A trend analysis method based on identification results of microorganisms in production environments

Abstract

This invention provides a trend analysis method (MBE) based on the identification results of microorganisms in production environments. The MBE platform manages the metadata (including room, cleanroom level, sampling method, and planned category) and identification results of microbial samples detected in pharmaceutical, food, and cosmetic production environments. It aggregates data according to specified time units (year, month, day, and batch), analyzes the genus and species ratios of environmental microbial flora and the distribution of metadata within each classification within a given time range, and analyzes temporal changes in the flora from four perspectives: phenotypic classification, genus and species level, bacteria of concern, and community diversity. Frequency analysis is used to analyze the temporal and spatial distribution patterns of the flora, and multivariate statistical analysis is used to analyze correlations between different classifications and between different genera and species. This invention can help identify microorganisms that may have a significant impact on product quality and safety, assess and control microbial contamination risks, and ensure quality and safety.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics analysis, and primarily to a phenotypic database of microorganisms and a method for analyzing trends in microbial flora in production environments. Background Art

[0002] In pharmaceutical, food and other production environments, process control of microorganisms is an important part of current cGMP and quality risk management. Monitoring of environmental microorganisms requires not only control of the microbial load, but also identification of the types of microorganisms. The trend of isolated microbial species provides additional valuable information for the management and control of the production environment. For example, it can help confirm the common flora in the environment for risk management, discover microorganisms that appear repeatedly in specific areas, or reveal changes in the number of microorganisms of concern, thereby indirectly reflecting the stability of the production environment and the effectiveness of control measures. Therefore, the pharmacopoeia and relevant regulations also support the inclusion of identification requirements in environmental microbial monitoring to determine the type information of microorganisms and provide more effective data support for environmental management and control.

[0003] However, current pharmacopoeias and related regulations do not provide trend analysis methods for microbial species. Current trend analysis by manufacturers often relies primarily on load data, corresponding to monitoring technologies such as HPC and even ATP. Some commercial software, such as SPASS, MiniTab, and Excel, provide statistical models for trend analysis of microbial load data. However, these software programs also have some shortcomings. First, these software programs are not specifically designed for microbial monitoring data, and the applicability of the models has not been verified. Second, these commercial software programs only analyze quantity and cannot integrate trend analysis with microbial species data. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a trend analysis method based on the identification results of production environment microorganisms.

[0005] A trend analysis method based on the identification results of production environment microorganisms, the steps are as follows:

[0006] S1 summarizes the sampling metadata, identification results, and phenotypic characteristics of the strain to form a data record for each identification result;

[0007] S2 performs matrix transformation on the summary information obtained by S1, aggregates the data according to the specified time unit, and forms an aggregation matrix;

[0008] S3 performs genus / species-level proportion statistics on the aggregation matrix obtained in S2, and displays the top 10 species / genera in abundance;

[0009] S4 The aggregation matrix obtained in S2 is analyzed by source and visualized using a Sange diagram to show the proportional distribution relationship between different taxa and genera / species;

[0010] S5 uses the aggregate matrix obtained in S2 to analyze the trend of bacterial abundance over time and displays it in a bubble chart;

[0011] S6: Draw a time-varying trend analysis line graph for a single bacterial genus / species of concern, and calculate the warning limit and correction limit based on the model fitting;

[0012] S7 performs trend analysis on the aggregation matrix obtained in S2 according to phenotypic classification, calculates the average value, and displays it in the form of a line graph;

[0013] S8 uses the aggregation matrix obtained in S2 to calculate α-diversity indices such as the Shannon index, Simpson index, and coverage, and displays the temporal trends of these indices through line graphs;

[0014] S9 further converts the aggregation matrix obtained in S2 into detection frequency, analyzes the co-detection phenomenon of different bacterial genera between different classifications and the properties of their residence in the environment, and visualizes them through a collection diagram;

[0015] S10 further converts the aggregation matrix obtained in S2 into detection frequency, analyzes the continuous detection of different bacterial genera within the analysis time range and under the specified source, displays it through heat map, and identifies the three types of bacterial communities: core, persister, and transient;

[0016] S11 performs dimensionality reduction analysis on the aggregation matrix obtained in S2 through multivariate statistics to obtain the correlation and distance information between bacterial genera or species and between different classifications, and performs a visual display.

[0017] The data record input in step S1 is based on the identification result of a single colony or pure culture, which is expressed in Latin name. The sampling meta-information includes the sample information including the sampling point, clean room level, sampling method, plan category, and sampling time. The identification result information and the sampling meta-information are associated through the sample number, and the phenotypic information is obtained by querying the phenotypic database through the Latin name of the identification result.

