DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-15 and 17-21 are pending and under consideration in this action. Claim 16 was canceled in the amendment filed 11/17/2022.
Priority
The instant application is 371 of PCT/US2020/059502, filed 11/6/2020, which claims priority to U.S. Provisional Application number 62/932,128, filed 11/7/2019, as reflected in the filing receipt mailed 12/29/2022. The claim for domestic benefit for claims 1-15 and 17-21 is acknowledged. As such, the effective filing date of claims 1-15 and 17-21 is 11/7/2019.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/12/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner.
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
The domain substrate-binding residues in Fig. 13B are missing SEQ ID identifiers in accordance with 37 CFR 1.831(c). Applicant may remedy the deficiency by filing a replacement drawing for Figure 13B or by amending the short description of the drawings for Figure 13B to include the appropriate SEQ ID NO’s.
The Incorporation by Reference paragraph required by 37 CFR 1.821(c)(1) is missing or incomplete. Applicant may remedy the deficiency by including an Incorporation by Reference paragraph (See item 1(a) or 1(b) above).
Specification
The abstract of the disclosure is objected to because it is only one sentence and does not provide a complete description of the instant disclosure. The abstract should be within the range of 50 to 150 words in length and should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (Pg. 33, Line 17). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 13 and 19 are objected to because of the following informalities.
Claim 13 is missing a period at the end of the claim.
Claim 19 recites the phrase “generating grouping the BGCs into gene cluster families” which should be corrected to “grouping the BGCs into gene cluster families” for clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “responsible for the synthesis of metabolites” in line 7 of the claim. There is insufficient antecedent basis for this limitation in the claim, since there is no prior mention of this phrase earlier in the claim. This rejection can be overcome by amendment of claim 1 to recite “responsible for synthesis of metabolites”. Claims 2-15 are also rejected due to their dependency from claim 1.
Claim 14 recites the limitations “comparing the pairwise distances of BGCs or GCFs” and “with the pairwise distances of metabolite features or MFs” in lines 2 and 3 of the claim, respectively. There is insufficient antecedent basis for these limitations in the claim, since there is no prior mention of these phrases in claim 1, to which this claim depends. This rejection can be overcome by amendment of claim 14 to recite “comparing pairwise distances of BGCs or GCFs” and “with pairwise distances of metabolite features or MFs”.
Claim 15 recites the limitations “comparing the frequency of BGCs or GCFs” and “with the frequency of metabolite features or MFs” in lines 2 and 3 of the claim, respectively. There is insufficient antecedent basis for these limitations in the claim, since there is no prior mention of these phrases in claim 1, to which this claim depends. This rejection can be overcome by amendment of claim 15 to recite “comparing a frequency of BGCs or GCFs” and “with a frequency of metabolite features or MFs”.
Claims 17 and 20 recites the limitation “to determine the degree of relatedness” in lines 7 and 5 of the claims, respectively. There is insufficient antecedent basis for this limitation in the claims, since there is no prior mention of this phrase earlier in the claims. This rejection can be overcome by amendment of claim 17 and 20 to recite “to determine a degree of relatedness”. Claims 18-19 and 21 are also rejected due to their dependency from claims 17 and 20.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-15 and 17-21 are directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mental process (i.e., an evaluation/comparison of two networks) in “comparing the network of BGCs and network of metabolites to link particular mass spectrometric features with the BGCs responsible for the synthesis of metabolites that correspond to the particular mass spectrometric features”.
Claim 2 recites a mental process (i.e., an observation/evaluation of the content of the sequences) in “wherein the genomic sequences from multiple strains of fungi comprise 100 or more full or partial genomic sequences”.
Claim 3 recites a mental process (i.e., an observation/evaluation of the content of the sequences) in “wherein the genomic sequences from multiple strains of fungi comprise full or partial genomic sequences from 100 or more strains of fungi”.
Claim 4 recites a mental process (i.e., an observation/evaluation of the content of the sequences) in “wherein the genomic sequences from multiple strains of fungi comprise full or partial genomic sequences from 100 or more species of fungi”.
