Microbiota is the consortium of all microbial members colonized in a particular niche. Different human body niches carry different microbiota with distinct qualitative and quantitative compositions. In the human body, the GM is composed of the highest number of microbes of different kingdoms, including members of protozoa, archaea, eukaryotes, viruses, and bacteria (Barko et al., 2018). It is interesting to note that the composition and physiology of the GM that influence human health are also highly dependent on human lifestyle (e.g., diet, alcohol consumption, smoking, medication, stress, and sleep) and the surrounding environment. GM contributes to maintaining human health by preventing pathogenic infections (by occupying the gut space and modulating immunity) and by contributing to the nutrition and metabolism of the host (Thursby and Juge, 2017; Schmidt et al., 2018). However, the homeostasis in the GM may get disrupted due to alterations in either lifestyle or environment, leading to dysbiosis and associated complications (Bajaj et al., 2014). Dysbiosis in GM, inadequate nutrition, exposure to enteric pathogens due to improper hygiene early in life may result in environmental enteropathy, which impairs the immune, metabolic, and neuroendocrine physiology that may lead to the long-term cognitive deficit and poor vaccine efficacy (Watanabe and Petri, 2016; Hajela et al., 2020; Singhvi et al., 2020). In addition, dysbiosis may produce microbe-derived metabolites that are detrimental to the host and may result in diseases like non-alcoholic fatty liver disease (NAFLD) (Yuan et al., 2019). Maintaining a high diversity in GM is essential for gut homeostasis. The beneficial microbes like Lactobacillus plantarum and the bacteria-derived short-chain fatty acids such as butyrate are crucial in maintaining epithelial integrity (Karczewski et al., 2010).

Moreover, recent studies found that the abundance of two bacterial species, Faecalibacterium prausnitzii and Roseburia hominis, in the colon is significantly lower for the patients with ulcerative colitis than for the controls (Machiels et al., 2014). Likewise, a lower abundance of Bifidobacterium in the gut is related to peptic ulcer and gastric cancer (Devi et al., 2021). In contrast, Fusobacterium nucleatum colonization is positively associated with colon cancer (Kostic et al., 2012; Repass et al., 2018). Besides, by incorporating metagenomic sequencing technologies, large data comparison and analysis of the microbial communities have yielded the link between microbiome alteration with human diseases such as cancer (Yu et al., 2017; Thomas et al., 2019; Wirbel et al., 2019), type II diabetes (Wang et al., 2012), cirrhosis (Oh et al., 2020). For detailed perspectives on the inter-relationship between gut microbiota and human health, the authors refer to Zheng et al. (2018), Durack and Lynch (2019), and Gibbons et al. (2022).

Understanding the interplay among GM, gut microbial metabolites, and the host in homeostasis and disease is one of the top challenges in modern science. Modern research on GM is now rapidly moving from relative to quantitative approaches, potentially revealing more information relevant to human health.

The biomarkers from GM can be used for assessing various disease progressions, the nature of severity, and the response toward medical treatments. Microbial biomarkers have been proposed for different health problems, such as lung cancer (Liu et al., 2019; Temraz et al., 2019), obesity & diabetes (Zheng et al., 2018; Singer-Englar et al., 2019), and cirrhosis (Bajaj, 2019). Despite difficulties and low success rates, many studies attempted to target gut microbes for diagnostic purposes. Most of these attempts were focused on estimating the metabolites such as glucose, volatiles, endotoxins, etc. (Kassal et al., 2018). Due to the recent attention to the relationship between GM and health, novel microbial biomarkers are now being discovered.

