3D shows a PCoA of the relative abundance of fungal species by site.
  • Color-intensity of samples are specific to ERH or site, and shapes are specific to ERH. Overlap between samples indicates greater similarity based on between-sample distance. A 95% confidence ellipse was added for each ERH condition in Figures 3A and 3B.
  • Figure 4 shows a metabolic pathway map of fungal genes upregulated at 100% ERH when compared to 50% ERH.
  • Figure 5 shows a bubble plot of representative GO terms associated with fungal growth in all ERH comparisons. Bubble sizes represent the number of upregulated genes within a GO category for a specific ERH comparison. Bubble color intensity values are based on the significance (-loglO(FDR)) of the GO term with darker intensity representing higher significance of GO enrichment. GO terms having similar functions were grouped into broader categories. Bars next to GO terms indicate the four broader categories: Morphological, Stress response, Mitochondria and Secondary metabolism.
  • Figure 6 shows a heatmap of TMM-normalized CPM expression values of fungal target genes upregulated at 100% ERH condition (top group) and upregulated at both 100% and 85% ERH (bottom group). Darker color intensities represent higher gene expression values. The bottom of the heatmap shows state locations ordered from west to east. Genes are ordered alphabetically.
  • Figures 7A-7C show bubble plots of log2FC values for target genes in each of the three upregulated fungal gene groups.
  • Figure 7 A shows genes upregulated at 100% ERH.
  • Figure 7B shows genes upregulated at 100% and 85% ERH and
  • Figure 7C shows genes upregulated at 85% along with their broad functional categories. Bubble color intensities represent functional categories, and the bubble size represents the magnitude of the log2FC value.
  • the log2FC values of genes upregulated at both 100% and 85% ERH are based on the 100% vs 50% comparison.
  • Figures 8A-8B show from top to bottom: contig count for clusters formed by Trinity, CD-HIT -EST clusters, contigs with a Swiss-Prot database annotation, contigs with a gene ontology mapping, and contigs with a KEGG ontology annotation.
  • Figure 8A shows the number of properly paired reads that exist after sequencing and filtering.
  • Figure 8B indicates the number of contigs assembled by Trinity and those with an annotation.
  • Figure 9 shows a heatmap of TMM-normalized CPM expression values of the top 10,000 differentially expressed genes. Samples and genes are hierarchically clustered based on the Complete linkage method.
  • Figures 10 A- 10C show MA plots for changes in gene expression between relative humidity ERH conditions of 100% vs 85% as in Figure 10A, 100% vs 50% as shown in Figure 10B, and 50% vs 85% as shown in Figure 10C.
  • Significantly differentially expressed contigs p a dj ⁇ 0.05
  • MA plots transform the counts onto log2 ratio (M, y-axis) and average log2 (A, x-axis) scales.
  • Figure 11 shows a Spearman correlation heatmap of differentially expressed genes in all pairwise comparisons having log2FC > 2 and p ⁇ 0.001.
  • Log2 transformed and TMM- normalized Counts Per Million values are used. Rows and columns are ordered based on hierarchical clustering based on the Complete linkage method. Correlation coefficients that are not significant (p>0.05) are shown as blank tiles on the heatmap.
  • Figure 12 shows the number of significantly upregulated and downregulated genes in each pairwise comparison for overall genes expressed and fungal annotated genes, where upregulated and downregulated genes had a log2FC >
  • Figure 13 shows the number of genes present in the dust at different ERH levels after one week.
  • Figure 14 shows a metabolic pathway map of fungal genes upregulated at 50% ERH when compared to 100% ERH.
  • Figures 15A-15C shows images showing the increase in the number of upregulated fungal metabolic pathways at 50% ERH (50% vs 100%) in Figure 15 A, to 85% (85% vs 50%) in Figure 15B and to 100% ERH (50% vs 100%) in Figure 15C.
  • Figure 16 shows fungal concentrations (spore equivalents per mg dust) in the dust at different ERH levels after one week.
  • Figure 17 shows composition of fungal taxa at 50% ERH after one week.
  • Figure 18 shows a heatmap of TMM normalized CPM expression values of target genes upregulated at 85% ERH (compared to 50%). Darker color intensities represent higher gene expression values. The bottom of the heatmap shows state locations ordered from west to east. Genes are ordered alphabetically.
  • Ranges can be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed.
  • antibody is used in the broadest sense, and specifically covers monoclonal antibodies (including full length monoclonal antibodies), polyclonal antibodies, and multispecific antibodies (e.g., bispecific antibodies).
  • Native antibodies and immunoglobulins are usually heterotetrametric glycoproteins of about 150,000 Daltons, composed of two identical light (L) chains and two identical heavy (H) chains. Each heavy chain has at one end a variable domain (VH) followed by a number of constant domains. Each light chain has a variable domain at one end (VL) and a constant domain at its other end.
  • Antibodies (Abs) exhibit binding specificity to a specific target.
  • Antibody specificity can be assessed by comparing binding signals in cells expressing the target protein to control cells with the target gene knocked out. A highly specific antibody should show no binding activity if the target is not there. With protein antigens, the antibody molecule contacts the antigen over a broad area of its surface that is complementary to the surface recognized on the antigen. Electrostatic interactions, hydrogen bonds, van der Waals forces, and hydrophobic interactions can all contribute to binding.
  • antimicrobial refers to an agent that kills microorganisms or stops their growth.
  • antibacterial refers to an agent that is proven to kill bacteria or stops bacterial growth.
  • antibiotics refers to a type of antimicrobial substance active against bacteria. These are the most important type of antimicrobial agent for fighting bacterial infections, and antibiotics medications are widely used in the treatment and prevention of such infections. They may either kill or inhibit the growth of bacteria.
  • Busilt environment as used herein is any human-made, naturally-occurring or modified structure, including commercial, retail, private, governmental, educational, temporary, vehicular, and recreational structures.
  • buffer refers to a solution consisting of a mixture of acid and its conjugate base, or vice versa. The solution is used as a means of keeping the pH at a nearly constant range to be used in a wide variety of chemical and biological applications.
  • “Comprising” is intended to mean that the compositions, methods, etc. include the recited elements, but do not exclude others. "Consisting essentially of' when used to define compositions and methods, shall mean including the recited elements, but excluding other elements of any essential significance to the combination.
  • compositions consisting essentially of the elements as defined herein would not exclude trace contaminants from the isolation and purification method and pharmaceutically acceptable carriers, such as phosphate buffered saline, preservatives, and the like.