Claim 5 recites a mental process (i.e., an evaluation of the genomic sequences) in “wherein analyzing genomic sequences from multiple strains of fungi comprises identifying BGCs with the genomic sequences”.
Claim 6 recites a mental process (i.e., an evaluation of sequences to group them into gene cluster families) in “wherein analyzing genomic sequences from multiple strains of fungi comprises grouping BGCs with the genomic sequences into gene cluster families (GCFs)”.
Claim 7 recites a mental process (i.e., a pairwise evaluation of sequence and predicted structural features) in “wherein analyzing genomic sequences from multiple strains of fungi is based on pairwise comparisons of sequence and predicted structural features of the BGCs”.
Claim 8 recites a mental process (i.e., an observation/evaluation of the number of mass spectra) in “wherein the mass spectra of extracts from multiple strains of fungi comprise 100 or more mass spectra”.
Claim 9 recites a mental process (i.e., an observation/evaluation of the number of mass spectra from different fungi strains) in “wherein the mass spectra of extracts from multiple strains of fungi comprise mass spectra from 100 or more strains of fungi”.
Claim 10 recites a mental process (i.e., an observation/evaluation of the number of mass spectra from different fungi species) in “wherein the mass spectra of extracts from multiple strains of fungi comprise mass spectra from 100 or more species of fungi”.
Claim 11 recites a mental process (i.e., an evaluation of a mass spectra to identify a feature) in “wherein analyzing mass spectra of extracts from multiple strains of fungi comprises identifying mass spectrometric features with the mass spectra”.
Claim 12 recites a mental process (i.e., an evaluation of mass spec features to group them into molecular families) in “wherein analyzing mass spectra of extracts from multiple strains of fungi comprises grouping mass spectrometric features with the mass spectra into molecular families (MFs)”.
Claim 13 recites a mental process (i.e., a pairwise evaluation of mass spec features) in “wherein analyzing mass spectra of extracts from multiple strains of fungi is based on pairwise comparisons of mass spectrometric features of the mass spectra”.
Claim 14 recites a mental process (i.e., a pairwise evaluation of to determine correlations) in “wherein comparing the network of BGCs and network of metabolite features comprises comparing the pairwise distances of BGCs or GCFs within the BGC network with the pairwise distances of metabolite features or MFs within the metabolite feature network to identify correlations that indicate that a BGC or GCF is responsible for the synthesis of a metabolite feature or MF”.
Claim 15 recites a mental process (i.e., an evaluation of the frequency of BGCs, GCFs, metabolite features, or MFs to determine a correlation) in “wherein comparing the network of BGCs and network of metabolite features comprises comparing the frequency of BGCs or GCFs within the BGC network with the frequency of metabolite features or MFs within the metabolite feature network to identify correlations that indicate that a BGC or GCF is responsible for the synthesis of a metabolite feature or MF.”
Claim 17 recites a mental process (i.e., an evaluation of a sequence to identify a BGC) in “identifying biosynthetic gene clusters (BGCs) within genomic sequences from multiple strains of fungi”; a mental process (i.e., an evaluation of the BCG to determine sequence or structural characteristics) in “identifying sequence characteristics and predicted structural domains within the BGCs”; and a mental process (i.e., an evaluation/comparison of pairs to determine relatedness) in “comparing the sequence characteristics and predicted structural domains between multiple pairs of BGCs to determine the degree of relatedness between the pairs of BGCs”.
Claim 19 recites a mental process (i.e., an evaluation of the degree of relatedness to group BCGs) in “generating grouping the BGCs into gene cluster families based on the degree of relatedness between the pairs of BGCs”.
Claim 20 recites a mental process (i.e., an evaluation of a mass spec to determine a feature) in “identifying mass spectrometric features within mass spectra of extracts from multiple strains of fungi”; and a mental process (i.e., an evaluation / comparison of pairs to determine relatedness) in “comparing characteristics of the mass spectrometric features between multiple pairs of mass spectrometric features to determine the degree of relatedness between the pairs of mass spectrometric features”.