[0018] In step S2, data is aggregated for different meta-information or phenotypic information classification fields using the dict and pandas functions in Python according to the specified time unit to generate an aggregation matrix of bacterial species abundance under different classifications.

[0019] In step S3, statistics are collected for the aggregate matrix according to all samples and by room, cleanroom grade, sampling method, and plan category. The top 10 bacterial genera / species with the highest abundance and their abundance and proportion under different classifications are displayed using a Nightingale rose diagram.

[0020] In step S5, the trend of bacterial abundance over time is analyzed, with time as the X-axis and bacterial genus / species as the Y-axis. The abundance and proportion of bacterial genus / species are mapped to the size and color of bubbles for visualization.

[0021] In step S6, a trend analysis line graph is drawn for a single genus / species of interest over time. Warning limits and correction limits need to be calculated. Warning limits and correction limits are obtained by using the fitdistr function to perform maximum likelihood fitting of the data distribution and the chi-square goodness of fit test using the chisq.test method. The optimal model distribution of the data is determined based on the p-value of the test and calculated using the QC_Lines or nptol.int method.

[0022] In step S7, trend analysis is performed according to phenotypic classification. It is necessary to classify and summarize the bacterial genera / species according to the phenotypic fields, calculate the mean, and draw a line graph of the change in phenotypic group abundance over time with time as the X-axis.

[0023] In step S8, the diversity index is calculated by using the R package vegan to calculate the community α-diversity index under the specified time unit and specified classification, including the Shannon index, Simpson index and Good's coverage, and their means, and visualized through a line graph.

[0024] In step S9, the genus / species abundance in the aggregation matrix was normalized to "detected" or "not detected", thereby converting the aggregation matrix into a detection frequency matrix. The pandas function was used to perform a set analysis of different classifications, and the R package VennDetail was used to obtain the common bacterial genera / species between different classifications and display them in a combination of graphs and tables.

[0025] In step S10, the aggregation matrix is converted into a frequency matrix as in step S9, with time as the X-axis and genus / species classification as the Y-axis. The abundance is mapped to color, and a heat map is used to display the temporal continuity of genus / species.

[0026] In step S11, the aggregation matrix was subjected to correlation analysis of bacterial genera / species using the cor function in the R package corrplot. The correlation between bacterial genera / species and the similarity between bacterial communities under different classifications were analyzed. The principal component analysis method was used to perform dimensionality reduction analysis on bacterial genera of different classifications. The R package FactoMineR was used to draw scatter plots and confidence ellipses for visualization and identify outliers.

[0027] Beneficial effects of the present invention:

[0028] Currently, pharmacopoeias and related regulations do not provide trend analysis methods for microbial species. Currently, trend analysis by manufacturers is often based on load data. Systems that only perform load analysis only provide results and changing trends for the total number of microorganisms, but cannot provide detailed information on the types of microorganisms. This lacks a specific assessment of potential risks and cannot provide detailed data for targeted control measures. The present invention provides a multi-dimensional and comprehensive analytical framework for trend analysis of microbial monitoring in pharmaceutical production environments that combines species information. The present invention provides a bacterial phenotypic classification database that provides important data for determining the risks and sources of microorganisms in pharmaceutical production environments. The present invention integrates detailed information on microbial species, numbers, and trend changes to help identify microorganisms that may have a significant impact on product quality and safety, and can help pharmaceutical companies better assess and control microbial contamination risks and ensure product quality and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of the present invention.

[0030] Figure 2 This is an example of input data summary table data.

[0031] Figure 3 is an example of aggregated data after the input data is processed.

[0032] Figure 4 This is a Nightingale rose chart of the top 10 fungal genera / species.

[0033] Figure 5 This is a Sankey diagram of the bacterial community composition distribution.

[0034] Figure 6 This is a bubble chart showing the changes in the top 10 bacterial genera / species over time.

[0035] Figure 7 A line chart showing trend analysis of bacteria of concern.

[0036] Figure 8 Line graph for phenotypic trend analysis.

[0037] Figure 9 Line graph for diversity index zone analysis.

[0038] Figure 10 A collection diagram for analyzing common and unique genera / species from different sources.

[0039] Figure 11 Heatmap for analysis of core, persister, and transient microbiota.

[0040] Figure 12 The PCA diagram for analyzing the distance between bacterial communities at different sites.

