current research topics in applied microbiology and microbial biotechnology

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Applied microbiology articles from across Nature Portfolio

Applied microbiology is a scientific discipline that deals with the application of microorganisms and the knowledge about them. Applications include biotechnology, agriculture, medicine, food microbiology and bioremediation.

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Engineered probiotic Escherichia coli elicits immediate and long-term protection against influenza A virus in mice

Influenza virus infection is a global health threat and vaccines are required that show broad protection against a range of viral subtypes. Here the authors present a universal influenza vaccine based on Escherichia coli Nissle 1917 that activates innate and adaptive humoral and mucosal immunity, providing both immediate and long-term protection against influenza A virus infection in a murine model.

What is holding back cyanobacterial research and applications? A survey of the cyanobacterial research community

Cyanobacteria have been the subject of intense basic research, but translation towards industrial applications remains limited. Here, Schmelling and Bross conduct a survey among researchers in the cyanobacterial community, as well as a literature analysis, to highlight potential strategies to enhance cyanobacterial research and accelerate the development of industrial applications.

• Nicolas M. Schmelling
• Moritz Bross

Engineering new-to-nature biochemical conversions by combining fermentative metabolism with respiratory modules

The need for redox balancing limits the array of fermentable substrate-product combinations in anaerobic microbe-based bioproduction. Here, the authors design and engineer an E. coli strain with new-to-nature aerobic fermentative metabolism that allows tightly controlled re-balanced fermentations.

• Helena Schulz-Mirbach
• Jan Lukas Krüsemann
• Steffen N. Lindner

Aspergillus cvjetkovicii protects against phytopathogens through interspecies chemical signalling in the phyllosphere

Beneficial Aspergillus cvjetkovicii protects host plants against fungal diseases by inactivating pathogenicity-related gene transcription of phytopathogens via 2,4-DTBP signalling.

• Xiaoyan Fan
• Haruna Matsumoto
• Mengcen Wang

Combining Bifidobacterium longum subsp . infantis and human milk oligosaccharides synergistically increases short chain fatty acid production ex vivo

An ex vivo study shows that B. infantis synergistically increases short chain fatty acid production when combined with a mix of HMOs. Where HMOs were partially consumed by fecal background microbiota, full consumption was achieved upon B. infantis supplementation.

• Florac De Bruyn
• Kieran James
• Johnson Katja

Superiority of native soil core microbiomes in supporting plant growth

Native core microbiomes are often neglected when developing synthetic microbial communities to support plant health and growth. Here, the authors show that native core microorganisms have greater potential to support plant growth than both native non-core and non-native microorganisms.

• Yanyan Zhou
• Donghui Liu
• Xiaogang Li

News and Comment

Autotrophic yeast

Yeast is a widely used cell factory for the conversion of sugar into fuels, chemicals and pharmaceuticals. Establishing yeast as being autotrophic can enable it to grow solely on CO 2 and light, and hereby yeast can be used as a wider platform for transition to a sustainable society.

• Jens Nielsen

Bacteria curb emissions in farmland

In this study, Hiis et al. demonstrate that the N 2 O-respiring bacterial strain Cloacibacterium sp. CB-01 can reduce nitrous oxide emissions from soil.

• Agustina Taglialegna

Decomposer communities are universal in death

Decomposer microbiomes are universal across cadavers regardless of environmental conditions, and they use complex cross-feeding and interkingdom interactions to break down organic matter.

• Michael S. Strickland
• Laurel Lynch

Cutibacterium improves skin condition

In this study, Knödlseder et al. explore the potential of an engineered Cutibacterium acnes strain as a microbiome-based therapy for skin conditions.

QurvE: user-friendly software for the analysis of biological growth and fluorescence data

• Nicolas T. Wirth
• Jonathan Funk
• Pablo I. Nikel

Insulin as a catalyst to recombinant DNA technology

Scientific research on human insulin was a crucial development in medicine, and its discovery led to the treatment of diabetes, one of the most prevalent global chronic diseases. A seminal work published in 1979 describing recombinant DNA technology to produce human insulin through biocatalysis has resulted in this field’s establishment and routine industrial applications.

• Thavendran Govender
• Tricia Naicker

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• DOI: 10.1142/7133
• Corpus ID: 90418691

CURRENT RESEARCH TOPICS IN APPLIED MICROBIOLOGY AND MICROBIAL BIOTECHNOLOGY

• A. Heras , C. Vázquez , +2 authors M. González-Jaén
• Published 2009
• Biology, Environmental Science

33 Citations

Erwinia aphidicola isolated from commercial bean seeds (phaseolus vulgaris).

• Highly Influenced

Biogas Production System as an “Upcycler”

Microbial volatilome in food safety. current status and perspectives in the biocontrol of mycotoxigenic fungi and their metabolites, chitosan and chitin production and extraction in isolates of cunninghamella sp., natural biostimulants elicit plant immune system in an integrated management strategy of the postharvest green mold of orange fruits incited by penicillium digitatum, molecular characterization of salmonella species isolates from some hospitals in jos, nigeria, inactivation efficacies and mechanisms of gas plasma and plasma-activated water against aspergillus flavus spores and biofilms: a comparative study, medfly ceratitis capitata as potential vector for fire blight pathogen erwinia amylovora: survival and transmission, photosensitisers – the progression from photodynamic therapy to anti-infective surfaces, effects of light and circadian clock on growth and chlorophyll accumulation of nannochloropsis gaditana, related papers.

Showing 1 through 3 of 0 Related Papers

• Current research topics in app...

Current research topics in applied microbiology and microbial biotechnology : proceedings of the II International Conference on Environmental, Industrial and Applied Microbiology (BioMicroWorld2007) /

This book contains a compilation of papers presented at the II International Conference on Environmental, Industrial and Applied Microbiology (BioMicroWorld2007) held in Seville, Spain on 28 November - 1 December 2007, where over 550 researchers from about 60 countries attended and presented their c...

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• Fermentation microbiology and biotechnology / Published: (2007)
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• Applied microbiology and biotechnology.

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• v.15(1); 2022 Jan

The future of Microbial Biotechnology

Lawrence p. wackett.

1 University of Minnesota, St. Paul MN, 55108 USA

Great strides have been made with regard to the one gene‐one enzyme‐one function paradigm in microorganisms. Indeed, biotechnology largely grew up on single gene cloning and overexpression – think insulin, erythropoietin or proteins rendering plants resistant to herbicides. This has been a lucrative enterprise. Recombinant human insulin, produced in microbial hosts, is medically superior to animal‐derived insulin, where sequence differences could cause undesirable immunoresponses.

Since those times, the synthesis and expression of multiple genes and functions has become readily attainable. The price of DNA synthesis is dropping considerably and new research advances in the gene synthesis industry will lower costs further (Eisenstein, 2021 ). This will allow the synthesis of artificial operons and other larger units of genomes. Gene multiplexing methods provide for laboratory evolution via combinatorial gene mixing. This has opened the door to ambitious genomic engineering projects.

To accomplish this broader vision of multifunctional engineering, there must be a corresponding revolution in better understanding gene interplay for phenotypes involving multiple genes. This is sometimes referred to as complex traits. By way of example, microbes naturally and constantly must respond to stresses of temperature, osmotic balance, desiccation and other environmental changes. The responses typically require multiple systems and consequently involve complex genetic interactions. Many components, such as various heat shock proteins, have been studied in isolation under one set of changing conditions. Increasingly, heat shock proteins are revealed to be involved in multiple stress responses.

Gene interplay is ripe for deeper understanding via machine learning approaches (Cai et al., 2021 ; Shah et al., 2021 ). This can take advantage of the burgeoning genomic and transcriptomic data, that is too complex for humans to analyse meaningfully by manual methods. This approach can highlight important genes in certain stress conditions, the interactions between genes, and how different microbes have evolved different strategies for dealing with changes.

A counterargument might be that methods like random forest tree building, or even mutating codes, give outputs that appear as a blackbox to humans. That is, we see a result, but cannot follow how the machine arrived at that endpoint. This might seem disconcerting at first. However, most scientists believe in the notion that the most important results emerge from good hypotheses. And good hypotheses regarding the most complex microbial processes are hard to come by. In that context, I foresee a beautiful machine‐human duality in which machines process large datasets, unexpected gene interactions emerge, and humans use that new information to devise new hypotheses and the key experiments to test them.