  • Consisting of' shall mean excluding more than trace elements of other ingredients and substantial method steps for administering the compositions provided and/or claimed in this disclosure. Embodiments defined by each of these transition terms are within the scope of this disclosure.
  • a “control” is an alternative subject or sample used in an experiment for comparison purposes.
  • a control can be "positive” or “negative.”
  • Culture or “cell culture” is the process by which cells are grown under controlled conditions, generally outside their natural environment. After the cells of interest have been isolated from living tissue, they can subsequently be maintained under carefully controlled conditions. These conditions vary for each cell type, but generally consist of a suitable vessel with a substrate or medium that supplies the essential nutrients (amino acids, carbohydrates, vitamins, minerals), growth factors, hormones, and gases (CO2, O2), and regulates the physio-chemical environment (pH buffer, osmotic pressure, temperature). Most cells require a surface or an artificial substrate to form an adherent culture as a monolayer (one single-cell thick), whereas others can be grown free floating in a medium as a suspension culture.
  • essential nutrients amino acids, carbohydrates, vitamins, minerals
  • CO2, O2 growth factors, hormones, and gases
  • Cell culture also refers to the culturing of cells derived from multicellular eukaryotes, especially animal cells, in contrast with other types of culture that also grow cells, such as plant tissue culture, fungal culture, and microbiological culture (of microbes).
  • a “decrease” can refer to any change that results in a smaller amount of a symptom, disease, composition, condition, or activity.
  • a substance is also understood to decrease the genetic output of a gene when the genetic output of the gene product with the substance is less relative to the output of the gene product without the substance.
  • a decrease can be a change in the symptoms of a disorder such that the symptoms are less than previously observed.
  • a decrease can be any individual, median, or average decrease in a condition, symptom, activity, composition in a statistically significant amount.
  • the decrease can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% decrease so long as the decrease is statistically significant.
  • detect or “detecting” refers to an output signal released for the purpose of sensing of physical phenomenon. An event or change in environment is sensed and signal output released in the form of light.
  • database denotes a set of stored data that represents a collection of sequences, which in turn represent a collection of biological reference materials.
  • differentially expressed refers to the differential production of the mRNA transcribed from the gene, or the protein product encoded by the gene.
  • a differentially expressed gene may be overexpressed or under expressed as compared to the expression level of a normal or control cell. In one aspect, it refers to a differential that is 2.5 times, preferably 5 times, or preferably 10 times higher or lower than the expression level detected in a control sample.
  • the term “differentially expressed” also refers to nucleotide sequences in a cell or tissue which are expressed where silent in a control cell or not expressed where expressed in a control cell.
  • DNA construct refers to a sequence of deoxyribonucleotides including deoxyribonucleotides obtained from one or more sources.
  • “Expression” as used herein refers to the process by which information from a gene is used in the synthesis of a functional gene product that enables it to produce a peptide/protein end product, and ultimately affect a phenotype, as the final effect.
  • the term “gene” as used in this specification refers to a segment of deoxyribonucleotides (DNA) possessing the information required for synthesis of a functional biological product such as a protein or ribonucleic acid (RNA).
  • genetic engineering is used to indicate various methods involved in gene manipulation including isolation, joining, introducing of gene(s) as well as methods to isolate select organisms containing the manipulated gene(s).
  • gene expression refers to efficient transcription and translation of genetic information contained in concerned genes.
  • An "increase” can refer to any change that results in a greater amount of a symptom, disease, composition, condition, or activity.
  • An increase can be any individual, median, or average increase in a condition, symptom, activity, composition in a statistically significant amount.
  • the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% increase so long as the increase is statistically significant.
  • Inhibit means to decrease an activity, response, condition, disease, or other biological parameter. This can include but is not limited to the complete ablation of the activity, response, condition, or disease. This may also include, for example, a 10% reduction in the activity, response, condition, or disease as compared to the native or control level. Thus, the reduction can be a 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%, or any amount of reduction in between as compared to native or control levels.
  • kits describes a wide variety of bags, containers, carrying cases, and other portable enclosures which may be used to carry and store solid substances, liquid substances, and other accessories necessary to detect microbial growth in a built environment. Such kits and their contents along with any applicable procedures may be used to provide access to better health outcomes in accordance with the teachings of the present disclosure.
  • lysis refers to the process of breaking down the membrane of a cell, often by viral, enzymatic, or osmotic mechanisms that compromise cellular integrity.
  • metabolite or “metabolic compound” as used herein refers to small molecules that are generally intermediates or end products of a metabolic pathway or process.
  • a “mitochondrion” is a cellular membrane-bound compartment, or organelle found in most eukaryotic cells, which are essential for cellular respiration and cellular energy production. These cellular structures comprise their own genome consisting of 37 genes important for energy production, respiration, calcium regulation, heat generation, and mediating cell growth and death.
  • microorganism refers to one or more forms/species of bacteria or fungi.
  • nucleic acid as used herein means natural and synthetic DNA, RNA, oligonucleotides, oligonucleosides, and derivatives thereof. For ease of discussion, such nucleic acids are at times collectively referred to herein as “constructs,” “plasmids,” or “vectors.”
  • polymerase refers to an enzyme that synthesizes long chains of polymers or nucleic acids. DNA polymerase and RNA polymerase are used to assemble DNA and RNA molecules, respectively, by copying a DNA template strand using base-pairing interactions.
  • PCR polymerase chain reaction
  • PCR as used herein, also includes variants of PCR such as allele-specific PCR, asymmetric PCR, hot-start PCR, ligation-mediated PCR, multi- plex-PCR, reverse transcription PCR, or any of the other PCR variants known to those skilled in the art.
  • prevent or other forms of the word, such as “preventing” or “prevention,” is meant to stop a particular event or characteristic, to stabilize or delay the development or progression of a particular event or characteristic, or to minimize the chances that a particular event or characteristic will occur. Prevent does not require comparison to a control as it is typically more absolute than, for example, reduce. As used herein, something could be reduced but not prevented, but something that is reduced could also be prevented. Likewise, something could be prevented but not reduced, but something that is prevented could also be reduced. It is understood that where reduce or prevent are used, unless specifically indicated otherwise, the use of the other word is also expressly disclosed.
  • a “primer” is a short polynucleotide, generally with a free 3'-OH group that binds to a target or "template” potentially present in a sample of interest by hybridizing with the target, and thereafter promoting polymerization of a polynucleotide complementary to the target.