[0041] Figure 13 This is the genus / species correlation analysis. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other unless there is a conflict.

[0043] Example 1

[0044] This embodiment provides a method for analyzing bacterial flora trends based on the identification results of microorganisms collected in the production environment. Figure 1 , the steps include:

[0045] S1 Organize the summary table of identification results according to the sampling meta-information (including room, clean room level, sampling method, plan category) and the Latin name of the identification result, and query the phenotype database with the Latin name of the identification result to establish a summary table containing phenotype classification. Some data in the summary table are as follows Figure 2 shown.

[0046] S2 performs a matrix transformation on the summary information obtained in S1, aggregates the data according to the specified time unit, and aggregates the data to form an aggregate matrix containing species / genus abundance. This example aggregates the abundance at the genus level with the month as the time unit. The matrix format is as follows: the first column is the Latin name of the species genus, the first row is the classification information, including sources such as sedimentation bacteria, planktonic bacteria, PW, etc., and each cell value is the number of bacterial genera detected under a specific classification in that month. Figure 3 For example Figure 3 The number 44 in the fourth column of the second row indicates that species of the genus Afpia under the PW classification were detected 44 times in that month.

[0047] S3 aggregates the data from the summary information table obtained in S1 at the genus and species levels, calculates the proportion of the top 10 bacterial genera / species, and draws a Nightingale rose diagram for visualization ( Figure 4 This example shows the Latin names and proportions of the top 10 genera. For example, the top genus is Phyllobacterium, detected 46 times, accounting for 9.39% of the detections in this category. Species outside the top 10 are grouped in the "Other" category.

[0048] S4 aggregates the data from the summary table obtained in S1 by genus / species. Combined with the classification of the meta-information, this analysis analyzes the distribution of the multi-level meta-information and the summarized identification results. This visualization is done using the Python Sankey function to create a Sankey diagram. Rectangular boxes display the meta-information and species / genus values, with the box size representing the distribution of the number of species, and the lines connecting the boxes representing the flow relationship between the two variables. Figure 5 The data shows that Gram-negative bacteria primarily originate from PW and WFI, and originate from PW Station 2, Workshop 209, Workshop 701, and Workshop NXM. Furthermore, the specific genera comprising the Gram-negative and Gram-positive bacteria, as well as fungi, are shown. For example, the Gram-negative bacteria in the example are primarily composed of genera such as Phyllobacterium, Afipia, and Sphingomonas.

[0049] S5 aggregates the summary information table obtained in S1 by genus, species, and time unit (e.g., by month), with time as the x-axis and the top 10 genera / species as the y-axis. Map the detection abundance of each month to the size of the bubble for visualization ( Figure 6 ).For example Figure 6 Afipia was detected from March to July, with the highest detection rate in April, and the bubbles were also the largest and darkest. Staphylococcus was detected in July. Phyllobacterim showed a gradual increasing trend from January to June. The bubble chart can better display the changes in detection trends.

[0050] S6 Aggregate the bacteria of concern by genus, species and time unit (e.g., by month), plot the time trend of individual bacteria with time as the x-axis, and plot the warning limit and correction limit, marking the point where the limit is exceeded ( Figure 7 ). Figure 7 To isolate the Phyllobacterium genus, test results from January to December 2022 are summarized and plotted on a monthly basis, with the month plotted on the horizontal axis and the abundance of detected species plotted on the vertical axis. The yellow warning limit represents the upper limit of the 95th percentile interval calculated using the negative binomial distribution model. This chart identifies the month exceeding the warning limit as June 2022. Months exceeding the warning limit indicate a loss of control over the environmental microbiome, a disruption of environmental stability, and a time point requiring attention and action.

[0051] S7 summarizes the data obtained in S1 using phenotypic classification information and time units, visualizes the proportion of each component in the form of a line graph, and calculates the average proportion of each category, with arrows indicating the direction of change over time ( Figure 8 ). Figure 8Negative indicates Gram-negative bacteria, positive indicates Gram-positive bacteria, fungi indicate eukaryotic microorganisms such as molds and yeasts, and the rest that cannot be classified are unknown. The aggregation matrix containing the Latin names of the identification results is further classified and summarized according to the phenotype after querying the phenotype to obtain the number of microorganisms detected for the corresponding phenotype each month, and a line graph is drawn to analyze the trend of different phenotypes over time. For example Figure 8 The proportion of Gram-negative bacteria (blue broken line) continued to be higher than that of Gram-positive bacteria (red broken line) from March 2022 to June 2022. From September to December, the proportion of Gram-positive bacteria exceeded that of Gram-negative bacteria. In the clean room, the ratio of Gram-negative bacteria to Gram-positive bacteria reflects the quality of clean room control. Negative bacteria mean a higher risk of pathogenicity and a humid clean room environment.