Some envision a further step into the machine world. Recently, biotech industries have expressed significant concerns about the irreproducibility of impactful biotechnology research results ( Challenges in reproducible results ). Most believe the problem largely emanates from small methodological inconsistencies that are not adequately communicated in the respective Materials and Methods sections of published articles. One proposed solution is the use of more automated methods run by standard computer code (Leman et al., 2021 ). Fortunately, for those of us dealing with microorganisms, bacterial cultivation and subsequent manipulations are more likely to prove reproducible than experiments conducted with colonies of lab rats. Still, automated microbial batch growth and robotic microtiter well screening are becoming much more common in microbiology laboratories. One can only expect that we will increasingly rely on machine methods for both enhancing reproducibility and increasing our research throughput. The machine‐human‐microbe axis is upon us, and with it, the future looks bright.

Microb. Biotechnol. (2021) 15 ( 1 ), 79–80 [ PMC free article ] [ PubMed ] [ Google Scholar ]

• Eisenstein, M. (2021) Enzymatic DNA synthesis enters new phase . Nat Biotechnol 38 : 1113–1115. [ PubMed ] [ Google Scholar ]
• Cai, W. , Long, F. , Wang, Y. , Liu, H. , and Guo, K. (2021) Enhancement of microbiome management by machine learning for biological wastewater treatment . Micro Biotech 14 : 59–62. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shah, H.A. , Liu, J. , Yang, Z. , and Feng, J. (2021) Review of machine learning methods for the prediction and reconstruction of metabolic pathways . Front MolBiosci 8 : 634141. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Challenges in reproducible results . Nature article compilation , August 29, 2021, URL https://www.nature.com/collections/prbfkwmwvz/
• Leman, J.K. , Lyskov, S. , Lewis, S. , Adolf‐Bryfogle, J. , Alford, R.F. & Barlow, K. et al. (2021) Ensuring scientific reproducibility in bio‐macromolecular modeling via extensive, automated benchmarks. bioRxiv. 10.1101/2021.04.04.438423 [ PMC free article ] [ PubMed ] [ CrossRef ]

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Advances in microbial biotechnology for sustainable alternatives to petroleum-based plastics: a comprehensive review of polyhydroxyalkanoate production.

1. Introduction

2. phas: types, synthesis and industrial production, 2.1. types of phas, 2.2. biosynthesis of phas, 2.3. industrial production of phas, 3. overcoming the limitations of industrial pha production with microbial biotechnology, 3.1. brief introduction to microbial biotechnology techniques for pha production, 3.2. modification of metabolic pathways, 3.2.1. strategies that directly impact the biosynthetic pathway of phas, 3.2.2. approaches to modulate the amount of nad(p)h, 3.2.3. promoter engineering to enhance pha synthesis.

3.3. Expanding the Range of Renewable Substrates Bioconverted into PHAs

3.4. Improvement in the Downstream Processing

4. conclusions, author contributions, conflicts of interest.

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González-Rojo, S.; Paniagua-García, A.I.; Díez-Antolínez, R. Advances in Microbial Biotechnology for Sustainable Alternatives to Petroleum-Based Plastics: A Comprehensive Review of Polyhydroxyalkanoate Production. Microorganisms 2024 , 12 , 1668. https://doi.org/10.3390/microorganisms12081668

González-Rojo S, Paniagua-García AI, Díez-Antolínez R. Advances in Microbial Biotechnology for Sustainable Alternatives to Petroleum-Based Plastics: A Comprehensive Review of Polyhydroxyalkanoate Production. Microorganisms . 2024; 12(8):1668. https://doi.org/10.3390/microorganisms12081668

González-Rojo, Silvia, Ana Isabel Paniagua-García, and Rebeca Díez-Antolínez. 2024. "Advances in Microbial Biotechnology for Sustainable Alternatives to Petroleum-Based Plastics: A Comprehensive Review of Polyhydroxyalkanoate Production" Microorganisms 12, no. 8: 1668. https://doi.org/10.3390/microorganisms12081668

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REVIEW article

Artificial intelligence applications in the diagnosis and treatment of bacterial infections.

• 1 First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
• 2 Department of Infectious Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

1 Introduction

Bacterial infections remain a major challenge in medical and public health in the 21st century, with millions of patient deaths annually. According to a study published in The Lancet on November 21, 2022, bacterial infections are one of the leading causes of global health loss and have become the second leading cause of death globally, after ischemic heart disease ( GBD, 2019 ). Accurate and rapid identification of pathogens and their drug susceptibility profiles is essential for selecting the right treatment and reducing mortality. However, most current bacterial identification and drug susceptibility testing require culture times of several days, which not only delays the initiation of treatment, but also increases the risk of the development of resistant bacteria due to the long-term use of broad-spectrum antibiotics. At the same time, surveillance and management of bacterial infections are essential to prevent their spread and safeguard public health. In this context, the medical community urgently seeks new tools and strategies to better cope with bacterial infections. The rise of artificial intelligence (AI) technology, offers a new way to deal with bacterial infection ( Mintz and Brodie, 2019 ; Larentzakis and Lygeros, 2021 ; Ting Sim et al., 2023 ).

Recently AI, as a powerful computational tool, has shown great potential in the diagnosis and treatment of bacterial infections ( Goodswen et al., 2021 ; Jiang et al., 2022 ). AI is a science and technology that simulates human intelligence through computers, capable of mimicking human cognitive abilities and decision-making processes. In medicine, the main focus should be on the following terms: machine learning (particularly deep learning), natural language processing, computer vision, knowledge graph, and robotics, etc. ( Mintz and Brodie, 2019 ) ( Figure 1 ). The rapid expansion of AI technology spans from enhancing epidemiological surveillance to accelerating pathogen identification and predicting bacteria sensitivity to antimicrobial agents, Furthermore, AI supports the research and development of new drugs, vaccines, and innovative therapies, thereby promoting the development advancement of personalized medicine. tThe wide application of AI is expected to fundamentally transform the management, diagnosis, and treatment of bacterial infection ( Wong et al., 2023 ).

Figure 1 . The relationship between machine learning (particularly deep learning), natural language processing, computer vision, knowledge graph, robotics, and artificial intelligence.

Based on a comprehensive analysis of the existing literature and the latest research results, this study aimed to explore how AI technology can improve the efficiency and accuracy of medical diagnosis, as well as the level of personalized treatment, while focusing on the challenges that may hinder its practical clinical application. This will primarily provide medical workers with a comprehensive understanding of the application of AI technology in the diagnosis and treatment of bacterial infectious diseases, jointly promote the application of AI in the fight against bacterial infections, provide patients with more accurate and efficient medical services, and contribute to the development of global public health.

2 Application of AI in epidemiological surveillance of bacterial infectious diseases

AI and big data technologies are rapidly transforming the epidemiology of infectious diseases, particularly in the research and management of public health emergencies (PHEs). The modelsof infectious disease dynamics (IDD) and dynamic Bayesian networks (DBN)have not only promoted the spread of disease forecast accuracy, but also strengthened the ability analysis outbreakevolution ( Gao and Wang, 2022 ). Through cloud computing platforms, AI can process massive data in real time and effectively monitor infectious disease outbreaks. Despite the challenge of long model training time, its practicability makes it an indispensable tool for early epidemic warning ( Li et al., 2023 ). In addition, the development and application of geographic information systems (GIS), with its advanced data overlay capabilities, has greatly optimized the integration of public health data and has gained widespread acceptance ( Wells et al., 2021 ). Similarly, the ToxPi*GIS Toolkit enables the visualization and analysis of geospatial data in the ArcGIS environment, a visualization framework that integrates multiple data sources and generates intuitive graphic files with through Python scripts, ArcGIS Pro methods, and custom toolkits ( Fleming et al., 2022 ). In addition, the cloud data storage and use of Internet search data, such as Google Flu Trends, show the potential of disease surveillance systems based on large data to enhance real-time monitoring ( Pfeiffer and Stevens, 2015 ).

Although these advanced tools and methods are currently used primarily in viral epidemiology, their potential for disease surveillance, data presentation and analysis, and public health decision-making continues to evolve. This suggests that their contribution to bacterial epidemiology is also expected to increase. For example, machine learning models can predict in advance the risk of Clostridioides difficile infection among patients in large hospitals, allowing healthcare teams to implement preventive measures proactively before infection occurs ( Oh et al., 2018 ; Tilton and Johnson, 2019 ). Real-time locator systems can be used for contact tracing in the emergency department, which is not only more efficient and timely than tracing methods relying on electronic medical records, but also significantly increases the number of potentially exposed individuals identified while optimizing the use of time and resources ( Hellmich et al., 2017 ). Maia Lesosky et al. revealed the impact of inter-hospital patient flow on methicillin-resistant Staphylococcus aureus (MRSA) transmission through Monte Carlo simulation ( Lesosky et al., 2011 ). Further studies explored cross-hospital pathogen transmission using a susceptible infection model, demonstrating the important value of AI and big data in curbing hospital-acquired infections ( Ciccolini et al., 2014 ).