  • a “polymerase chain reaction” (“PCR”) is a reaction in which replicate copies are made of a target polynucleotide using a "pair of primers” or a “set of primers” consisting of an "upstream” and a “downstream” primer, and a catalyst of polymerization, such as a DNA polymerase, and typically a thermally-stable polymerase enzyme.
  • PCR A PRACTICAL APPROACH
  • All processes of producing replicate copies of a polynucleotide, such as PCR or gene cloning, are collectively referred to herein as "replication.”
  • a primer can also be used as a probe in hybridization reactions, such as Southern or Northern blot analyses. Sambrook et al., supra.
  • a "probe" when used in the context of polynucleotide manipulation refers to an oligonucleotide that is provided as a reagent to detect a target potentially present in a sample of interest by hybridizing with the target.
  • a probe will comprise a label or a means by which a label can be attached, either before or subsequent to the hybridization reaction.
  • Suitable labels include, but are not limited to radioisotopes, fluorochromes, chemiluminescent compounds, dyes, and proteins, including enzymes.
  • the term “recombinant” cells or population of cells refers to cells or population of cells into which an exogenous nucleic acid sequence is introduced using a delivery vehicle such as a plasmid.
  • “reduce” or other forms of the word, such as “reducing” or “reduction,” is meant lowering of an event or characteristic (e.g., tumor growth). It is understood that this is typically in relation to some standard or expected value, in other words it is relative, but that it is not always necessary for the standard or relative value to be referred to. For example, “reduces tumor growth” means reducing the rate of growth of a tumor relative to a standard or a control.
  • treatment refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder.
  • This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder.
  • active treatment that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder
  • causal treatment that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder.
  • palliative treatment that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder
  • preventative treatment that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder
  • supportive treatment that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.
  • kits Disclosed are the components to be used to prepare the disclosed kits as well as to be used within the methods disclosed herein. These and other materials are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these materials are disclosed that while specific reference of each various individual and collective combinations and permutation of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein. For example, if a particular kit is disclosed and discussed and a number of modifications that can be made to the kit components are discussed, specifically contemplated is each and every combination and permutation of the kit components and the modifications that are possible unless specifically indicated to the contrary.
  • kit components A, B, and C are disclosed as well as a set of kit components D, E, and F and an example of a combination of the components, or, for example, a combination of kit components comprising A-D is disclosed, then even if each is not individually recited each is individually and collectively contemplated meaning combinations, A-E, A-F, B-D, B- E, B-F, C-D, C-E, and C-F are considered disclosed. Likewise, any subset or combination of these is also disclosed. Thus, for example, the sub-group of A-E, B-F, and C-E would be considered disclosed. This concept applies to all aspects of this application including, but not limited to, steps in methods of making and using the disclosed compositions. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods.
  • Mold species can vary in damp areas, and secondary metabolic processes in mold can be independent of species.
  • This invention provides an evidence-based measurement target for evaluation of mold growth in built environments based on species-independent metabolic processes.
  • products from secondary metabolic pathways of fungi are speciesindependent and are more effective indicators of mold growth than measurement of any specific species.
  • the methods and kits herein are based on identification of nucleic acids, proteins, metabolites, volatile organic compounds, chemicals or a combination thereof that are differentially expressed when microbes are growing in a built environment. These nucleic acids and/or proteins can serve as targets in a quantitative microbial growth measurement method. The targets can be detected in a variety of ways discussed herein.
  • the invention provides a quantitative measurement technique that avoids subjectivity in microbial growth assessment and more robust results, which was a long-felt need. The lack of such a test is partially due to the complex nature of these indoor exposures.
  • Each home contains a unique and diverse microbial community that varies based on surface type, as well as a complex mixture of chemicals. Microbial species in a home can number in the hundreds to thousands.
  • the present invention provides methods related to indicators inherently associated with the presence of excess moisture and microbial growth. This invention takes advantage of the advent of high-throughput DNA/RNA sequencing, which presents an important opportunity to vastly improve exposure assessment. Previously, the use of culturebased methods to study microbial communities could only reveal a small fraction of these organisms present.
  • RNA sequencing reveals microbial function within an entire community. The use of this cutting-edge technology on environmental samples represents an underutilized opportunity to reveal answers to fundamental questions about the microbial processes that occur in damp buildings.
  • a built environment is a natural or man-made structure, or building wherein people live or work for example a house, laboratory, hospital, manufacturing plant, airport, airplane, school, and office.
  • Increase in the humidity and decrease in ventilation of such a built environment can support the growth of microbes such as bacteria and fungi, especially mold.
  • Mold is a type of fungi and can be broadly classified into three types: Allergenic, Pathogenic and Toxigenic.
  • Allergenic mold species are those that trigger allergic reactions such as asthma. Some examples for allergenic mold species are Chaetomium, Alternaria, Ulocladium, Serpula, Mucor, Aureobasidium and Penicillium .
  • Pathogenic mold species cause disease in immunocompromised individuals. In some embodiments, the pathogenic mold species is Aspergillus. Toxigenic mold species create and produce their own toxins which can lead to health problems that are sometimes lethal.
  • the toxigenic mold species are Stachybotrys or black mold and Trichoderma.
  • the built environment can have an equilibrium relative humidity (ERH) of 30%-100%.
  • ERH is the relative humidity of the atmosphere at a particular temperature at which a material neither gains nor loses moisture.
  • the ERH can be 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%,
  • the first step in the method of inhibiting or reducing microbial growth is identifying the microbial growth by detecting one or more gene(s) or product(s) thereof associated with the fungal growth processes including but not limited to sporulation, hyphal growth and conidium formation and other fungal growth- related functional processes in at least one sample collected from the built environment.
  • the sample can be a dust sample, a surface sample, an air sample, and/or a combination of environmental samples.
  • Sporulation is the process by which a vegetative cell undergoes a developmental change to form a metabolically inactive spore, or endospore in the scarcity of nutrition and optimal growth conditions.
  • fungal growth and sporulation genes detected or products thereof are selected from Table 2.
  • the genes include, ALTA7, atfB, catA, hsp30, nuo-49, rodA, wA, arpl, arp2, gel2, gna-3, mpgl, mtlD, pkaR, tpsA, velB, vosA, wetA, Canx homolog, tpiA, camA, crzA, ecm33, hexl, bimG, mdmlO, cap2, catB, chsA, gpaA, nudA, rhoC, steA, candA-C, candA-N, ccg-8, dopl, fib A, fluG, laeA, nudG, sidH, chsC, grrA, hymA, or sidC or a combination thereof.