Claim 21 recites a mental process (i.e., an evaluation of the degree of relatedness to group mass spec features) in “grouping the mass spectrometric features into molecular families based on the degree of relatedness between the pairs of mass spectrometric features”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following claims recite limitations that equate to additional elements:
Claim 1 recites “analyzing genomic sequences from multiple strains of fungi to generate a network of biosynthetic gene clusters (BGCs)” and “analyzing mass spectra of extracts from multiple strains of fungi to generate a network of metabolite features”.
Claim 18 further recites “generating a network of BCGs based on the degree of relatedness between the pairs of BGCs”.
Claim 20 recites “generating a network of mass spectrometric features based on the degree of relatedness between the pairs of mass spectrometric features”.
Regarding the above cited limitations in claims 1, 18, and 20 of (i) analyzing genomic sequences from multiple strains of fungi to generate a network of biosynthetic gene clusters (BGCs) (claim 1); (ii) analyzing mass spectra of extracts from multiple strains of fungi to generate a network of metabolite features (claim 1); (iii) generating a network of BCGs based on the degree of relatedness between the pairs of BGCs (clam 18); and (iv) generating a network of mass spectrometric features based on the degree of relatedness between the pairs of mass spectrometric features (claim 20). These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of comparing the network of BGCs and network of metabolites (claim 1); comparing the sequence characteristics and predicted structural domains between multiple pairs of BGCs (claim 17); or comparing characteristics of the mass spectrometric features between multiple pairs of mass spectrometric features (claim 20) (see MPEP § 2106.04(d)). As such, claims 1-15 and 17-21 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 1, 18, and 20 of (i) analyzing genomic sequences from multiple strains of fungi to generate a network of biosynthetic gene clusters (BGCs) (claim 1); (ii) analyzing mass spectra of extracts from multiple strains of fungi to generate a network of metabolite features (claim 1); (iii) generating a network of BCGs based on the degree of relatedness between the pairs of BGCs (claim 18); and (iv) generating a network of mass spectrometric features based on the degree of relatedness between the pairs of mass spectrometric features (claim 20). These limitations when viewed individually and in combination, are WURC limitations as taught by Kelleher et al. (U.S. Patent Application Publication, US 2017/0335335 A1) and Doroghazi et al. (A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat Chem Biol. 10(11): 963-968 (2014)). Kelleher et al. discloses the generation of networks for related metabolites based on fingerprints from tandem mass spectrometry analysis for secondary metabolites extracted from several strains of fungi (limitations (ii) and (iv)) (Para. [0036], [0052], and [0099]). Kelleher et al. further discloses the generation of a network of biosynthetic gene clusters (BGCs) from sequencing and systematic screening of gene clusters of several types of fungi (limitation (i)) (Para. [0043] and [0101]). Doroghazi et al. discloses a method of generating a network of BCGs based on three similarity measurements that were calculated for every natural product biosynthetic gene cluster pair (limitation (iii)) (Pg. 963, Col. 2, Para. 2 and Pg. 964, Col. 1, Para. 1).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-15 and 17-21 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 and 8-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kelleher et al. (U.S. Patent Application Publication, US 2017/0335335 A1; published 11/23/2017; provided in the IDS dated 7/12/2023).