AI is paving new ways to predict and prevent bacterial infections. AI technology integrates and analyzes vast amounts of complex data to achieve early recognition and accurate prediction of bacterial infection outbreaks. This optimizes prevention and control measures, guides public health decisions, and supports the global fight against infectious diseases and the new solution.

3 AI has revolutionized the study of bacterial infection mechanism

Further study of the pathogenesis of bacterial infectious diseases is crucial to fully understand the nature of these diseases. This process not only involves the complex process of how bacteria colonize, invade, and spread in the host but also involves the host’s immune response and its interaction with pathogens. Among them, pathogen-host interaction is the key link, and animal models have been an indispensable tool in traditional research. They provide valuable data for observing the infection process of pathogens, host immune response, and disease development ( Younes et al., 2020 ; Burkovski, 2022 ). While such approaches, although capable of providing accurate and rich biologic insights, are often costly, time-consuming, and associated with ethical concerns. With the rapid development of AI technology, especially the emergence of machine learning models, researchers can simulate and understand the complex interactions between pathogens and hosts without animal experiments. For example, the PHISTO tool promotes a deep understanding of infection mechanisms by synthesizing different databases and using text mining techniques, supplemented by graph theory analysis and BLAST search ( Durmuş Tekir et al., 2013 ). A novel set of modular structural plasmids named pTBH (toolbox of Haemophilus) demonstrates coexistence and co-infection kinetics of fluorescently labeled strains by 3D microscopy combined with quantitative image analysis ( Rapún-Araiz et al., 2023 ). Furthermore, AI models can effectively simulate the complex interactions between bacteria and hosts in different metabolic states ( Dillard et al., 2023 ). Using advanced fluorescence microscopy detection and automated image analysis techniques, researchers have found that Staphylococcus aureus isolates from patients with bone/joint infection, bacteremia, and infective endocarditis show different infection characteristics in different host cell types ( Rodrigues Lopes et al., 2022 ). These techniques not only provide a visual basis for understanding microbial behavior in specific host environments but also assist in the design of drugs and vaccines.

The application of machine learning models provides us with a highly flexible way to predict and simulate the complex mechanisms of pathogen-host interactions, which not only accelerates the research process but also reduces the research cost. Although AI models are not a complete replacement for all animal model studies, they provide new ways to explore uncharted territories.

4 AI application in the diagnosis of bacterial infections

In the traditional approach to diagnosing bacterial infectious diseases, laboratory technicians rely on microbiological and biochemical tests to identify pathogens. It includes bacterial culture, morphological observation, biochemical reaction tests, and serological techniques ( Ernst et al., 2006 ; Váradi et al., 2017 ) ( Table 1 ). In addition, molecular biology techniques are widely used for the identification of bacterial DNA sequences, of which the polymerase chain reaction (PCR) is a commonly used method ( Wilson, 2015 ; Deusenbery et al., 2021 ). Although PCR technology is more advanced than traditional biochemical and microbiological methods, it requires a long time to complete the experimental process. Moreover, the integration and application of AI technology not only optimizes the traditional bacterial detection and management process, but also has the potential to bring about a complete revolution ( Ho et al., 2019 ; Wang et al., 2020 ; Paquin et al., 2022 ; Howard et al., 2024 ) ( Figure 2 ).

Table 1 . Advantages and limitations of the traditional bacterial identification methods.

Figure 2 . Artificial intelligence facilitates the diagnosis of bacterial infectious diseases.

4.1 AI improves the efficiency and accuracy of pathogen identification

AI technology provides a new way to diagnose bacterial infections rapidly and accurately. For example, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with ClinProTools software provided a method for the rapid identification of two Staphylococcus aureus subspecies, which achieved 100% identification and classification accuracy through genetic analysis and a fast classifier model ( Pérez-Sancho et al., 2018 ). Findaureus, an open-source application based on Python, demonstrated the ability to automatically locate bacteria in the tissue section using immune fluorescent tags. It overcomes the challenges of the manual threshold-setting process and optimizes the analysis of the condition of complex tissue cell efficiency ( Mandal et al., 2024 ). PhenoMatrix (PM) Colorimetric Detection Module (CDM) digital imaging software uses the automated Walk Away Specimen Processor to detect Group B Streptococcus (with high sensitivity similar to that of molecular testing methods, increasing laboratory productivity and reducing the potential for human error ( Baker et al., 2020 ). In addition, DNA microarray technology, using a machine learning decision-making algorithm (DendrisChips), identifies 11 types of bacteria associated with respiratory tract infections within 4 h. This technology combines PCR amplification of bacterial 16S rDNA and specific oligonucleotide hybridization on DendrisChips®, which are read with a laser scanner, thereby achieving quick and accurate detection and differentiation with over 95% accuracy ( Senescau et al., 2018 ). Using neural networks to analyze response patterns, a researcher has designed a sensor capable of identifying 16 different bacterial species and their Gram-staining properties with >90% accuracy. The sensor is stable for up to 6 months after preparation and requires one-thirtieth the amount of dye and sample as traditional solution-based sensors, compared to conventional techniques ( Laliwala et al., 2022 ). Thus, this method provides an innovative diagnostic tool that promises clinical applications in resource-limited settings.

In diagnosing diseases that pose a serious threat to human health, such as tuberculosis, although conventional microscopy methods are effective, they are slow and of limited sensitivity. The introduction of AI, specifically Metasystems’ automated antifungal bacilli (AFB) smear microscopy scanning and deep learning-based image analysis module (Neon Metafer), has greatly improved the speed and accuracy of antifungal bacilli (AFB) smear-negative slide recognition speed and accuracy ( Desruisseaux et al., 2024 ). A deep neural network (DNN) classifier combined with an automated slide scanning system reduces analysis time from several minutes to approximately 10 s per slide ( Horvath et al., 2020 ). Further, a novel diagnostic system combining T-SPOT with DL-based computed tomography image analysis can significantly improve the classification accuracy of nontuberculous mycobacterial lung disease and pulmonary tuberculosis ( Ying et al., 2022 ). AI tools, such as artificial neural networks, are becoming important in providing rapid and effective pathogen detection methods ( Dande and Samant, 2018 ). AI technology brings unprecedented accuracy and speed to pathogen detection through efficient learning and analysis capabilities. It will not only promote the automation of pathogen detection but also substantially decrease error rates caused by human operation, thereby improving the reliability of the diagnostic process.

4.2 AI optimizes antimicrobial susceptibility testing

Identifying pathogens and performing Antimicrobial Susceptibility Testing (AST) in today’s clinical laboratories often relies on culturing and isolating pathogens. Standard AST methods ( CLSI, 2023 such as disk diffusion, microbroth dilution, and AGAR dilution methods, typically require 2–3 days or longer from sample collection to obtaining culture and drug susceptibility results ( Abu-Aqil et al., 2023 ). To effectively control infections and prevent them from rapidly deteriorating or spreading to other parts of the body, clinicians often choose broad-spectrum antimicrobials for empirical treatment, given that many infectious diseases are often difficult to diagnose by symptoms in the early stages. However, this practice may increase the risk of drug-resistant strains arising due to inappropriate drug selection; therefore, there is an urgent need for rapid and accurate AST technologies to guide diagnosis and treatment.

With the rapid advancement of technology, AI has become an important tool in bacterial AST, providing various efficient and rapid methods to perform drug susceptibility testing. For example, Raman spectroscopy based on image stitching technology enables single-cell level detection, which can automatically, efficiently, and rapidly identify drug-resistant bacteria ( Nakar et al., 2022 ; Dou et al., 2023 ). Combining machine learning and infrared spectroscopy enables rapid and definitive identification of urinary tract infection bacteria and their drug resistance, dramatically reducing the time from sample collection to results. This approach decreases the time of identification and sensitization of Escherichia coli , Proteus mirabilis , and Pseudomonas aeruginosa from 48 h to approximately 40 min ( Ciccolini et al., 2014 ; Tilton and Johnson, 2019 ; Younes et al., 2020 ; Burkovski, 2022 ). Similarly, the SlipChip microfluidic device uses electrophoresis technology to extract and enrich bacteria directly from positive blood cultures. This device enables parallel inoculation of bacteria into nanoscale droplets of broth, facilitating simultaneous multiple AST. Results can be reported to clinicians within 3–8 h, ensuring reliable AST results and enabling earlier reporting and targeted antimicrobial treatment ( Yi et al., 2019 ).