  • the genes or products thereof can be fungal growth-related morphological changes, stress response, mitochondria and secondary metabolism, and other metabolic processes.
  • quantitative polymerase chain reaction PCR
  • Lateral flow chromatography is used to quantify the product(s) thereof.
  • the increase in the gene(s) expression or the quantity of the product(s) in collected samples as compared to the levels in controls can indicate microbial growth.
  • the quantity of the microbes identified are compared to a control with a threshold value, database value, normalized value, relative value, validated value, or a combination thereof. This control can be from a different environment, or the same environment at different time point (or multiple previous time points).
  • a treatment can be applied to inhibit the microbial growth.
  • the microbial growth inhibition techniques comprise the use of a dehumidifier, an exhaust fan, an anti-microbial compound, a hydrophobic paint, or a combination thereof.
  • the treatment can be administered hourly, every 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23 hours, daily once, twice or three times weekly, monthly for up to 1, 2, 3 week(s), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 month(s), 1, 2, or 3 years.
  • the anti-microbial treatment can be anti-fungal and/or antibacterial.
  • an anti-fungal agent is selected from the group comprising (3- ethoxypropyl)mercury bromide, 2-methoxyethylmercury chloride, 2-phenylphenol, 2,4,5- tri chlorophenol, 2, 2-dibromo-3 -nitrilopropionamide, 8-hydroxy quinoline, 8- phenylmercurioxyquinoline, acibenzolar, acypetacs, albendazole, aldimorph, allicin, allyl alcohol, allyl isothiocyanate, ametoctradin, aminopyrifen, amisulbrom, amobam, ampropylfos, anilazine, asomate, aureofungin, azaconazole, azithiram, azoxystrobin, barium polysulfide, benalaxyl, benodanil, benomyl, benquinox, bentaluron, benthiavalicarb,
  • the antibacterial is an antibiotic.
  • the antibiotic is selected from a group including, but not limited to penicillins (including, but not limited to amoxicillin, clavulanate and amoxicillin, ampicillin, dicloxacillin, oxacillin, and penicillin V potassium), tetracyclins (including, but not limited to demeclocycline, doxycycline, eravacycline, minocycline, omadacycline, sarecycline, and tetracycline), cephalosporins (cefaclor, cefadroxil, cefdinir, cephalexin, cefprozil, cefepime, cefiderocol, cefotaxime, cefotetan, ceftaroline, cefazidme, ceftriaxone, and cefuroxime), quinolones (also referred to as fluoroquinolones include, but are not
  • Also disclosed herein is a method of detecting and identifying microbial growth by identifying one or more gene(s) or product(s) thereof, wherein the microbe is a bacterium, fungi, or protozoan.
  • a product is a compound produced by a cell metabolism and excreted to the extracellular medium.
  • the extracellular medium can be air or soil inside the built environment.
  • microbial gene(s) or product(s) thereof are ALTA7, atfB, catA, hsp30, nuo-49, rodA, wA, arpl, arp2, gel2, gna-3, mpgl, mtlD, pkaR, tpsA, velB, vosA, wetA, Canx homolog, tpiA, cam A, crzA, ecm33, hexl, bimG, mdmlO, cap2, catB, chsA, gpaA, nudA, rhoC, steA, candA-C, candA-N, ccg-8, dopl, fib A, fluG, laeA, nudG, sidH, chsC, grrA, hymA, or sidC.
  • the detection assays can be quantitative polymerase chain reaction (qPCR), lateral flow chromatography, colorimetric dye, fluorescent dye, Biuret, Bradford, bicinchoninic, Folin-Lowry, Kjeldahl, antibody binding, ultraviolet light absorbance, gel electrophoresis, capillary electrophoresis, diphenylamine, polymerase chain reaction, RFLP analysis, and/or a combination thereof.
  • qPCR quantitative polymerase chain reaction
  • lateral flow chromatography colorimetric dye
  • fluorescent dye fluorescent dye
  • Biuret Bradford
  • bicinchoninic Folin-Lowry
  • Kjeldahl Kjeldahl
  • antibody binding ultraviolet light absorbance
  • gel electrophoresis capillary electrophoresis
  • diphenylamine diphenylamine
  • polymerase chain reaction RFLP analysis, and/or a combination thereof.
  • the one or more gene(s) or product(s) thereof are measured by identifying protein, RNA, and
  • the one or more gene(s) or product(s) thereof is identified by detecting and/or quantifying the expression of one or more gene(s).
  • the one or more gene(s) are either genomic and/or mitochondrial and are measured using quantitative polymerase chain reaction (qPCR).
  • the ALTA7, atfB, catA, hsp30, nuo-49, rodA, wA, arpl, arp2, gel2, gna-3, mpgl, mtlD, pkaR, tpsA, velB, vosA, wetA, Canx homolog, tpiA, cam A, crzA, ecm33, hexl, bimG, mdmlO, cap2, catB, chsA, gpaA, nudA, rhoC, steA, candA-C, candA-N, ccg-8, dopl, fib A, fluG, laeA, nudG, sidH, chsC, grrA, hymA, or sidC are associated with a fungal growth process such as hyphal extension, sporulation and/or a combination thereof.
  • Kits Disclosed herein is a kit for the detection of microbial growth in a built environment, wherein the kit is used to identify and quantify expression of one or more gene(s) or product(s) thereof in one or more microbe(s) in at least one sample obtained from a built environment.
  • the kit comprises a sample collection device.
  • the sample collection device is selected from a group comprising of a swab, a brush, sterile tubes with lids, vacuum cleaner with a sterile collection bag, a canister, a zip-top bag, or a combination thereof for the sterile collection of samples, wherein a sample is a dust sample, a surface sample, an air sample, and/or a combination of environmental samples.
  • the kit further can comprise a glass chamber, for incubating the soil samples collected from the built environment and a salt solution or distilled water to maintain relative humidity along with a AquaLabTM dew point water activity meter to measure the relative humidity of the sample.
  • the kit can further comprise a sample resuspension buffer, a lysis buffer, a wash buffer, a phenol, and chloroform for extraction of nucleic acids and proteins. Wherein, during the phenol-chloroform extraction, a mixture of phenol, chloroform, and isoamyl alcohol is added to samples to promote the partitioning of proteins, lipids and debris into an organic phase, leaving the DNA in the aqueous phase.