Regarding claim 1, Kelleher et al. teaches a method for the detection of secondary metabolites and the correlation of such metabolites to the biosynthetic gene clusters responsible for the biosynthesis thereof (Abstract). Kelleher et al. further teaches that sequencing efforts uncovered thousands of biosynthetic gene clusters (BGCs) (Para. [0043]). Kelleher et al. further teaches that experiments were conducted during development of embodiments herein 58 fungal artificial chromosomes (FACs) comprising putative biosynthetic gene clusters (pBGCs) from Talaromyces marneffei, Fusarium solani, Pseudogymnoascus destructans, and Penicillium expansum using the LC-MS screening, FAC-score analysis, and deletion validation. Metabolites were identified and pBGCs confirmed as being BGCs from F. solani, P. expansum, and T marneffei. This analysis yielded an approximately 9% hit rate (Para. [0101]). These systems and method provide the systemic screening of large numbers (e.g., 1000, 2000, 5000, 10,000 or more) of gene clusters with a reliable hit rate (i.e., analyzing genomic sequences from multiple strains of fungi to generate a network of biosynthetic gene clusters (BCGs)) (Para. [0043]). Kelleher et al. further teaches that a tandem mass spectrometry analysis (MS2 analysis) involves first measurement of the intact mass to charge ratio of a metabolite. Once an intact mass measurement is made, an ion is isolated and collided with a gas to cause it to fragment. The way an ion fragments yields its "MS2 fingerprint." The MS2 fingerprints are compared and organized into clusters of related metabolites that can be visualized as a network (i.e., analyzing mass spectra to generate a network of metabolite features) (Para. [0099]). Kelleher et al. further teaches that secondary metabolites were extracted from several strains of fungi (i.e., analyzing extracts from multiple strains of fungi) (Para. [0036] and [0052]). Kelleher et al. further teaches that systems and methods are provided for the detection of secondary metabolites and the correlation of such metabolites to the biosynthetic gene clusters responsible for the biosynthesis thereof (Para. [0007]). Kelleher et al. further teaches that network analysis provides for identification of highly unique and abundant metabolites produced by various BGCs analyzed (Para. [0100]). Kelleher et al. further teaches that the method includes the steps of: (a) screening a sample a sample produced by expressing a pBGC in a host system for metabolites, wherein the sample is screened by one or more bioanalytical techniques (i.e., mass spectrometry); (b) scoring metabolites detected by the screen based on a combination of uniqueness and abundance; and (c) identifying the pBGC as a biosynthetic gene cluster (BGC), and/or the particular metabolite as being produced by the pBGC, if the scoring identifies a particular metabolite as being highly unique and abundant relative to other scored metabolites (i.e., comparing the network of BGCs and network of metabolites to link particular mass spectrometric features with the BGCs responsible for the synthesis of metabolites that correspond to the particular mass spectrometric features) (Claim 19).
Regarding claim 2, Kelleher et al. teaches that screening was performed using library sizes of up to 1,000, corresponding to the scale of the Department of Energy 1,000 fungal genomes project (i.e., wherein the genomic sequences from multiple strains of fungi comprise 100 or more full or partial genomic sequences) (Para. [0017]).
Regarding claim 3, Kelleher et al. teaches the analysis of genomic sequences as described for claim 1 above. Kelleher et al. further teaches that the technology can be extended across numerous strain collections (for example, 100 or 1,000 fungal strains as shown in Fig. 4C) (i.e., wherein the genomic sequences from multiple strains of fungi comprise full or partial genomic sequences from 100 or more strains of fungi) (Para. [0045] and Fig. 4C).
Regarding claim 4, Kelleher et al. teaches the analysis of genomic sequences as described for claim 1 above. Kelleher et al. further teaches that using the technology across numerous strain collections would be expected to reveal a large number of unknown metabolites. For instance, estimating an average of 50 BGCs per 100 Aspergillus species (the genus contains several hundred species) with a hit rate of 30% (as observed in the experiments conducted during development of embodiments herein) would yield about 1,500 products and correlated BGCs from this genus alone (i.e., wherein the genomic sequences from multiple strains of fungi comprise full or partial genomic sequences from 100 or more species of fungi) (Para. [0045]).
Regarding claim 5, Kelleher et al. teaches that the FAC libraries were screened by FAC-end-sequencing and reference genome alignment, leading to confirmation of 156 FACs, each encoding a predicted BGC of A. wentii, A. terreus, or A. aculeatus (note that each organism has a sequenced or reference genome available) (i.e., wherein analyzing genomic sequences from multiple strains of fungi comprises identifying BGCs with the genomic sequences) (Para. [0084]).
Regarding claim 8, Kelleher et al. teaches that the networks have been successfully created from greater than 1,000 mass spectrometry files from multiple years of FAC-MS data collection (i.e., wherein the mass spectra of extracts from multiple strains of fungi comprise 100 or more mass spectra) (Para. [0099]).