Automation technology has also demonstrated high efficiency in detecting certain special drug-resistant bacteria, such as MALDI-TOF MS, for the detection of MRSA and carbapenem-resistant Klebsiella pneumoniae (CRKP) ( Wieser et al., 2012 ; Zhang et al., 2023 ). However, the novel ML-based MALDI-TOF MS method enables rapid identification of MRSA and CRKP from labeled blood cultures within 1 h ( Yu et al., 2023a , b ). Recent studies have shown that using computer science to analyze a large number of MALDI-TOF MS data can provide a comprehensive understanding of western blot mapping between resistant and sensitive isolates ( Wang et al., 2021 ). WASPLab automation system can significantly shorten the vancomycin resistant enterococcus (VRE) recognition time ( Cherkaoui et al., 2019 ). In addition, the automated plate evaluation system (APAS Independence) has significantly improved the productivity of high-throughput laboratories through its highly sensitive digital image analysis technology to accurately classify MRSA and sensitive Staphylococcus aureus (MSSA) cultures as negative or positive without human intervention ( Gammel et al., 2021 ).

In conclusion, the application of AI technologies to antimicrobial susceptibility testing enables the rapid and accurate identification of drug-resistant bacteria, thereby dramatically shortening the time from sample collection to result confirmation, and can be accomplished without human intervention. These technologies provide laboratories with a rapid and automated means of drug resistance monitoring, which significantly improves diagnostic efficiency and helps clinicians make rational antimicrobial treatment decisions as early as possible ( Table 2 ).

Table 2 . Artificial intelligence in the bacteria identification and drug sensitivity analysis.

4.3 AI can improve bacterial genome sequencing

Genome sequencing technologies (including whole genome sequencing and next-generation sequencing) have significantly accelerated not only the identification of infectious agents, but also the tracking of transmission pathways in healthcare settings and the analysis of the impact of complex microbial communities on human health ( d’Humières et al., 2021 ; Deusenbery et al., 2021 ). It also provides a powerful tool for monitoring and responding to antimicrobial resistance (AMR) globally ( Waddington et al., 2022 ; Sherry et al., 2023 ).

Current genetic testing techniques mainly match based on sequence similarity; however, these tools are often unsuccessful in identifying new species without closely related genomes or related sequences in reference databases. In response to this challenge, the machine learning-based PaPrBaG method provides a reliable and consistent prediction method that maintains its reliability even with low genome coverage ( Deneke et al., 2017 ). In addition, machine learning combined with metagenomic sequencing can significantly improve the diagnostic accuracy of diseases that are difficult to diagnose, such as tuberculous meningitis ( Ramachandran et al., 2022 ).

Another challenge for genetic testing technologies is how to rapidly and accurately interpret high-dimensional genomic data as the cost of second-generation sequencing technology decreases and throughput increases. Machine learning techniques have shown their potential in processing large genomic data by analyzing and predicting the health impact of Shiga toxin-producing Escherichia coli infections, providing new methods and perspectives for microbial risk assessment ( Njage et al., 2019 ). In addition, Bayesian neural networks using a nonparametric Bayesian algorithm excelled in accelerating the analysis of genetic association studies and efficiently and accurately identifying variant strains of infection ( Beam et al., 2014 ).

Combining machine-learning models with genomics technology has shown excellent performance in predicting pathogen resistance, which is significantly better than existing methods. Some researchers have used machine learning to construct a knowledge map of antimicrobial resistance in Escherichia coli , which realizes the automatic discovery of knowledge of antimicrobial resistance in Escherichia coli and reveals unknown drug resistance genes ( Youn et al., 2022 ). Based on the XGBoost and convolutional neural network approaches, the researchers not only accurately predicted the minimum inhibitory concentrations of Klebsiella pneumoniae clinical isolates against 20 antimicrobial drugs, but also successfully identified strains with high drug resistance or high virulence ( Nguyen et al., 2018 ; Liu et al., 2021 ; Lu et al., 2022 ). Similarly, some researchers have innovated a decision tree method called Treesist-TB for identifying mutant strains and predicting drug resistance, which has a recognition ability beyond the existing TB-Profiler tools ( Deelder et al., 2022 ), This technique not only demonstrates the value of decision trees in the tuberculosis field but also provides a reference template to identify other drug-resistant pathogens.

AI has shown great potential in genome sequencing technology. In response to the challenges of identifying new species and interpreting high-dimensional data, machine learning has surpassed the limitations of traditional genetic detection techniques and deepened our understanding of the microscopic world of pathogens. Furthermore, machine learning excels in predicting antimicrobial drug resistance, outperforming traditional methods, and strengthening global antibiotic resistance (AMR) monitoring efforts.

5 Application of AI in the treatment of bacterial infections

The challenges in the treatment of bacterial infections are diverse, and one of the most serious is the increasing resistance to antimicrobial agents. The importance of Antimicrobialresistance was formally declared at the United Nations General Assembly High-level Meeting on antimicrobial Resistance in 2016, and countries were called on to commit to developing their national action plans on antimicrobial resistance. Nearly 5 million people died globally due to resistant pathogens in 2019 ( Antimicrobial Resistance Collaborators, 2022 ). Current projections suggest that by 2050, 10 million people globally could be burdened by antimicrobial drug resistance each year ( Walsh et al., 2023 ). Over time, bacteria have acquired resistance to antimicrobial drugs through natural selection and genetic variation, thereby undermining the effectiveness of traditional treatments. In addition, the high diversity of bacteria and the complexity of bacterial-host interactions further increase the difficulty of treatment, making the development of vaccines and novel drugs difficult. Hence, developing new antimicrobial strategies and therapeutic approaches are urgently needed to address these issues ( Stracy et al., 2022 ).

In this context, AI technology accurately simulates the complex interactions between pathogen, host, and drugs, revealing microbial infection features and optimizing drug and vaccine design ( Figure 3 ). In addition, AI application in the field of phage therapy brings new hope for the fight against bacterial resistance.

Figure 3 . AI technology can model complex interactions between pathogens, hosts, and drugs.

5.1 AI revolutionizes drug discovery and development

In drug research and development, the application of AI is breaking the boundaries of traditional research, providing new strategies to overcome the problem of drug resistance. For example, by combining high-throughput biophysical analysis and machine learning, a framework was established to identify and predict bioactive targets of antimicrobial drugs, which successfully revealed the relationship between phenotype, target, and chemotype, providing an effective way to identify candidate therapeutic drugs ( Santa Maria et al., 2017 ). Meanwhile, combining fragment-based drug design with quantitative structure–activity relationship modeling demonstrates the potential of artificial neural networks in the drug discovery process ( Kleandrova and Speck-Planche, 2020 ). Using data-driven techniques, the study of bacterial minimal inhibitory concentration data using machine learning and matched molecular pair analysis has revealed key chemical features that affect bacterial biological activity, thus promising to expand the chemical space of broad-spectrum antimicrobial agents ( Gurvic et al., 2022 ). In a study, a support vector machine learning approach was applied to analyze genomics, metabolomics, and transcriptomics data of Pseudomonas aeruginosa . This approach successfully identified a key molecular mechanism that distinguishes between pathogenic and non-pathogenic strains of Pseudomonas aeruginosa , which not only provides high-value targets for the development of novel antimicrobial therapeutics but also highlights the importance of dynamically integrating multidimensional data in modern drug discovery and development ( Larsen et al., 2014 ).

Furthermore, significant breakthroughs have been made in the application of AI in specific disease areas, such as anti-tuberculosis drug development. The machine learning and artificial neural network method can be used to successfully find LeuRS for Mycobacterium tuberculosis and MetRS double targets of inhibitors ( Volynets et al., 2022 ), and small-molecule inhibitors of the enzymes required for M. tuberculosis topoisomerase I have been successfully identified ( Ekins et al., 2017 ), providing a new strategy to overcome multidrug resistance in tuberculosis. In addition, combining public Mtb data with machine learning not only greatly improves the efficiency of drug discovery, but also accumulates valuable data resources for future anti-tuberculosis research and new drug development ( Lane et al., 2022 ).