  • control sample(s) a nucleic acid or protein detection probe
  • DNA or RNA polymerase DNA or RNA polymerase and thermocycler or a lateral flow chromatography device.
  • the nucleic acid detection probe is a pair of forward and reverse primers and the expression of the one or more gene(s) is identified and quantified by quantitative polymerase chain reaction (qPCR).
  • the protein detection probe can be an antibody, and the one or more product(s) thereof is detected in a whole protein lysate obtained from the at least one sample by lateral flow chromatography wherein the lateral flow chromatography device comprises of a protein lysate loading well, protein detection probe bound to a nitrocellulose membrane and a sample running buffer.
  • decanted sample resuspension buffer can be collected after resuspending the sample and loaded on the lateral flow chromatography device.
  • the qPCR gene expression and protein density results can be read and quantified via a smart phone-based application.
  • the kit comprises components for comparing the expression of the one or more gene(s) to a control with a threshold value, database value, normalized value, relative value, validated value, or a combination thereof.
  • a threshold value e.g. 1 mM
  • database value e.g. 1 mM
  • normalized value e.g. 1 mM
  • relative value e.g. 1 mM
  • validated value e.g., a combination thereof.
  • Dust was collected and incubated in laboratory chambers to simulate elevated moisture conditions. First, RNA from 9 sites were screened for increased secondary metabolic pathways associated with elevated moisture. 10 potential target products associated with moisture were identified and then validated through qPCR in 50 sites ( Figure 1).
  • Dust collection Floor dust was focused on because 1) it is less variable than air samples and 2) it represents a long-term exposure that could be expected to be stable for about a season.
  • the staff collected house dust samples by vacuuming into a filter using established protocols. A sample was collected from both the main living area and bedroom. Collection from carpets was prioritized to maximize dust collection but was collected from solid surfaces when needed. The goal was to collect >25 g of dust. If insufficient dust ( ⁇ 10 g) was collected (as noted by visual inspection), it was also be collected from upholstered furniture. The occupant was asked for their vacuum bag or for dust in their bagless vacuum.
  • the EPA’s Asthma Home Environment Checklist will be offered to the occupant.
  • a survey was conducted to gain more information about dwelling (age, rental status, condition), pests (cockroaches, mice, rats), pets (dogs, cats, other furry animals, birds, other), number of occupants, heating and cooling systems, whether windows are opened on a regular basis, tobacco product use, cooking habits, and other factors.
  • Other information such as location, was observed from a Geographic Information System (GIS).
  • GIS Geographic Information System
  • Chamber Experiments Methods were consistent with previous protocols. Briefly, dust was sieved to 300 am, mixed, 100 mg placed on baking aluminum foil trays, and incubated at 25°C with set relative humidity levels. Dust was stored at room temperature for the short period of time prior to use to preserve microbial communities. Relative humidity was controlled in each 3.8 L glass chamber using 100 mL of salt solution (NaCl above water activity of 0.76 and MgC12 below) and verified with an Aqualab 4TE water activity meter (Decagon Devices, Pullman, WA). Relative humidity included the following conditions held for 1 week: 30%, 40%, 50%, 60%, 70%, 80%, 85%, 90%, and 100% RH. Relative humidity was monitored in the chambers using HOBO data loggers (OnSet, Cambridge, MA). Before and after incubation, water activity of the dust was measured in the AquaLab 4TE water activity meter and water content were calculated by changes in dust weight.
  • salt solution NaCl above water activity of 0.76 and MgC12 below
  • RNA sequencing is a screening step to identify potential targets. It was limited to 9 sites due to the high cost and processing time associated with this process, and 10 potential targets were evaluated by qPCR in all 50 sites.
  • RNA Extraction and Sequencing To measure gene expression, RNA was extracted using a method that was utilized previously. The Qiagen Microbiome RNA extraction kit was utilized with a lOx increase in the concentration of P-mercaptoethanol to further prevent RNA degradation from RNases. Extracted RNA was immediately frozen at -80°C prior to use and transported on dry ice. RNA was sequenced at the Yale sequencing facility using a protocol that had successfully been used to retrieve RNA from dust in the past. Fungal RNA is more important than bacterial RNA due to the broader range of tolerated relative humidity levels in fungi. Therefore, eukaryotic RNA was selected using the polyA protocol. A total of 9 samples were run on a single Illumina NovaSeq lane (3 total lanes will be used), which has provided sufficient coverage in the past.
  • qPCR assays were created for these 10 potential target genes and the presence of these targets was measured in cDNA reverse transcribed from RNA from samples incubated at 50%, 85%, and 100% ERH from all 50 sites to validate the results.
  • the top three targets that were the most strongly associated (Kruskal Wallis) with moisture level were selected for analysis in Objective 2.
  • Microbial measurement To determine microbial communities, dust was extracted with a modified DNA extraction protocol using the MoBio PowerSoil Powerlyzer kit (MoBio, Carlsbad, CA, USA). Growth was analyzed using qPCR with universal fungal and bacterial primers as well as DNA sequencing of the ITS and 16S regions, as described previously. Bioinformatics analysis was conducted with QIIME, BLAST, and FHiTINGS to process reads and assign taxonomy. Sequencing data was made quantitative by multiplying the relative abundance values by total concentration values determined by qPCR. These techniques allowed for full characterization of the microbial communities, including richness, evenness, total concentration, P diversity measures, and taxonomic identification and quantification. Overall, this analysis allowed determining the amount of microbial growth as well as the species that grow, as done in previous studies.
  • FHiTINGS was selected as the tool to use for taxonomic identification of fungi.
  • Other available tools require clustering of reads prior to identification. Clustering prior to sequence identification could result in misidentification of reads.
  • Sample Analysis The three selected moisture indicators identified in Objective 1 were measured in all of the homes in at least two locations. In damaged homes, one location was close to the damage (same room), and another was far from the damage (adjacent room). In non-moisture damaged homes, two adjacent rooms were selected for sampling, with preferential selection of the living room.