[0052] S8 uses the matrix of genus, species and time unit (e.g., "monthly") to aggregate the summary information table obtained in S1 and calculate the Shannon index of the bacterial community for each month. The calculation formula is as follows: H = -Σ(p(i) * log(p(i))), where p(i) is the relative abundance of genus / species i in the microbial community. The Simpson index is calculated as: D = 1 -Σ(p(i) * p(i)), and the goods_coverage calculation method is 1-Σ(x==1) / -Σ(x), where x is the abundance of genus / species in the microbial community. Visualize the changing trend over the month in the form of a line graph ( Figure 9 ). Figure 9 The example shows that the Shannon index (red line) is between 2 and 5 from January to December, which is relatively stable. It shows a significant decline in August 2022, and goods_coverage also decreases at this time, indicating a decrease in diversity and coverage index due to the decrease in the number of samples.

[0053] S9 classifies the summary information table obtained in S1 according to different metadata (here taking the room field as an example), calculates the species / genus intersection between different classifications, and helps us understand the relationship between different groups. For example, the common and unique detected microorganisms between different cleanliness level areas, different rooms, and different sources are visualized in the form of a collection diagram ( Figure 10 In the graph, connected points below the x-axis indicate microorganisms commonly detected within these categories. For example, six microorganisms are unique to CXC, three microbial genera are shared only between CXC and DN, one microorganism is shared between CXC and LABQ, and one microorganism is detected in six areas: Workshop 209, QC2, CXC, NXM Workshop, DN, and R300PW Station 2. Ensemble analysis can reveal the widespread distribution of bacterial genera and the similarity of bacterial communities between different areas, thereby reflecting the contamination pathways.

[0054] S10 summarizes bacterial genera and species by month and categorizes the detected bacterial communities based on the distribution and continuity of detection time. Core bacterial communities are defined as genera that appear at more than 90% of the time points and are more than 80% continuous; persistent bacterial communities are defined as genera that appear at more than 60% of the time points and are more than 80% continuous; transient bacterial communities are defined as genera detected in addition to core and persistent bacterial communities. Detection status and genus types are visualized in the form of heat maps ( Figure 11 ). Figure 11 It shows that the Ralstonia genus was detected continuously from March to August 2022, accounting for 6 / 10*100%=60% of the months when samples were sent, and the detection time was 100% continuous, which meets the definition of persistent flora and is marked as "Persistent" on the far left of the heat map. The continuous detection of flora indicates that colonization has occurred in the environment, or the production environment has failed to resolve the source of contamination. Transient flora may be caused by accidental factors entering the system, not by system control problems. This analysis is used to distinguish between system problems and accidental factors.

[0055] S11 aggregates the summary information table obtained in S1 using genus, species, and time units (e.g., "monthly"), and further filters it based on the metadata. After classification and filtering, the data is reduced in dimension through principal component analysis and visualized in the form of scatter plots and confidence ellipses ( Figure 12 ). Figure 12 The Euclidean distances of bacterial communities from different sources (ID, PW, WFI, sedimentation bacteria, floating bacteria, bioindicators, and NA classifications) were analyzed, reflecting the differences in bacterial communities from different sources, as well as the differences in bacterial communities from different months from the same source. For example, the purple diamond-shaped letter ellipse representing sedimentation bacteria is small, indicating that there is little difference between months. However, there are two points on the vertical axis that are far away from the central point, indicating that there are two months with relatively large differences in bacterial community structure, suggesting the need for investigation of related environmental factors. In addition, the R package corrplot was used to analyze the similarity of bacterial genera / species detected between different months, sources, classifications, and environments. The correlation between different species / genera was analyzed by calculating the correlation coefficient, and a heat map of the correlation coefficient was drawn to display it ( Figure 13 ). Figure 13 The results showed that the correlation coefficients of Phyllobacterium, Pelomonas, Afipia, and Sphingomonas genera were close to 1, and the significance test was less than 0.001, indicating that the detection of these genera was significantly positively correlated, that is, they were detected together, representing the pattern of these bacterial communities in the environment, that is, they may coexist in biofilms or have common carriers. The information is used for inference in environmental pollution investigations.