These advanced technologies not only accelerate the research and development process of new drugs, but also enhance the possibility of discovering potential therapeutic options, fundamentally changing how researchers understand and operate complex biological systems, and heralding a new era of smarter and more precise development in the pharmaceutical field.

5.2 AI brings breakthroughs in vaccine development

Currently rapid progress has been made in vaccine research and development against viral diseases. In particular, the speed and efficiency of response to emerging virus epidemics have been greatly improved, such as the application of computer-aided design of COVID-19 vaccine candidates in the global pandemic of COVID-19 in early 2020 ( Abbasi et al., 2022 ). In contrast, bacteria in the field of vaccine research and development are faced with more complicated challenges. The high variability of bacteria, rapidly evolving drug resistance, and complexity of interactions between bacteria and their hosts all challenge the development of effective vaccines against bacterial infectious diseases. To address these challenges, leveraging emerging tools such as artificial intelligence, computer-aided design, and advanced immunological evaluation techniques has become pivotal to accelerating the development of safe and effective vaccines.

In the process of vaccine design, scientists are challenged not only to identify the key antigens that can trigger lasting immune memory, but also to ensure that the vaccine can elicit broad protective immune responses, including humoral and cellular immune responses, to achieve effective protection in the long term. Recently, reverse vaccinology (RV) technology has been widely used in vaccine research and development. As a calculation method, RV is mainly applied to bacterial pathogens. Bexsero, a Neisseria meningitidis B vaccine designed by RV, has been registered and widely used in many countries ( Heinson et al., 2015 ). In addition, a key component of vaccine development—antigen identification—is strongly supported by computational tools such as deep learning, reverse vaccinology and immunoinformatics. In-depth analysis of vaccine targets derived from pathogen protein-coding genomes has led to the successful development of a multi-epitope subunit vaccine with potentially potent protection. Although the safety and immunogenicity of the vaccine need to be further verified ( Rawal et al., 2021 ), this approach not only accelerates the vaccine design process and reduces the reliance on traditional trial methods, but also has important implications for addressing the threat of drug-resistant bacteria. Research shows that a new type of machine learning model, compared with traditional methods, achieves higher precision and sensitivity in predicting aspects of mycobacterium tuberculosis ( Khanna and Rana, 2019 ).

The application of machine learning technology not only optimizes the vaccine development process and improves efficiency by reducing the reliance on traditional experiments and animal testing, but also provides strong scientific and technological support to cope with evolving epidemics of bacterial infections.

5.3 AI drives innovative applications of phage therapy

Phage therapy has attracted much attention from the scientific community for its potential advantages in combating drug-resistant bacterial infections ( Viertel et al., 2014 ; Kulshrestha et al., 2024 ). However, accurate prediction of the complex interactions between phages and their target pathogens and hosts remains challenging ( Cisek et al., 2017 ), and AI models become an important tool to overcome this challenge. For example, a machine learning-based local K-mer strategy is used to accurately predict phage-bacteria interactions ( Qiu et al., 2024 ). Simultaneously, machine learning can assist in the design of clinical phage therapy, particularly for urinary tract infections caused by multidrug-resistant E. coli ( Keith et al., 2024 ). In addition, a tool called HostPhinder predicted phage host genus and species with 81 and 74% accuracy, respectively, demonstrating the technology’s ability to pinpoint therapeutic targets ( Villarroel et al., 2016 ).

Consequently, the application of phages, either alone or in combination with antimicrobial agents, can be a viable alternative to treat infections with resistant pathogens ( Tagliaferri et al., 2019 ). The rapid development of AI technology enhances the potential of phage therapy by accurately predicting complex interactions between pathogens and phages, thereby contributing to the design of personalized treatment. This not only accelerates the development of phage therapy but also enhances its treatment success.

5.4 AI-assisted clinical decision support systems

The timing of effective antimicrobials is a key determinant of morbidity and mortality in the management of infectious diseases, specifically in the case of septic shock ( Evans et al., 2021 ). Early identification can not only reduce the poor prognosis caused by delayed treatment, but also help avoid unnecessary medical intervention and reduce treatment costs, thus significantly improving the survival rate and quality of life of patients.

Under the background of increasing emphasis on individualized treatment and precision medicine, AI progress not only promotes medical innovation, but also may overturn the existing diagnosis and treatment mode. In bacterial infectious disease diagnosis and treatment, AI and ML are used to simplify the clinicians’ work process, improve the quality of decision-making, and promote the development of personalized treatment options ( Langford et al., 2024 ). For example, ML models have been successfully applied to diagnose respiratory syncytial virus infection and pertussis in children by combining clinical symptoms with laboratory test results ( Mc Cord-De Iaco et al., 2023 ). Based on statistically significant clinical indicators such as sex and age, LightGBM and other ML models have a good effect on predicting the etiology of classical Fever of Unknown Origin in patients ( Yan et al., 2021 ). In addition, ML models can rapidly predict the risk of MRSA infection in patients with community-acquired pneumonia and facilitate the implementation of targeted antimicrobial treatment ( Rhodes et al., 2023 ). Clinical decision trees generated based on recursive methods are valuable for determining the likelihood of infection with extended-spectrum beta-lactamase strains in patients with bacteremia ( Goodman et al., 2016 ). A system for early warning of antimicrobial drug allergies, K-CDSTM, effectively warns of antimicrobial drug allergies and prevents patients from being prescribed antimicrobial drugs that may trigger allergic reactions ( Han et al., 2024 ). The ontology-driven clinical decision support system uses big data to assist the treatment decision-making of infectious diseases and constructs a bridge between patients and medical workers ( Shen et al., 2018 ). In the development of predictive disease models, tools such as multiple infectious disease diagnostic models are significantly more accurate than traditional prediction techniques based on large amounts of training data ( Wang et al., 2022 ). In a 3-month case–control study using a computerized clinical decision support system in an experimental group, time was reduced by approximately 1 h and antimicrobial costs were saved by approximately US $84,000 ( McGregor et al., 2006 ).

In summary, machine learning models have been successfully used to improve diagnostic accuracy and predict disease risk in clinical decision-making, showing better accuracy and efficiency than traditional approaches ( Figure 4 ). AI and ML technologies are leading the wave of medical innovation and have the potential to change the traditional methods of diagnosis and treatment.

Figure 4 . The AI-assisted clinical decision support system can quickly collect the patient’s history of present disease, past history, personal history, family history, travel history, and antibiotic use history. Simultaneously, the system can integrate relevant auxiliary examination (including imaging examination and laboratory examination) and analysis of the genetic information of hosts and pathogens to provide the best treatment, becoming a bridge of effective communication between doctors and patients.

6 AI helps personalized medical development

Through deep study and the analysis of the complex algorithm, AI can process and interpret patients with huge amounts of data, including genetic information, living habits and historical health records, etc. This not only enables accurate diagnosis of the disease, but also facilitates personalized treatment plans for each patient. For example, in cancer treatment, AI can help doctors choose the most appropriate combination of drugs for patients, reduce side effects, and improve cure rates. Similarly, AI can also predict efficacy and possible complications and provide tailored health management plans for patients ( Bilgin et al., 2024 ; Elemento, 2024 ).

In the field of bacterial infections, a novel method called CombiANT can rapidly quantify antimicrobial synergy through a single test and automated image analysis, enabling personalized clinical synergy testing to improve the anti-infection combination therapy ( Fatsis-Kavalopoulos et al., 2020 ). Kuo-Wei Hsu et al. developed an automated portable antimicrobial susceptibility testing system for four common urinary tract infection bacterial strains, taking only 4.5–9 h to complete the test, which holds promise for future application in personalized medicine practice ( Hsu et al., 2021 ). Connor Rees et al. showed an overall success rate of > 90% for correct diagnoses in the list of 10 differential diagnoses generated by ChatGP-3 ( Hirosawa et al., 2023 ). In the future, more research is expected to focus on evaluating more complex cases and promote the development of fully trained artificial intelligence chatbots to improve the accuracy and completeness of diagnosis and further personalize patient treatment.