Regarding claim 9, Kelleher et al. teaches that 100 or more strains of fungi were used for analysis as described for claim 3 above. Kelleher et al. further teaches that the that the networks have been successfully created from greater than 1,000 mass spectrometry files from multiple years of FAC-MS data collection, including every FAC expression strain analyzed during development herein (i.e., wherein the mass spectra of extracts from multiple strains of fungi comprise mass spectra from 100 or more strains of fungi) (Para. [0099]).
Regarding claim 10, Kelleher et al. teaches that 100 or more strains of fungi were analyzed as described for claim 4 above. Kelleher et al. further teaches that the networks have been successfully created from greater than 1,000 mass spectrometry files from multiple years of FAC-MS data collection (i.e., wherein the mass spectra of extracts from multiple strains of fungi comprise mass spectra from 100 or more species of fungi) (Para. [0099]).
Regarding claim 11, Kelleher et al. teaches the feature detection of untargeted metabolomic data. Chromatographic and m/z features were extracted and grouped using the open source metabolomics software XCMS, running in R. Generally, about 12,500 features were detected for each species, with the abundance of each feature in each extract and treatment being recoded (i.e., wherein analyzing mass spectra of extracts from multiple strains of fungi comprises identifying mass spectrometric features with the mass spectra) (Para. [0075]).
Regarding claim 12, Kelleher et al. teaches the potential structures of ophiobolin-like compounds with m/z=369.2776 in Fig. 12. One compound in the molecular family is Ophiobolin H, which was identified by exact mass and clustered with the ophiobolin-like compound (Fig. 12B and Para. [0025]). The molecular family of the ophiobolin-like compound is also shown in Fig. 13C (i.e., wherein analyzing mass spectra of extracts from multiple strains of fungi comprises grouping mass spectrometric features with the mass spectra into molecular families (MFs)).
Regarding claim 13, Kelleher et al. teaches that the metabolite extracts are analyzed for unique and/or abundant metabolites in comparison to other samples (e.g., extracts from other species/strains, etc.) and/or controls. (Para. [0052]). Kelleher et al. further teaches that methods are provided for the analysis of mass spectra to identify unique and/or abundant molecular species within a test sample in comparison to other test samples and/or control samples. In some embodiments, m/z features are extracted from mass spectra (i.e., wherein analyzing mass spectra of extracts from multiple strains of fungi is based on pairwise comparisons of mass spectrometric features of the mass spectra) (Para. [0062]-[0063]).
Therefore, Kelleher et al. teaches all the limitations in claims 1-5 and 8-13.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6-7 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kelleher et al. as applied to claims 1-5 and 8-13 above, and further in view of Doroghazi et al. (A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat Chem Biol. 10(11): 963-968 (2014); published 9/28/2014; provided in the IDS dated 7/12/2023).
Regarding claim 17, Kelleher et al. teaches that the sequencing efforts uncovered thousands of biosynthetic gene clusters (BGCs) (Para. [0043]). Kelleher et al. further teaches that experiments were conducted during development of embodiments herein 58 FACs from comprising pBGCs from Talaromyces marneffei, Fusarium solani, Pseudogymnoascus destructans, and Penicillium expansum using the LC-MS screening, FAC-score analysis, and deletion validation. Metabolites were identified and pBGCs confirmed as being BGCs from F. solani, P. expansum, and T marneffei. This analysis yielded an approximately 9% hit rate (i.e., identifying biosynthetic gene clusters (BGCs) within genomic sequences from multiple strains of fungi) (Para. [0101]). Kelleher et al. further teaches that the AtFAC9J20 construct was sequenced to identify genes involved in benzomalvin production. Interestingly, 17 ORFs, eight of which are missing in the genome data of A. terreus NIH 2624, were detected. The eight unique ORFs included two predicted NRPS enzymes, benY and benZ, with domain structures of A-T-C and A1-T1-C1-A2-T2-C2, respectively. The extracted 10 amino acid A domain signatures for both the A domain of benY and the A1 domain of benZ match the distinct signature expected for Anth-encoding A-domains. The A2 domain of benZ is predicted to encode a Phe, NmPhe, or Tyr residue. Additionally, ORFs encoding a predicted PKS, a third NRPS enzyme, and an isoprenoid synthase enzyme were found on the FAC and annotated (i.e., identifying sequence characteristics and predicted structural domains within the BGCs) (Para. [0079]).