[0056] The above description is only a preferred embodiment of the present invention and does not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A trend analysis method based on the identification results of production environment microorganisms, characterized in that: Here are the steps: S1 summarizes the sampling metadata, identification results, and phenotypic characteristics of the strain to form a data record for each identification result; S2 performs matrix transformation on the summary information obtained by S1, aggregates the data according to the specified time unit, and forms an aggregation matrix; S3 performs genus / species-level proportion statistics on the aggregation matrix obtained in S2, and displays the top 10 species / genera in abundance; S4 The aggregation matrix obtained in S2 is analyzed by source and visualized using a Sange diagram to show the proportional distribution relationship between different taxa and genera / species; S5 uses the aggregate matrix obtained in S2 to analyze the trend of bacterial abundance over time and displays it in a bubble chart; S6: Draw a time-varying trend analysis line graph for a single bacterial genus / species of concern, and calculate the warning limit and correction limit based on the model fitting; S7 performs trend analysis on the aggregation matrix obtained in S2 according to phenotypic classification, calculates the average value, and displays it in the form of a line graph; S8 calculates the Shannon index, Simpson index and coverage of the aggregation matrix obtained in S2, and displays the trend of these indices over time through a line graph; S9 further converts the aggregation matrix obtained in S2 into detection frequency, analyzes the co-detection phenomenon of different bacterial genera between different classifications and the properties of their residence in the environment, and visualizes them through a collection diagram; S10 further converts the aggregation matrix obtained in S2 into detection frequency, analyzes the continuous detection of different bacterial genera within the analysis time range and under the specified source, displays it through heat map, and identifies the three types of bacterial communities: core, persister, and transient; S11 performs dimensionality reduction analysis on the aggregation matrix obtained in S2 through multivariate statistics to obtain the correlation and distance information between bacterial genera or species and between different classifications, and performs a visual display; The data record input in step S1 is based on the identification result of a single colony or pure culture. The result is expressed in Latin name. The sampling metadata includes the sample information including the sampling point, clean room level, sampling method, plan category, and sampling time. The identification result information and sampling metadata are linked by the sample number. The phenotypic information is obtained by querying the phenotypic database using the Latin name of the identification result. In step S2, data is aggregated using the dict and pandas functions in Python for different meta-information or phenotypic information classification fields according to the specified time unit to generate an aggregation matrix of bacterial species abundance under different classifications; In step S3, statistics are collected on the aggregate matrix based on all samples and by room, cleanroom grade, sampling method, and plan category. The top 10 bacterial genera / species with the highest abundance and their abundance and proportion under different categories are displayed using a Nightingale rose diagram. In step S5, the trend of bacterial abundance over time is analyzed, with time as the X-axis and bacterial genus / species as the Y-axis. The abundance and proportion of bacterial genus / species are mapped to the size and color of bubbles for visualization; In step S6, a time-varying trend analysis line graph is plotted for each genus / species of interest. Warning and correction limits need to be calculated. These limits are calculated by performing a maximum likelihood fit of the data distribution using the fitdistr function and performing a chi-square goodness-of-fit test using the chisq.test method. The p-value of the test is used to determine the optimal model distribution for the data. These limits are calculated using the QC_Lines or nptol.int method. In step S7, trend analysis is performed based on phenotypic classification. It is necessary to classify and summarize the bacterial genera / species according to the phenotypic fields, calculate the mean, and draw a line graph of the phenotypic group abundance over time with time as the X-axis; In step S8, the diversity index is calculated by using the R package vegan to calculate the community α-diversity index under the specified time unit and specified classification, including the Shannon index, Simpson index and Good's coverage, and their means, and visualized through a line graph; In step S9, the abundance of genus / species in the aggregation matrix was normalized to "detected" or "not detected", thereby converting the aggregation matrix into a detection frequency matrix. The pandas function was used to perform a set analysis of different classifications. The R package VennDetail was used to obtain the common bacterial genera / species between different classifications and present them in a combination of graphs and tables. In step S10, the aggregation matrix is converted into a frequency matrix as in step S9, with time as the X-axis and the genus / species classification as the Y-axis. The abundance is mapped to color, and a heat map is used to display the temporal continuity of the genus / species. In step S11, the aggregation matrix is subjected to correlation analysis on the genus / species using the cor function in the R package corrplot. The correlation between genera / species and the similarity between bacterial communities under different classifications are analyzed. The principal component analysis method is used to perform dimensionality reduction analysis on the genera of different classifications, and the R package FactoMineR is used to draw scatter plots and confidence ellipses for visualization to identify outliers.

Images