  • TRINITY_DN1656_cO_gl RODL EMENI rodA Unregulated at 100% ERH 0
  • TRINIT Y DN372_c0_g 1 WA EMENI wA Unregulated at 100% ERH 0
  • TRINITY_DN9867_cO_g2 CAPZB ASPFU cap2 Unregulated at 85% ERH 0
  • TRINITY DN284 l_c0_g2 CAN1C EMENI candA-C Unregulated at 85% ERH 0
  • TRINIT Y DN21893_c0_g2 CANIN EMENI candA-N Unregulated at 85% ERH 0
  • TRINITY_DN1725_cO_gl CCG8 NEUCR ccg-8 Unregulated at 85% ERH 0
  • TRINITY_DN30562_c0_g6 SIDA ASPFU sidA Unregulated at 85% ERH 0
  • TRINITY_DN46210_c0_gl 2 8 5.37 1.22E-05 100% vs 85%
  • TRINITY_DN285_cO_g2 0 8.91 8.37E-12 100% vs 85%
  • TRINITY_DN166_cO_g4 0 8.97 2.03E-11 100% vs 85% TRINITY_DN5789_cO_gl 0 8.54 1.77E-12 100% vs 85%
  • TRINITY_DN17345_cO_g2 0 8.97 7.45E-05 100% vs 85%
  • TRINITY_DN3575_c0_gl 9 50.91 1.72E-05 100% vs 50%
  • TRINITY_DN19748_cO_gl 9 12.48 4.38E-22 85% vs 50% TRINIT Y_DN2314_cO_g2 9 9.16 9.33E-11 85% vs 50%
  • TRINITY_DN10244_c0_gl 9 11.5 1.02E-16 85% vs 50%
  • TRINITY_DN13764_cO_gl 9 9.56 8.36E-15 85% vs 50%
  • TRINITY_DN2841_cO_g2 8.13 1.09E-12 85% vs 50%
  • TRINITY_DN11446_c0_gl 8 8.69 1.74E-11 85% vs 50%
  • TRINIT Y_DN219195_cO_gl 8 1 10.76 4.71E-12 85% vs 50%
  • TRINITY_DN16965_cO_gl 8 1 11.65 1.94E-16 85% vs 50%
  • TRINITY_DN48838_cO_gl 7 1 8.75 2.99E-09 85% vs 50%
  • TRINITY_DN3657_cO_gl 7 1 8.43 2.63E-10 85% vs 50%
  • TRINITY_DN22889_c0_g3 5 1 8.01 1.72E-06 85% vs 50%
  • RNA RNA could be used directly to measure mold growth in homes, or their protein products may also be used. All of these genes are associated with fungal growth.
  • Microbial communities that grow in response to damp conditions express genes and have specific metabolic pathways and functional changes that may be strongly associated with negative health outcomes. Analysis of gene expression and metabolic changes in microbial communities have repeatedly acted as early and sensitive predictors of environmental shifts in other systems. Changing environmental factors like temperatures and moisture result in fungal growth with increased production of volatile organic compound emissions (VOCs) and mycotoxins. Damp conditions lead to increased fungal allergen potency and metabolic activity that can result in degradation of chemicals such as phthalate esters in the dust. Growing fungal communities in house dust at elevated moisture conditions results in increased expression of secondary metabolite, allergenic and pathogenic genes.
  • VOCs volatile organic compound emissions
  • a Qualtrics survey (Qualtrics, Provo, UT) containing the consent form, as well as questions on relevant home and indoor environmental measures were used for screening participants. Participants were asked if there was any evidence of present water damage, moisture, leaks (such as damp carpet or leaky plumbing) or visible mold inside their homes. If participants answered in the affirmative, then these homes were not recruited for the study.
  • the survey also contained information about the floor area and flooring type that was vacuumed, the frequency of vacuuming, types of floor cleaning, the number of occupants (adults and children), number of pets (dogs, cats, birds, and other furry pets) as well as any prior history of moisture damage and mold in participants’ homes within the last five years.
  • Dust collection instructions were sent to the participants over email. Participants were asked to collect floor dust (>25 g), emphasizing collection from the main living areas inside their homes (living room and bedroom) using their home vacuum. If the home vacuum did not contain a vacuum bag, participants were asked to remove dust from the canister and place it in a zip top bag. Participants were then asked to ship their collected dust to the lab or have it dropped off to a designated location for pick up. Once the dust was received, all dust was screened to eliminate the presence of SARS CoV-2, using a previously described protocol and no dust samples were excluded. Recruitment and dust collection procedures were approved by the Ohio State University Behavioral Institutional Review Board under study number 019B0457 for the duration of the study.
  • RNA extracts were analyzed using the High Sensitivity RNA ScreenTape analysis on the Agilent 4200 TapeStation Bioanalyzer (Agilent, Santa Clara, CA, USA) at The Genomics Shared Resource Center (The Ohio State University Comprehensive Cancer Center Shared Resources, Columbus, OH, USA).
  • RNA extracts were then sent to the Yale Center for Genomic Analysis (Yale University, New Haven, CT, USA) where they were reverse transcribed and then sequenced on a NovaSeq 2x100 lane with 25 million reads per sample.
  • RNASeq library preparation was performed using the NEBNext Single Cell/Low Input RNA Library Prep Kit (New England Biolabs, USA) and the NEB Ultra II FS (New England Biolabs, USA) workflow for Illumina.
  • the polyA selection protocol was used to select eukaryotic mRNA. Sequence data was submitted to GenBank under accession number PRJNA1072816.
  • De novo metatranscriptome assembly was conducted using Trinity with default settings and was run on the Ohio Supercomputer (Ohio Supercomputer Center, Ohio). Trimmomatic within the Trinity pipeline was used to remove poor quality reads and contigs with a length less than 300 base pairs (bp). Contigs from the Trinity assembly were clustered using CD-HIT -EST based on 80% sequence similarity. These clusters from CD-HIT -EST represent all expressed contigs and constitutes the full transcriptome. [0126] Abundance estimation and alignment were run within the Trinity pipeline with default parameters. RSEM was used to estimate transcript abundance in each sample and to determine transcript-level expression counts of the RNA-Seq fragments for each transcript using alignment-based quantification.
  • Bowtie2 was used to align the quality trimmed paired-end reads after Trimmomatic to the full transcriptome created using CD-HIT -EST. Read coverage was then quantified using Samtools to capture read alignment statistics for concordant read pairs (yielding concordant alignments 1 or more times to the CD-HIT -EST transcriptome) with a MAPQ greater than 2.