Kelleher et al., as applied to claims 1-5 and 8-13 above, does not teach grouping BGCs with the genomic sequences into gene cluster families (GCFs); wherein analyzing genomic sequences from multiple strains of fungi is based on pairwise comparisons of sequence and predicted structural features of the BGCs; comparing the sequence characteristics and predicted structural domains between multiple pairs of BGCs to determine the degree of relatedness between the pairs of BGCs; generating a network of BGCs based on the degree of relatedness between the pairs of BGCs; and generating grouping the BGCs into gene cluster families based on the degree of relatedness between the pairs of BGCs.
Regarding claim 6, Doroghazi et al. teaches a systematic bioinformatics framework for the study of natural product gene clusters. They used MS data to verify gene cluster family designations and demonstrate utility for de novo correlation of natural products and biosynthetic genes (Pg. 963, Col. 1, Para. 1). Doroghazi et al. further teaches the creation and analysis of a GCF network. To do this, they created a data set comprising natural product biosynthetic gene clusters (NPGCs) in the synthesis of nonribosomal peptides, type I and type II polyketides, NRPS-independent siderophores, lanthipeptides and thiazole-oxazole modified microcins. Three distance metrics were calculated for every NPGC pair: (i) the number of homologous genes shared; (ii) the proportion of nucleotides involved in a pairwise alignment; and (iii) the amino acid sequence identity between the domains of repeated protein modules (Pg. 963, Col. 2, Para 2). The GCF network created using these methods from the 830 actinobacterial genomes comprised 140,986 genes from 11,422 NPGCs grouped into 4,122 GCFs (i.e., wherein analyzing genomic sequences from multiple strains of fungi comprises grouping BGCs with genomic sequences into gene cluster families (GCFs)) (Pg. 964, Col. 2, Para. 2)
Regarding claim 7, Doroghazi et al. teaches that three distance metrics were calculated for every NPGC pair, as described for claim 6 above (i.e., wherein analyzing genomic sequences is based on pairwise comparisons of sequence and predicted structural features of the BGCs) (Pg 963, Col. 2, Para. 2 and Pg. 964, Fig. 1A).
Regarding claim 17, Doroghazi et al. teaches that gene cluster families were created using three similarity measurements (i.e., the degree of relatedness) (Online Methods, Pg. 1, Col. 1, Para. 3). Three distance metrics were calculated for every natural product biosynthetic gene cluster (NPGC) pair, as described for claim 6 above (i.e., comparing the sequence characteristics and predicted structural domains between multiple pairs of BGCs to determine the degree of relatedness between the pairs of BGCs) (Pg. 963, Col. 2, Para. 2 and Pg. 964, Fig. 1).
Regarding claim 18, Doroghazi et al. teaches that the combined score that incorporates all three similarity metrics produces coherent GCFs that include only highly related NPGCs, as assessed by manual inspection of the gene cluster diagrams and GCF network visualizations (i.e., generating a network of BGCs based on the degree of relatedness between the pairs of BGCs) (Pg. 964, Col. 1, Para. 1).
Regarding claim 19, Doroghazi et al. teaches that the combined similarity metric correctly grouped 103 characterized gene clusters into 41 GCFs that direct the synthesis of highly similar natural products (i.e., generating grouping the BGCs into gene cluster families based on the degree of relatedness between the pairs of BGCs) (Pg. 964, Col. 1, Para. 1).
Therefore, regarding claims 6-7 and 17-19, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting secondary metabolites and correlating those metabolites with a biosynthetic gene cluster of Kelleher et al. with the analysis of Doroghazi et al. because the ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules is an invaluable tool in aiding the discovery of biotechnologically applicable molecules (Doroghazi et al., Abstract). Additionally, the method of Doroghazi et al. also increases the speed and reduces the cost of the analysis of specialized metabolites from a large cohort of organisms using sequence information already available in public databases (Doroghazi et al., Pg. E2618, Col. 1, Para. 2 – Col. 2, Para. 1).One of ordinary skill in the art would be able to combine the teachings of Kelleher et al. with Doroghazi et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for correlating biosynthetic gene clusters with secondary metabolite production. Therefore, regarding claims 6-7 and 17-19, the instant invention is prima facie obvious (MPEP § 2142).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kelleher et al. as applied to claims 1-5 and 8-13 above, and further in view of Goering et al. (Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer. ACS Central Science. 2(2): 99-108 (2016); published 1/20/2016; provided in the IDS dated 7/12/2023).