7 Challenges of AI in the medical field

Although the application of AI in the field of bacterial infections has great potential and prospects, it also faces numerous challenges. The first is the problem of data quantity and data quality. The collection, sorting and sharing of case data related to bacterial infectious diseases are restricted by privacy protection and standardization, which limits the training efficiency and application scope of AI models ( Cath, 2018 ; Baowaly et al., 2019 ; Hummel and Braun, 2020 ). Second, deep-learning algorithms often lack the ability to provide a convincing explanation for their predictions—the so-called “black box” problem—which can affect prediction accuracy and public trust in AI systems ( Schwartz et al., 2024 ). In addition, most healthcare AI research to date has been done in non-clinical Settings, with few instances of successful integration of AI into clinical care and most cases are still in the experimental stage ( Alami et al., 2020 ). Therefore, generalizing the results of the study may be challenging. Moreover, complex and variable bacterial infection mechanisms and rapid mutation of bacterial genes make it more difficult to accurately predict pathogen behavior and drug sensitivity. Furthermore, the establishment of AI models requires interdisciplinary fields, including microbiology, biochemistry, genetics, mathematics and computer science, etc. ( Figure 5 ), This requires a high level of knowledge and skills from the researchers and developers, posing a significant challenge for research teams with limited resources.

Figure 5 . The successful application of AI models in medicine relies on multidisciplinary collaboration.

Currently in the field of artificial intelligence, a perfect legal system and authoritative standards have not been established. With the continuous progress of technology and the expansion of application fields, formulating and updating relevant regulations is essential, which will be a dynamic development process ( Rees and Müller, 2022 ).

8 Conclusion

The rise of AI technology has opened up a new way to deal with bacterial infections. With the help of advanced technologies such as machine learning and deep learning, AI has been applied in many key areas, from rapid pathogen detection and antimicrobial susceptibility analysis to the interpretation of complex genomic data and the development of personalized treatment options. Through highly optimized algorithms, AI technology not only greatly improves the speed and accuracy of pathogen identification, but also accurately predicts the susceptibility of pathogens to specific antibiotics based on historical data, thus providing strong scientific decision support for doctors. Similarly, in the field of epidemiological surveillance, AI technology has strengthened the real-time monitoring and early warning ability of the spread of bacterial infectious diseases by analyzing and processing a large amount of epidemiological dataand providing a powerful analytical tool and basis for public health decision-making.

Although AI has a broad application prospect in the treatment of bacterial infectious diseases, there remain important issues to be solved, such as how to ensure the transparency and interpretability of AI decision-making and how to accelerate the diagnosis and treatment while strictly controlling the ethics and patient safety. To overcome these challenges and achieve its wide application in clinical practice, interdisciplinary cooperation, technological innovation and policy support are needed.

Prospectively, AI technology will bring a profound transformation in the field of diagnosis and treatment of bacterial infections. With the continuous strengthening and maturity of AI in pathogen identification, drug susceptibility testing and genomic analysis, it will become the right hand of clinicians. With the assistance of AI, medical workers will can better cope with the challenges brought by bacterial infections, continue to promote the development of medical practice in the direction of more precision, efficiency, and personalization, and ultimately achieve the goal of providing optimal care and treatment for patients.

Author contributions

XYZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. DZ: Data curation, Formal analysis, Software, Supervision, Validation, Visualization, Writing – review & editing. XFZ: Data curation, Investigation, Software, Supervision, Visualization, Writing – review & editing. XZ: Conceptualization, Formal analysis, Project administration, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We would like to thank Editage (www.editage.cn) for English language editing. The images in this article are drawn with the help of https://app.biorender.com/ and WPS office.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: bacterial infections, artificial intelligence, machine learning, diagnosis, treatment, epidemiologic surveillance

Citation: Zhang X, Zhang D, Zhang X and Zhang X (2024) Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front. Microbiol . 15:1449844. doi: 10.3389/fmicb.2024.1449844

Received: 16 June 2024; Accepted: 04 July 2024; Published: 06 August 2024.

Reviewed by:

Copyright © 2024 Zhang, Zhang, Zhang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xin Zhang, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Frontiers in Food Biotechnology

• © 2024
• Jayachandra S. Yaradoddi 0 ,
• Bharati S. Meti 1 ,
• Sulochana B. Mudgulkar 2 ,
• Dayanand Agsar 3

Department of Biotechnology, Basaveshwar Engineering College, Bagalkote, India

You can also search for this editor in PubMed   Google Scholar

Dept. of Biotechnology, Basaveshwar Engineering College, Bagalkot, India

Gulbarga university, kalburagi, india.

• Covers current trends in modern food biotechnology
• Presents recent advances in biotechnology for food production, nutritional quality, food safety, and preservations
• Covers all aspects of this interdisciplinary technology; knowledge, methods and expertise are combined

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About this book

This is a great book to explore the science underlying the Food Biotechnology, which explores and presents current biotechnological advances and approaches to improving the nutritional value of modern-foods. Novel fermentation and enzyme technological processes, protein engineering, genetic engineering, metabolic engineering, bioengineering, quorum sensing and nanobiotechnology have been incorporated to fetch into new dimensions in current food biotechnology research and this book provides a deep insight on all these aspects as a comprehensive resource for anybody interested in all the types of foods, latest processing, preservation technology and safety. Written by leading scientists in the field, the book will be a valuable resource for students and researchers in the fields of food chemistry, nutritional science, taste physiology, and neuroscience, as well as for professionals in the food industry.

• Food spoilage
• Fermented food
• Microbiology
• Food safety
• preservation

Table of contents (27 chapters)

Front matter, introduction to food biotechnology, prospect of biotechnology in foods.

• Jayachandra S. Yaradoddi, Bharati S. Meti, Shoba H., S. S. Injaganeri

Bioactive Components of Foods, Activities and Their Source

• Renuka Meti, Shweta Pattanashetti

Nutraceuticals: Classification, Sources and Relation with Medicine

• Bhuvaneshwari G., Vasant M. Ganiger, Vijaykumar B. Narayanpur, T. B. Allolli

Functional Foods in Brief

• Premjyoti Patil

Food Safety and Supply

Microbiology of food spoilage.

• Madhumala Y., Veena S. Soraganvi

Food Processing and Preservation

• Sulochana M. B.

Grain Processing and Baking Technology

• Harshitha T., Parinitha A., Pratiksha Prabhakar Gawali, Somya Adusumilli, Sudheer Kumar Yannam

Food Packaging Technology

• Shoba H., Ramappa, S. K. Jain

Understanding Food Allergies and Allergens: A Comprehensive Guide to Diagnosis, Treatment, and Management

• Jayashri B. Uppin, Gayatri Vaidya, Ghouse Modin N. Mamdapur

Food Toxicology and Safety

Applied technologies and frontiers, nanotechnology in food industry.

• Preeti S. Kumarmath

Fermented Foods and Their Potential

• Manoj Girish, Jayashree V. Hanchinalmath, Shefali Srivastava, Kirankumar Shivasharanappa

Fermentation Technology and Food Enzymes

• Yallappa M., Gangadhar B. Megeri, Basavaraj S. Hungund, Jayachandra S. Yaradoddi

Food Industry By-Products and Waste Management

• Bharati S. Meti, Spoorthi R. Kulkarni, Shilpa K. Jigajinni, Basavaraj Nainegali

Food Additives and Preservatives

• Harshitha T., Akshay H. Dasalkar, Parinitha A., Sudheer Kumar Yannam

Recent Development in Genetically Engineered Foods

Editors and affiliations.

Jayachandra S. Yaradoddi

Bharati S. Meti

Sulochana B. Mudgulkar, Dayanand Agsar

About the editors

Dr. Jayachandra S. Yaradoddi is presently working as a Assistant Professor of Food Biotechnology (PG), Dept. of Biotechnology, Basaveshwar Engineering College, Bagalkote, Karnataka, India. He obtained his Master’s and Doctoral degree from the Gulbarga University, Kalaburagi. He has received the university merit fellowship from Gulbarga University, Kalaburagi and UGC MRP “Project Fellowship” from the University Grants Commission, Govt. of India. His research areas are Microbial Ecology, Industrial Biotechnology, Environmental Biotechnology, Biomaterials, and Bionanotechnology. He was a research scientist at KLE Technological University, Hubballi, for seven years. As an outcome of his study, he has received a 25 Lakhs Research Grant from Karnataka IT, BT and Science and Technology, Govt. of Karnataka, India, under the Idea2PoC Scheme. He also worked as a Postdoctoral Research Fellow in the Dept. of Biological and Environmental Sciences, University of Helsinki, Niemenkatu, Lahti. He has received International research grants from the prestigious Päijät-Hämeen Rahasto, Finland, to work on “Recent trends in actinomycetes diversity of the environment and their potential bioactive molecules” under the supervision of Prof. Merja H. Kontro. He has guided 14 undergraduate projects and co-guided one Ph.D. student (Submitted thesis) at KLE Technological University, Hubballi. He has received the “Hargobhind Khorana Young Scientist Award” from the BSS, TNSRO, a DST-recognized society from 2015-16.  He has published more than 55 articles in various national and international journals of repute and filed an Indian patent, one each of edited and authored Books. He is also a Life member of the Biotech Research Society of India (BRSI) and a Fellow of the World Researcher’s Association and Bose Science Society (FBSS) India. He is an editorial member and also a recognized reviewer for peer-reviewed and prestigious international journals.