  • Transcript-level abundance estimates were used to construct a matrix of counts and a matrix of normalized expression values. Normalized expression values include Counts Per Million (CPM), Transcripts per Million (TPM) and Trimmed Mean of M-values (TMM) and account for transcript length, number of reads mapped to a transcript, total number of reads over all transcripts and library size (sequencing depth). Gene-level count and gene-level normalized expression matrices were calculated using txlmport implemented directly in the Trinity pipeline.
  • DESeq2 was used within the Trinity pipeline to perform Differential Gene Expression (DGE) analysis of expressed genes. DGE performed using gene-level counts were used for downstream target gene identification. Performing differential expression analysis on gene levels, in addition to contig or transcript levels, improves interpretation of annotated contigs and potentially increases statistical power. Pairwise comparisons between the three ERH conditions (50%, 85% and 100%) were performed, giving rise to six pairwise ERH comparisons. Genes that were most differentially expressed based on the most significant False Discovery rate (FDR) and log2FC (log2 fold change) values were extracted and used for subsequent Gene Ontology (GO) enrichment analysis.
  • FDR False Discovery rate
  • log2FC log2 fold change
  • Transcripts were annotated using Trinotate, designed for comprehensive functional annotation of de novo transcriptomes. Trinotate integrates all functional annotation data into an SQLite database, which is used to create a whole annotation report for the transcriptome.
  • Trinotate used BLAST+ sequence homology search of transcripts and predicted coding regions against the SwissProt database and protein domain identification using a HMMER search against the PF AM database. Predicted coding regions were identified using TransDecoder that utilizes a minimum length Open Reading Frame (ORF) found in a transcript sequence.
  • the TrEMBL/SwissProt database was used for Gene Ontology (GO) and KEGG assignments of transcripts using Trinotate. KEGG assignments for genes were analyzed using the KEGG Mapper tool to identify the number of metabolic pathways and visualized using the iPath3 tool as metabolic pathway maps.
  • GOseq developed specifically to account for gene length bias in RNA-seq data, was used within the Trinity pipeline to perform functional GO enrichment testing. Results from the GO enrichment was analyzed for enriched GO categories based on significance of enrichment using FDR values and the number of DE genes within these GO categories at each pairwise ERH comparison.
  • DNA extractions were performed using the Maxwell RSC PureFood GMO and Authentication Kit (Promega, USA) using the protocol for lysing food and seed samples. Modifications included alterations to the bead beating in which 0.3 g of 100 gm glass beads, 0.1 g of 500 gm glass bead, and 1 g of PowerBeads (Qiagen, USA) were used for the bead mix and bead beat for 5 minutes. In addition, the incubation step was modified to allow the samples to be incubated for 30 minutes at room temperature.
  • a DAD A2 -based bioinformatics pipeline customized for ITS sequences was run using R on Ohio Supercomputer (Ohio Supercomputer Center, Ohio). Adapters were first removed using Cutadapt, BioStrings, and ShortRead. Denoising was performed using DADA2 where the maxEE and truncQ parameters of the filterAndTrim function were both set to eight following Rolling et al. The UNITE version 9.0 database was used for taxonomic identification.
  • PCA Principal Component Analysis
  • CCM Principal Coordinates Analysis
  • the adonis2 function in R using the vegan package was used to determine statistical significance of ERH groupings (p ⁇ 0.05) from the Euclidean and Bray-Curtis distance matrix.
  • the test employed 10,000 permutations and used FDR to adjust for multiple comparisons. Significance was defined at FDR-adjusted p ⁇ 0.05.
  • a 95% confidence ellipse using the stat ellipse function within the ggplot2 package was created to compare moisture conditions to each other, where a smaller ellipse around the data indicates less variance in that dataset group.
  • the Spearman rank correlation coefficient was calculated using the corrplot package for differentially expressed genes based on moisture condition. Only the correlation coefficients that were significant (p ⁇ 0.05) were considered. The Spearman rank correlation coefficient determines the strength and direction in the relationship between the data where a value of 1 indicates the strongest positive correlation. [0137] To identify species with differences in abundance between the ERH levels, the Kruskal- Wallis test was first performed to determine significant difference (p ⁇ 0.05), followed by pairwise Wilcoxon rank sum test using FDR to control for multiple comparisons. To determine significant differences between the number of fungal genes present by ERH condition, Kruskal- Wallis test followed by pairwise Wilcoxon rank sum test was performed, with FDR to adjust for multiple comparisons.
  • Morphological processes that occur during fungal growth are significantly enriched at both the 100% and 85% ERH conditions when compared to the low 50% ERH condition.
  • Filamentous fungi begin to grow by elongating the tip of their hyphae, which is followed by the formation of reproductive growth structures and the production of spores (sporulation).
  • Genes associated with the GO term “sporulation” were upregulated at the 100% and 85% ERH conditions when compared to 50% ERH.
  • GO terms associated with hyphal elongation such as “cell septum” and “hyphal tip” were significantly enriched at 85% ERH when compared to 50% (FDR ⁇ 10' 10 , Figure 5, Table 13).
  • GO terms associated with fungal secondary metabolism are significantly enriched at 100% and 85% ERH conditions when compared to 50% ERH (FDR ⁇ 0.05).
  • Secondary metabolic processes are chemical reactions and pathways that are not required for the growth and maintenance of the organism.
  • filamentous fungi manufactured
  • secondary metabolism includes the production of natural products such as pigments and harmful toxins such as mycotoxins and is often accompanied by fungal morphological growth and virulence.
  • Genes associated with fungal mycotoxin production belonging to GO terms such as “sterigmatocystin biosynthetic process” and “positive regulation of aflatoxin biosynthetic process,” were significantly upregulated at 100% and 85% ERH conditions when compared to 50% ERH (FDR ⁇ 0.05).
  • Hydrophobins, developmental regulators and secondary metabolite genes are consistently associated with moisture:
  • the most consistently upregulated genes at both 100% and 85% ERH included mitochondria related genes such as mdmlO showed upregulation at both 85% and 100% with log2FC 71.87 and was associated with the mitochondrial protein-containing complex” (GG:0098798) GO term.
  • the highly expressed fadA gene was associated with both morphological processes and secondary metabolic processes such as (log2FC 17.42) such as “sporulation” (G0:0043934) and “sterigmatocystin biosynthetic process” (G0:0045461).
  • the laeA gene additionally functions as a secondary metabolic gene and is associated with the GO term “sterigmatocystin biosynthetic process” (G0:0045461).