Kelleher et al., as applied to claims 1-5 and 8-13 above, does not teach comparing the pairwise distances of BGCs or GCFs within the BGC network with the pairwise distances of metabolite features or MFs within the metabolite feature network to identify correlations that indicate that a BGC or GCF is responsible for the synthesis of a metabolite feature or MF; and comparing the frequency of BGCs or GCFs within the BGC network with the frequency of metabolite features or MFs within the metabolite feature network to identify correlations that indicate that a BGC or GCF is responsible for the synthesis of a metabolite feature or MF.
Regarding claim 14, Goering et al. teaches a workflow for a “metabologenomics” approach to natural products discovery. Using information obtained from interpreting 178 sequenced genomes into gene clusters and gene cluster families and from MS-based metabolomics of the same 178 strains with accurate mass, pairwise correlation yields scores that associate metabolites with their gene cluster families (i.e., wherein comparing the network of BGCs and network of metabolite features comprises comparing the pairwise distances of BGCs or GCFs within the BGC network with the pairwise distances of metabolite features or MFs within the metabolite feature network to identify correlations that indicate that a BGC or GCF is responsible for the synthesis of a metabolite feature or MF) (Pg. 100, Fig. 1).
Regarding claim 15, Goering et al. teaches that metabolite and gene cluster data were used as inputs for a simple binary correlation algorithm, and scores ranged from 0 to ∼300, with scores >200 considered confident based on the successful correlation of knowns in the data set. Among all the GCF/NP pairs, tambromycin was identified as an ion with m/z = 536.190 and was selected for further analysis based on its high raw correlation score of 229 with nonribosomal peptide synthetase (NRPS) GCF 519. This metabolite was expressed by six different strains in the data set that each encoded the same NRPS biosynthetic gene cluster. The m/z = 536.190 was detected in 7 of 9 strains containing a gene cluster from this GCF. Direct analysis of tandem mass spectra confirmed that the observed ion species represented the same secondary metabolite in all the strains in which it was detected (i.e., wherein comparing the network of BGCs and network of metabolite features comprises comparing the frequency of BGCs or GCFs within the BGC network with the frequency of metabolite features or MFs within the metabolite feature network to identify correlations that indicate that a BGC or GCF is responsible for the synthesis of a metabolite feature or MF) (Pg. 101, Col. 1, Para. 2 – Col. 2, Para. 1).
Therefore, regarding claims 14-15, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting secondary metabolites and correlating those metabolites with a biosynthetic gene cluster of Kelleher et al. with the analysis of Goering et al. because combining genome sequencing and automated gene cluster prediction with mass spectrometry based metabolomics enables discovery at a larger scale by using data from multiple organisms to identify the BGCs responsible for the biosynthesis of expressed metabolites. The method also makes it possible to identify small molecules with related biosynthesis and discover natural products that do not have well studied relatives (Goering et al., Pg. 99, Col. 2, Para. 2 – Pg. 100, Col. 1, Para. 1). One of ordinary skill in the art would be able to combine the teachings of Kelleher et al. with Goering et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for correlating biosynthetic gene clusters with metabolite production. Therefore, regarding claims 14-15, the instant invention is prima facie obvious (MPEP § 2142).
Claims 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kelleher et al. in view of Doroghazi et al. as applied to claims 6-7 and 17-19 above, and further in view of Nguyen et al. (MS/MS networking guided analysis of molecule and gene cluster families. Proc Natl Acad Sci USA. 110(28): E2611-2620 (2013); published 6/24/2013; provided in the IDS dated 7/12/2023).