Prof. M. B. Sulochana is presently working as Professor in Department of Post Graduate Studies and Research in Biotechnology, Gulbarga University, Kalaburagi, Karnataka, India. She obtained her Master’s and Doctoral degree from the Gulbarga University, Kalaburagi. She has produced 3 M. Phil students, 10 Ph. D students and is currently guiding 2 Ph. D students. Completed 3 Major Research Projects one granted by UGC, New Delhi  and Two granted by DBT, New Delhi. She is also the Life Member of Association of Microbiologists of India (AMI), Life Member of Microbiologists Society, Karad, Life Member of Biotech Research Society of India (BRSI) and Life Member of Asian Federation of Biotechnology (AFOB). Appointed as a referee/ external examiner to adjudicate the Ph. D thesis for the Doctorate Degree and dissertations of  M. Phil Degree from many  Universities within state and outside state. Conducted an International Webinar on future Prospectives of Biotechnology, Future Prospectives of Biotechnology, 27th April, 2020, 300 above participants participated by virtual online. Organized many National Conferences like  Biotechnology for Industrial and Rural Development (NCBIRD-2008), National Conference on Recent Advances in Nanobiotechnology (NCINANOBT - 2012). Organized Two days Workshop on ‘Research methodology for Life Science Research Students’ as the Coordinator of the Workshop. She was invited as a resource person for many International and national Conferences and Seminars. She has deposited many gene sequences of Novel strains in Gen bank and has also Filed a patent provisionally on production of soluble melanin pigment from actinobacter.   She was awarded for the best oral presentation, Nominated for Bharat Jyothi award, New Delhi, Nominated for Best Citizen of India, New Delhi, Awarded Best PAPER PUBLICATION BY VGST, BANGALORE SEPTEMBER 2019.  Awarded Best Poster Presentation In Two Days National Conference On Biodiversity   Conservation And Future Strategies (NCBCFS- 2020) March 13th And 14th 2020.   Awarded 2nd Prize in BIO-POSTERS held at BENGALURU TECH SUMMIT 2021. Appointed As BiSEP Coordinator PG Diploma Course From 04/02/2017 Till todate. Appointed as Chairman, Dept. of Biotechnology from 13 April, 2020 to April, 2022 SIGNED TWO MOU’s BETWEEN: AgriLife, Hyderabad and BioEra, Pune for PG  Students Internship and Training In QC and RD.  She Published about more than 75 Research articles/ Papers   in both reputed International and National Scientific journals. She was also appointed as an Editorial Board Member and a Reviewer for internationally renowned journals.  

Dr. Bharati S. Meti is Dean student welfare and Professor & Head, Department of Biotechnology at Basaveshwar Engineering College, Bagalkote, Karnataka, India. She obtained her Master’s and Doctoral degree from the Gulbarga University, Kalaburagi. She has received the university merit fellowship from Gulbarga University, Kalaburagi during her PhD tenure. She has served the Basaveshwar Science College, Bagalkote for two years and since 20 years she is serving Basaveshwar Engineering College, Bagalkote at various capacities. She has involved in teaching, research adminstration and entrepreneurship development activity in the institute. Biofuels, food processing and by product utilization are her research areas. She has received around 2.45 crores funding from various agencies like KITS, KSCST, KSBDB, Bengaluru and EDI, Ahamadabad. She has guided 22 UG projects , 09 PG projects, 08 PhD students and 01 MSc Engineering by research student under Visveswarayya Technological University, (VTU) Belagavi. 04 patents are awarded, 25 peer reviewed international journal publications, 07 International conference presentations and 04 national conference presentations are in her credit. She has organized several programs in the institute for the students.  She has been awarded as BVVS Best Teacher award for the year 2018-19 and Best Publications  Award for 2021-22 from VGST, GoK. She is also in various committees of VTU Belagavi, UHS  Bagalkote, JSS Bagalkote.  She is a member of IEEE, Microbiologists society of India, Karnataka Rajya Vignan Parishad. She has involved in Entrepreneurship development activities in Basaveshwar Engineering College Science & Technology Entrepreneurs Park (BEC STEP) since 2016.   

Professor Dayanand Agsar is a present Vice Chancellor, of Gulbarga University, Kalaburagi; he has 38+ years of teaching and research experience, 14 years of experience as a Professor in the Dept. of Microbiology, Gulbarga University. He worked in various administrative capacities: to list; served as a Registrar, of Gulbarga University, Kalburagi for 3 years, served Chairman, Dept. of Microbiology. As a research credentials he has successfully completed funded projects from VGST, GoK, DBT, UGC, KSCST, and CSIR IICT- GUK collaborative project. He was instrumental in bringing the research collaboration between CSIR-IICT, Hyderabad in 2018, and also signed with the University of Pardubice, Czech Republic (2019). He has guided 23 Ph.D. and 09 M.Phil. Scholars and published 85 (Internationational-59 and National-26) research articles in various peer reviewed journals and he has received 2021 citations and 21 H-Index in Google Scholar platform, also received 413 citations in Scopus author profile to date. Filed An Indian Patent On Process For Production And Extraction of Water-Soluble Melanin by An Actinobacterium (No- 2731/Che/2015), Dayanand Agsar, D.N. Madhusudhan and M.B. Sulochana Patent Published 15.12.2017. International Linkages: Visiting Professor - SFIT, EPFL, Switzerland, 2015 - Edgard Gnansoun Invited Professor - University of Helsinkii, Finland, 2015 - Merja Kontro Collaborative Research - Sun Yat-Sen University, China - Wen Jun Lee.

Bibliographic Information

Book Title : Frontiers in Food Biotechnology

Editors : Jayachandra S. Yaradoddi, Bharati S. Meti, Sulochana B. Mudgulkar, Dayanand Agsar

DOI : https://doi.org/10.1007/978-981-97-3261-6

Publisher : Springer Singapore

eBook Packages : Biomedical and Life Sciences , Biomedical and Life Sciences (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

Hardcover ISBN : 978-981-97-3260-9 Published: 10 August 2024

Softcover ISBN : 978-981-97-3263-0 Due: 24 August 2025

eBook ISBN : 978-981-97-3261-6 Published: 09 August 2024

Edition Number : 1

Number of Pages : XIX, 501

Number of Illustrations : 97 b/w illustrations

Topics : Food Science , Biotechnology , Applied Microbiology , Food Microbiology

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Biotechnology Course Descriptions

Biology graduate courses  (biol).