Due to the advancement of synthetic biology and genetic engineering, biosensors such as whole-cell biosensors (WCBs) can detect target analytes directly from complex environments like wastewater or clinical samples. Bacterial biosensors, also called bactosensors, employ whole bacterial cells to determine various pathological biomarkers. Whole-cell bacterial bioreporters are designed to detect toxic chemicals. By integrating with microengineering, genetic circuits could be developed that translate the biochemical signals into quantifiable reporter protein signals (Van Der Meer and Belkin, 2010). Rutter et al. developed an engineered bacterial biosensor using Caenorhabditis elegans as a novel model organism to determine isopropyl β-D-1-thiogalactopyranoside (IPTG) in the gut (Rutter et al., 2019). Further advancement in designing the engineered bacterial circuit may help monitor human GM and detect gut inflammation by nitrate sensing (Woo et al., 2020). Sensing the gut metabolites could also provide crucial information on the host–microbe interactions in the gut. With the help of bactosensors, metabolites such as benzoate, lactate, anhydrotetracycline, and bile acids could be detected in human fecal samples. Microbe-associated fecal metabolites has the potential to be used as diagnostics and theranostics explorations (Zúñiga et al., 2022). Another artificial receptor platform called EMeRALD (Engineered Modularized Receptors Activated via Ligand-induced Dimerization) was developed and utilized to detect bile salts (a biomarker of liver dysfunction; Chang H. J. et al., 2021). Biosensors for other essential metabolites, such as trimethylamine-N-oxide (TMAO) (Chang Y. C. et al., 2021; Lakshmi et al., 2021) and indole, have also been developed recently (Wang J. et al., 2021).

Biosensors for detecting gastric pathogen Helicobacter pylori have been developed based on different detection methods such as piezoelectric (Su and Li, 2001) and electrochemical (Del Pozo et al., 2005; Ly et al., 2011). Real-time monitoring of the interaction of H. pylori with the human gastric mucin was also studied using a resonant mirror-based biosensor (Hirmo et al., 1999). Conversely, Bifidobacterium bifidum, proposed to have protective roles against H. pylori induced gastric diseases (Alexander et al., 2021; Devi et al., 2021), can be detected using quartz crystal microbalance immunosensor (Hou et al., 2020).

There has been considerable growth in biosensor technology for gut microbiome recently. One of the reasons is the advancement of nanotechnologies/nanoengineering and their use in biosensors technologies (Yadav et al., 2022). Due to their intrinsic properties, nanoparticles are now increasingly used to detect various microbes and their activities in GM (Fuentes-Chust et al., 2021). For example, a plasmonic sensor— based on the molecularly imprinted nanoparticles— has been developed for detecting Enterococcus faecalis (Erdem et al., 2019), whereas Shigella spp. were detected using gold nanoparticles on lateral flow biosensor (Wang et al., 2016). Another emerging biorecognition element for biosensors is the aptamer biomolecule. Aptamer-based biosensors are significantly explored as they can replace conventional antibody-based diagnostic technologies due to their high specificity and selectivity to bind with the target analyte. A DNA aptamer-based biosensor has recently been developed to study and detect human gut bacterium Akkermansia muciniphila (Raber et al., 2021).

Probiotics are specific strains of beneficial microbial species that provide health benefits to the host upon consumption in sufficient amounts. They are microorganisms with Generally Recognized As Safe (GRAS) status that are critical in preventing diseases like diarrhea, gut inflammation, viral infection, and even colorectal cancer (Uccello et al., 2012; Ghosh, 2018). The probiotics may help to increase the vaccine efficacy in developing countries (Hajela et al., 2020). Nowadays, probiotics are being used even as functional foods. However, probiotics could induce an individualized impact on the gut transcriptome, and the effect may not be universal (Zmora et al., 2018). Recently, probiotics have also initiated personalized therapies (Kort, 2014). Therefore, monitoring quantitative estimation of the probiotics within GM is very important for monitoring GI health and safety, and biosensor could become an instrumental technology.