  • the fungal alkaline protease gene alpl (also known as the allergen Asp f 13 gene) was upregulated at 100% compared to 85% ERH and has strong correlations with asthma severity and respiratory dysfunction and potential functions in promoting fungal growth and infection development in the host.
  • Genes related to mitochondrial functions such as mdmlO, were upregulated at 85% ERH compared to 50% and have potential associations with fungal virulence by regulating stress responses and mediating morphogenetic transitions.
  • Targeting metabolic functions specific to high moisture conditions is a more robust approach than species-based indicators to identifying microbial indicators of moisture damage.
  • Targeting genes that are upregulated at both the 100% and 85% ERH conditions (compared to 50%) or using multiple genes where some are indicative of the 100% condition and others of 85%, may be better at detecting microbial changes at the onset of dampness.
  • a quantitative microbial indicator of moisture would, at minimum, need to be consistently upregulated in most (if not all) sampling sites at high ERH conditions, but not expressed at the low 50% condition.
  • Such a fungal target could be used in homes similarly to fecal indicators in water systems.
  • crAssphage is a human gut-associated bacteriophage can be used as a viral indicator of human fecal pollution and can potentially be quantitatively representative of viral pathogen fate and concentration changes in sewage-contaminated waters.
  • the target gene groups reported in the study can measure moisture and mold damage in homes and help correlate these measurements to occupant health exposure and outcomes in a quantitative manner. Ultimately, these targets can be integrated into standards and regulations.
  • Table 9 R 2 and p-values for statistical tests for gene expression and species abundances.
  • PERMANOVAs (adonis2) were performed for etermining significant differences in gene expression and species composition with ERH condition based on distance measures.
  • Kruskal-Wallis tests ere used for determining significant differences in the number of fungal annotated genes by ERH and fungal concentration based on ERH.
  • Significant -values (p ⁇ 0.05) are bolded.
  • Table 10 Differentially abundant species in each ERH condition. Only species that were significantly abundant were included (FDR-adjusted p ⁇ 0.05). The number of sites that a species was present at each ERH condition are also reported.
  • Table 12 Number of overall and fungal annotated (BLASTX) upregulated genes in each ERH comparison. Upregulated genes has a log2FC > 2 and were statisticlly significant (FDR-adjusted p value ⁇ 0.05). i . i mu x i Number of fungal
  • Table 14 Expression and functions of target genes identified in the study. Targets fell into 3 groups: 1. Upregulated at 100% ERH, 2. pregulated at 85% and 100% 3. 85% ERH. Target genes are significantly upregulated (log2FC > 5 FDR-adjusted p ⁇ 0.001) at 100% ERH or 85% ERH or both. Broad functional categories and GO terms associated with fungal growth are also reported.
  • TRINITY_DN3575_c0_gl CALX ASPFU Canx homolog Upregulated at 85% and 100% ERH 0
  • TRINITY_DN10372_c0_gl CRZA_ASPFU crzA Upregulated at 85% and 100% ERH 0
  • TRINITY_DN5762_cO_gl MDM10 EMENI mdmlO Upregulated at 85% and 100% ERH 0
  • TRINIT Y DN58717_c0_gl NDUS8_NEUCR nuo21.3c Upregulated at 85% and 100% ERH 0
  • TRINITY_DN11730_c0_gl TPIS EMENI tpiA Upregulated at 85% and 100% ERH 0
  • TRINITY_DN2841_cO_g2 CAN1C EMENI candA-C Upregulated at 85% ERH 0
  • TRINITY_DN21893_cO_g2 CAN1N_EMENI candA-N Upregulated at 85% ERH 0
  • TRINITY_DN9867_cO_g2 CAPZB ASPFU cap2 Upregulated at 85% ERH 0
  • TRINITY_DN1725_cO_gl CCG8_NEUCR ccg-8 Upregulated at 85% ERH 0
  • TRINITY_DN11446_c0_gl FLBA EMENI flbA Upregulated at 85% ERH 0
  • TRINITY_DN30562_c0_g6 SIDA ASPFU sidA Upregulated at 85% ERH 0
  • TRINITY_DN285_cO_g2 0 8 8.91 8.37E-12 100% vs 85%
  • TRINITY_DN3575_c0_gl 9 9 50.91 1.72E-05 100% vs 50%
  • TRINITY_DN8240_cl_g2 8 8 9.09 8.67E-05 100% vs 50%° TRINITY_DN5762_cO_gl 4 7 71.87 8.94E-08 100% vs 50%
  • TRINITY_DN2841_cO_g2 8 1 10.13 1.09E-12 85% vs 50%
  • TRINITY_DN1725_cO_gl 8 1 11.01 5.41E-14 85% vs 50%
  • TRINIT Y_DN2314_cO_g2 9 1 9.16 9.33E-11 85% vs 50%
  • TRINITY_DN48838_cO_gl 7 1 8.75 2.99E-09 85% vs 50%
  • TRINIT Y_DN219195_cO_gl 8 1 10.76 4.71E-12 85% vs 50%
  • TRINITY_DN16965_cO_gl 8 1 11.65 1.94E-16 85% vs 50%
  • TRINITY_DN10244_c0_gl 9 4 11.5 1.02E-16 85% vs 50%
  • TRINITY_DN13764_cO_gl 9 1 9.56 8.36E-15 85% vs 50%
  • TRINITY_DN3657_cO_gl 7 1 8.43 2.63E-10 85% vs 50%
  • TRINITY_DN22889_c0_g3 5 1 8.01 1.72E-06 85% vs 50%
  • Bettina NS Webster DG. Microbial indicators as a diagnostic tool for assessing water quality and climate stress in coral reef ecosystems. Marine Biology. 2017;164: 1-18.
  • Bope A Haines SR, Hegarty B, Weschler CJ, Peccia J, Dannemiller KC. Degradation of phthalate esters in floor dust at elevated relative humidity. Environ Sci Process Impacts. 2019;21 :1268-79.

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Abstract

La présente invention concerne des procédés pour inhiber ou réduire la croissance microbienne dans un environnement bâti, avec un environnement humide. L'invention concerne également un kit utilisé pour identifier et quantifier l'expression d'un ou de plusieurs gènes ou d'un ou de plusieurs produits de ceux-ci, d'un ou de plusieurs microbes dans au moins un échantillon provenant d'un environnement bâti.
PCT/US2024/025034 2023-04-17 2024-04-17 Procédés et compositions pour détecter une croissance microbienne dans des environnements bâtis Pending WO2024220571A2 (fr)

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