Regarding claim 20, Kelleher et al. teaches the feature detection of untargeted metabolomic data. Chromatographic and m/z features were extracted and grouped using the open source metabolomics software XCMS, running in R. Generally, about 12,500 features were detected for each species, with the abundance of each feature in each extract and treatment being recoded (i.e., identifying mass spectrometric features within mass spectra of extracts) (Para. [0075]). Kelleher et al. further teaches that secondary metabolites were extracted from several strains of fungi (i.e., extracts from multiple strains of fungi) (Para. [0036] and [0052]).
Regarding claim 20, Doroghazi et al. teaches the use of MS data to verify gene cluster family designations and demonstrate utility for de novo correlation of natural products and biosynthetic genes (Pg. 963, Col. 2, Para. 1).
Kelleher et al. in view of Doroghazi et al., as applied to claims 6-7 and 17-19 above, does not teach comparing characteristics of the mass spectrometric features between multiple pairs of mass spectrometric features to determine the degree of relatedness between the pairs of mass spectrometric features; generating a network of mass spectrometric features based on the degree of relatedness between the pairs of mass spectrometric features; and grouping the mass spectrometric features into molecular families based on the degree of relatedness between the pairs of mass spectrometric features.
Regarding claim 20, Nguyen et al. teaches a method of correlating the production of specialized metabolites to the genetic capacity of the organism that produces such molecules, by matching molecular families (MFs) with gene cluster families (GCFs) using MS/MS networking and peptidogenomics (Abstract). Nguyen et al. further teaches that matching of MFs with GCFs of unsequenced microbes through association with sequenced genomes was accomplished by a four-step process. In the first step, fragmentation data for the molecules produced by these microbes were obtained for analysis by molecular MS/MS networking. They subjected 60 different strains of bacteria to nanoDESI analysis: there were 42 bacilli and 18 pseudomonads, and their resulting MS/MS spectra were networked and visualized. Such organization into networks enables the relationships between spectrally identical and related molecules to be mapped based on the spectral similarity of their MS/MS signatures (i.e., comparing characteristics of the mass spectrometric features between multiple pairs of mass spectrometric features to determine the degree of relatedness between the pairs of mass spectrometric features) (Pg. E2612, Col. 2, Para. 2). Nguyen et al. further teaches that an MS/MS cluster, where many nodes are connected by edges, indicates that many related molecules were observed, whereas an MS/MS cluster with few nodes may be a unique set of molecules with few alternative forms, which results in unique spectra. Furthermore, MS/MS networking enables the visualization of groups possessing unique spectral signatures that indicate that the molecules are distinct from the other molecules in a given dataset (Pg. E2612, Col. 2, Para. 2). An example molecular network is shown in Fig. 1B (i.e., generating a network of mass spectrometric features based on the degree of relatedness between the pairs of mass spectrometric features) (Pg. E2613, Fig. 1).
Regarding claim 21, Nguyen et al. teaches that MS/MS networking was used to generate de novo peptide sequences from nonribosomally synthesized peptides as well as their respective molecular families (MFs) (Pg. E2612, Col. 1, Para. 2). Nguyen et al. further teaches an example where there are 121 MS/MS clusters that contain three or more nodes of unique fragmentation patterns; these MS/MS clusters visualize individual MFs (i.e., grouping the mass spectrometric features into molecular families based on the degree of relatedness between the pairs of mass spectrometric features) (Pg. E2613, Col. 1, Para. 1 and Pg. E2613, Fig. 1).
Therefore, regarding claims 20-21, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of detecting secondary metabolites and correlating those metabolites with a biosynthetic gene cluster of Kelleher et al. in view of Doroghazi et al. with the teachings of Nguyen et al. because the method of Doroghazi et al. could be further strengthened by expanding to include MS/MS spectral networking (i.e., the method of Nguyen et al.) to constrain the analysis and also contribute to the discovery of additional natural products (Doroghazi et al., Pg. 967, Col. 2, Para. 2). One of ordinary skill in the art would be able to combine the teachings of Kelleher et al. in view of Doroghazi et al. with Nguyen et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for correlating biosynthetic gene clusters with metabolite production. Therefore, regarding claims 20-21, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
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