510.      CONSERVATION ECOLOGY  (3 credit hours) This course reviews the evolutionary and ecological bases for the Earth’s biodiversity and its importance to ecosystem function and human welfare.  The causes, rates and patterns of loss of biodiversity throughout the world and the concepts and techniques used in ecological conservation and restoration are reviewed.  Three class hours per week.  Prerequisites: graduate status and permission of instructor. 521.      ANIMAL PARASITISM  (4 credit hours) This course details the ecological concept of parasitism, utilizing the prominent parasitic species of animals and man.  The laboratory component of the course concerns the identification of species and structures of the important parasites of animals and man.  Lab and field projects dealing with natural and host-parasite systems will also be undertaken.  Six class hours per week.  Prerequisites: graduate status and permission of instructor. 550.      EVOLUTION  (3 credit hours) A course covering the concepts and theories of modern evolutionary biology, including the mechanisms of genetic change in populations, speciation patterns, and geologic change through time.  Three class hours per week.  Prerequisites: graduate status and permission of instructor. 561.      MICROBIAL GENETICS  (4 credit hours) Genetic mechanisms of bacteria, including their viruses, plasmids and transposons.  Integration of genetic principles and genetic/molecular tools for understanding biological questions.  Select topics in eukaryotic microbial genetics will be included.  Six class hours per week including laboratory. Prerequisites: graduate status and permission of instructor. 565.      THE BIOLOGY OF FISHES  (4 credit hours) This is an introductory course that examines the evolution, morphology, anatomy, physiology, and ecology of fishes.  The course will relate the above subject areas to aquaculture principles and practices. Six class hours per week.  Prerequisites: graduate status and permission of instructor. 573.      EUKARYOTIC MOLECULAR GENETICS  (4 credit hours) A study of genome structure, organization and function of model organisms with special reference to Arabidopsis and other higher eukaryotes; theory and methodology of genetic and physical mapping, comparative genomics, sequencing, sequence analysis and annotation; emphasis on the function of complex genomes, genome-wide expression analysis, genetic and epigenetic mechanisms, gene silencing, transposons, genome duplication and evolution.  Prerequisites: graduate status and permission of instructor. 575.      PRINCIPLES OF AQUACULTURE (4 credit hours) An in-depth step-by step study of the principles and practices underlying commercial aquaculture production, aquatic productivity and the levels of aquaculture management.  Practices in the United States will be the primary focus with attention to the world in general.  Six class hours per week. Prerequisites: graduate status and permission of instructor. 599.      SPECIAL TOPICS  (1 – 4 credit hours) An in depth study of special topics proposed by members of the biology faculty.  Open to graduate students.  Prerequisite: graduate status and permission of instructor. 605.      ADVANCED ECOLOGY  (4 credit hours) This course explores the topics at the forefront of basic and applied ecology through current and seminal primary and review literature.  Topics include plant adaptations to stress and environmental heterogeneity, ecosystem nutrient and energy dynamics, processes that generate and regulate biodiversity, the importance of biodiversity to ecosystem function, and the application of this information towards management, conservation and reclamation.  In laboratory, these concepts will be explored using field and laboratory experiments.  Six class hours per week.  Prerequisites: graduate status and permission of instructor. 635.      ANIMAL PHYSIOLOGY  (4 credit hours) This course is designed as an introduction to the mechanisms and principles involved in life processes.  A general and comparative approach is used to develop and understanding, in biophysical and biochemical terms have how animals function in order to produce an integrated functioning of the organ systems.  While all levels of organization are considered, particular emphasis is placed on the whole animal and its dynamic organ systems.  The course also emphasizes physiological responses to environmental stresses.  Six class hours per week including laboratory.  Prerequisites: graduate status and permission of instructor. 640.      FIELD BOTANY  (4 credit hours) An integrated laboratory study of the taxonomy, ecology and geography of plants with emphasis on the flora of West Virginia.  Six class hours per week.  Prerequisite: graduate status and permission of instructor. 644.      PLANT PHYSIOLOGY  (4 credit hours) This course includes an analysis of the cell biology, biochemistry, metabolism, ecological physiology, and development of plants.  Lecture topics include water relations, respiration, photosynthesis, nitrogen fixation, mineral nutrition, plant hormones, plant molecular biology, genetic engineering, and the role of environmental signals in plant development, and the environmental physiology of Mid-Atlantic, mixed mesophytic, deciduous forests.  Lectures will be supplemented with reading in research journals. Laboratory exercises are designed to demonstrate basic research techniques as well as the principles covered in lecture.  Six contact hours per week.  Prerequisite:  graduate status and permission of instructor. 660.      ENVIRONMENTAL MICROBIOLOGY  (4 credits) Microbial functions, interactions, and diversity in natural and man-made environments.  Applications of microbial activities in bioremediation, biodegradation, agriculture, health and environmental biotechnology.  Six class hours per week including laboratory.  Prerequisites: graduate status and permission of instructor. 666.      CANCER BIOLOGY  (3 credit hours) This course will introduce the student to the biology of tumors.   Emphasis will be placed on the cellular and molecular events that lead to tumor formation and progression to cancer. The course format will be a combination of traditional lecture and seminar.  Three class hours per week.  Prerequisites: Entry into the Biotechnology Graduate Program and permission of the instructor. 671.      ADVANCED ENVIRONMENTAL MICROBIOLOGY  (2 credit hours) Discussion of current and classical research literature in environmental microbiology, including microbial ecology and evolution, and the interface with plant, animal and medical microbiology.  Two class hours per week.  Prerequisites: graduate status and permission of instructor.

Biotechnology Graduate Program  (BT)

501.      SEMINAR FOR TEACHING ASSISTANTS  (1 credit hour) This elective course that will introduce graduate students to the teaching profession.  The course focuses on the structural organization of the academic institution, selected techniques in teaching, issues in the classroom, and current literature in higher education.  There will be selected readings, exercises, and guest speakers.  Class meets one hour per week.  A maximum of one credit of the course may be applied toward the course requirements of the Biotechnology MS or MA degrees.  Prerequisite: admission to graduate program or permission of instructor. 511.      BIOTECHNOLOGY SEMINAR  (1 credit hour)  This is a graduate-level seminar course involving a literature search, and written and oral presentations of biotechnology research.  Includes evaluation of presentations by off-campus professionals, faculty and peers.  Two class hours per week.  Prerequisite: Admission to graduate program. 555.      BIOSTATISTICS  (3 credit hours) An introduction to statistics emphasizing its application in biological investigation.  Topics include central tendencies, dispersion, normality, confidence intervals, probability, parametric and non-parametric tests of hypothesis (including tests of independence and goodness of it, correlation, regression, t-test, ANOVA, ANCOVA, and planned and unplanned comparisons), the relationships between effect size, power, and sample size, and fundamentals of experimental design. Two lecture and two lab hours per week.  Prerequisites: Math 101 or Math 121; admission to the program. 567.      CURRENT CONCEPTS IN BIOTECHNOLOGY  (3 credit hours) Recent developments in animal, plant, environmental and microbial biotechnology, including the engineering of biological processes from molecular to ecosystem-level scales.  Lecture/discussion format.  Three class hours per week.   Prerequisite: Admission to the program. 571.      TECHNIQUES IN BIOTECHNOLOGY I  (2 credit hours) The first in a two semester laboratory series, this course includes a broad scope of protein, RNA and DNA protocols providing experience in the manipulation of macromolecules and transformation of microbes.  Emphasis is on building the skills and intellectual framework necessary to work in the biotechnology field.  Six class hours per week.   Prerequisite: Admission to graduate program. 572.      TECHNIQUES IN BIOTECHNOLOGY II  (2 credit hours) This is the second course in a two semester laboratory series.  This course includes numerous organism-specific techniques of culture, propagation, maintenance and study.  These exercises provide training in bioinformatics, plant and animal genetic engineering, bioreactors and fermentation, research microscopy and cytogenetics, aquaculture, immunology and molecular diagnostics.  Six class hours per week.  Prerequisites: BT571 or equivalent; Admission to the program. 590.      GRADUATE RESEARCH  (1 – 4 credit hours) An independent research topic designed by the student with the assistance of a graduate faculty advisor that supervises the project.  The topic should be acceptable to the advisor and the chair.  Limited to specific problems in the biotechnology field.  A maximum of 4 credits of BT 590 may be counted toward a Master’s in Biotechnology.  Variable contact hours.  Prerequisites: admission to Biotechnology Graduate Program and permission of instructor. 591.      GRADUATE INDEPENDENT STUDY OR RESEARCH  (1 – 4 credit hours) An independent research topic designed by the student with the assistance of a graduate faculty advisor that supervises the project.  The topic should be acceptable to the advisor and the chair. Limited to specific problems in the biotechnology field.  Available after fulfilling 4 credit hours of BT 590.  Variable contact hours.  Course is graded pass / fail only.  Prerequisites:  admission to Biotechnology Graduate Program and permission of instructor. 592.      GRADUATE LIBRARY RESEARCH  (2 credit hours) Extensive library research techniques in a particular biological area.  Staff assigns a topic and supervises the project.  A maximum of 2 credits of BT 592 may be counted toward a Master’s in Biotechnology.  Prerequisites:  admission to Biotechnology Graduate Program and permission of instructor. 598.      INDUSTRY INTERNSHIP IN BIOTECHNOLOGY  (1-3 credit hours) Experience in the biotechnology industry through work at an industrial site or governmental agency.  Arrangement determined by industry/government partner in conjunction with the student’s graduate committee.  Prerequisites: Admission to graduate program; approval of graduate committee.

599.      SPECIAL TOPICS IN BIOTECHNOLOGY  (1-4 credit hours)