Although studying the properties of probiotics by using biosensors is not widely used, some attempts have been made. The aptamer (Hemag1P) based biosensors were developed to detect Lactobacillus acidophilus with high selectivity and specificity (Urmann et al., 2016). This label-free, simple, and rapid method could distinguish between live and dead bacteria. An aptamer-based electronic biosensor has recently been developed for monitoring the gut bacterium Roseburia intestinalis (Xing et al., 2022). The yeasts, also being used as probiotics, have been detected by biosensors (Dacquay and McMillen, 2021). Many essential metabolites from beneficial microbes could be targeted to assess human health. Biosensors have developed for metabolites such as short chain fatty acid butyrate (Bai and Mansell, 2020; Chang Y. C. et al., 2021), lactic acid (Radoi et al., 2010) and indoxyl sulfate (Filik et al., 2016). The immunostimulatory effects usually exerted by the probiotics (e.g., some of the Lactobacillus strains) could also be traced by biosensors (Rocha-Ramírez et al., 2017). Importantly, probiotic microbes have been engineered to function as biosensors for detecting autoinducer peptide-I, a quorum-sensing molecule produced by Staphylococcus sp. (Lubkowicz et al., 2018). The engineered microbes can increase the complexity, stability, and safety during diagnosis or therapeutics, as observed with smart engineered probiotics (Rottinghaus et al., 2020).

New biosensing concepts and techniques such as in vivo wireless and ingestible capsules, microfluidic chips, and internet of things (IoT) have emerged in the recent pasts that are more robust, compact, multiplex, programmable, and reliable. These innovative technologies and smart devices would augment conventional biosensors as efficient and robust POCT devices shortly.

Smart and responsive materials are now being explored to design and develop better and more robust biosensors (Ngashangva et al., 2020). Using such advanced materials, the ingestible capsules were designed to collect microbiome samples from the GI tract. The capsule consisted of 3D-printed acrylic housing, hydrogel, and flexible PDMS membrane (Waimin et al., 2020). This non-invasive sampling technique was validated using Escherichia coli. By employing ingestible electronic capsules and a self-powered biosensing system, the crucial metabolite composition of small intestines could also be monitored (De la Paz et al., 2022). Additionally, an ingestible probiotic biosensor has been developed to diagnose GI bleeding in swine. Such ingestible micro-bio-electronic devices have the potential to transform disease management and diagnosis of GI diseases (Mimee et al., 2018). Volatile and gas molecules from a patient’s sample are used as bio-signatures. An electronic nose device based on an array of 13 commercial electro-chemical and optical sensors has been designed to study the microbial volatile metabolites in urine sample of colorectal cancer patients (Westenbrink et al., 2015).

Due to the limitation of the animal model, microfluidic chip such as organ-on-chip has become a promising tool as the physiology and function of tissues or organs can be recapitulated in microfluidic in vitro devices. Gut-on-chips are explored to enhance understanding the complex nature of gut microbiota-host interaction (Puschhof et al., 2021; Signore et al., 2021). Another possibility for exploring lab-on-chip technology is multiplex assay and detecting multiple target analytes on a single platform (Shah et al., 2016). The concept of cost-effective, instrument-less, simple, and user-friendly sensing platforms such as microfluidic paper-based analytical devices (μ-PADs) have added benefits to the existing lab-on-chip based biosensor (Takahashi et al., 2022).

As clinical governance is becoming challenging, demanding, and laborious, ML/AI and the internet of medical things must be incorporated with the biosensing concept. Additionally, medical science is transforming from traditional healthcare to digital healthcare monitoring systems that enable improved access to quality healthcare for patients, clinicians, and remote communities. Despite all the advantages, conventional biosensors have limitations such as low specificity, sensitivity, and selectivity compared to other bioanalytical techniques. The overall performance of the biosensor could be improved by integrating with ML/AI approaches. As the individual gut health involves complex interactions between diet, host, microbiota, real-time GM monitoring using ML-based big data collection and analysis with the help of a biosensor could be established (Sosnowski et al., 2020). The ML could also be used to screen the cause-effect relationship between GM dysbiosis and diseases (e.g., cardiovascular diseases; Aryal et al., 2020). ML/AI-based approaches are meta-metabolic network models that are useful to predict the pattern and acquire insights into the synergistic, dysbiotic relationships, and phenotypic outcomes (Lloyd-Price et al., 2019; Cammarota et al., 2020). By understanding such interconnections, development of robust personalized POC biosensors for gut health could be enhanced tremendously.