There are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages. Here, we present a neuromorphic global-local synergic learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn local plasticity and receive top-down supervision information for multiscale learning. We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods. We further implement the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and prove that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm. Global and local learning represent two distinct approaches to artificial intelligence. In this manuscript, Wu et al present a hybrid learning strategy, drawing from elements of both approaches, and implement it on a co-designed neuromorphic platform. In contrast, neuroscience-oriented LP is essentially correlation-driven learning, which prominently occurs between presynaptic and postsynaptic neurons and is triggered by asynchronous spike activity. Rooted in neuroscience, LP has been widely used for feature abstraction and facilitates the realization of many advanced brain-inspired learning and memory mechanisms12. Despite remarkable practical advantages in low-latency and energy-efficiency computation14, applications of neuromorphic models are still limited to a small range of usages and the overall performance is inferior to the state-of-the-art results16. To fully exploit the potential still requires many effective algorithms. At the same time, local correlation-driven learning  and global error-driven learning ) are the two principal learning routes for artificial intelligence (AI). Both approaches have their unique advantages but neither can completely outperform the other on all learning problems. Thus, it is highly expected to integrate them into one single neuromorphic hybrid model to explore complementary synergic learning capabilities in multiple learning scenarios. However, the different learning circuits and weight update behaviors make the development of such hybrid learning obscure and the further incorporation of complex and diverse spiking dynamics poses a greater challenge for exploring hybrid learning on neuromorphic models.The majority of neuromorphic models are established by single backpropagation or single neuroscience-oriented local plasticity (LP), exhibiting radicals with different learning features and advantages. In general, backpropagation is global error-driven learning with two alternative information circuits (top-down and bottom-up). The learning process is implemented via layer-by-layer allocation of global supervised errors. Benefitting from the recent progress on deep learning, backpropagation and its many variants have been applied for training neuromorphic models represented by spiking neural networks (SNNs) and demonstrated good accuracy in certain specific tasks, such as image classification and reinforcement learning (RL)18. The three-factor learning rule describes a general framework of synaptic plasticity that incorporates presynaptic activity, postsynaptic activity, and additional third factors, such as neuromodulators and neurotransmitters, representing the top-down supervised signals. Several studies on developing spiking learning algorithms have been inspired by the three-factor learning rule23. Adopting a biologically plausible manner, they took supervised errors or rewards as the biological third factor to modulate the magnitude of local weight update and successfully applied the methods for simple image classification tasks19, RL tasks20 and probabilistic inference tasks23. Despite great progress in understanding biological learning, due to the different learning goals, most of the works fail to fully exploit the advantages of global gradient learning and are generally not good at solving many complex learning problems25.Recently, there has been an increasing number of studies involving global-local hybrid models with different interests. A related biological study was on the three-factor learning rule28 for SNNs narrowly focus on improving a single GP-based model without integrating LP. How to learn to optimize both the LP and GP with various spiking features and integrate their respective advantages in one neuromorphic model remains an important open issue. Alternatively, in the early 1990s, ref. 29 proposed a framework that can optimize LP through supervised signals. In follow-up research, several studies extended the framework to establish large-scale non-SNNs and have demonstrated high performance in solving few-shot learning31 and unsupervised learning33. However, due to the lack of an effective and configurable hybrid mechanism, the potential of global-local learning has not yet been fully explored, either in some important learning capabilities or computational efficiency. Furthermore, none of the works considered developing such hybrid synergic learning in neuromorphic computing.Another related vein is meta-learning. Meta-learning, also named learning-to-learn, is ubiquitous in nature for continuously improving the learning ability. Existing meta-learning studiesThe previous lack of powerful hybrid learning for neuromorphic models also affects the use of many-core neuromorphic hardware, which aims to provide ultralow-power hardware solutions for AI implementations. A feasible hardware scheme that can support online hybrid learning has yet to be reported. If neuromorphic hybrid learning models with algorithm-hardware co-design could be developed on neuromorphic platforms, then the neuromorphic many-core architecture can be exploited to explore hybrid on-chip computation schemes to obtain better performances in practical learning scenarios.Here, we show a spike-based hybrid plasticity (HP) model using a brain-inspired meta-learning paradigm and a differential spiking dynamics model with parameterized LP. The approach provides a generic and flexible integration of these two learning methods and facilitates high-efficiency hybrid learning on neuromorphic chips. By developing multiple synergic learning strategies, we demonstrate that, with slight modifications to the local modules, the proposed model can solve three different learning problems, including few-shot learning, continual learning, and fault-tolerance learning. Finally, we exploit the method of the algorithm-hardware co-designs to implement the hybrid model on Tianjic chips and further prove that it can fully utilize neuromorphic architecture to develop a hybrid computation paradigm.The proposed spike-based hybrid model exploits two streams of neuroscience experimental cues about synaptic modulation behaviors and a multiscale learning mechanism Fig.  to model35. In particular, some neuromodulators, such as adenosine, can affect the actions of synaptic functioning and other modulators in a hierarchical manner37. This indicates that neuromodulators can be formulized as a special type of meta-learning parameter acting on synaptic plasticity in a weight-sharing manner.First, in the hippocampus, local neural circuits can be simultaneously controlled by multiple types of top-down modulatory signals. These signals act on many synapses and modulate diverse plasticity behaviors, including the learning rate, update polarity, and plasticity consolidation38. The coupling of plasticity and neuromodulatory mechanisms plays a vital role in building complex behavior functions, such as muscle contraction39. It further implies a flexible and multiscale learning mechanism in which the learning process of neuromodulators and synapses may occur on different spatial and temporal scales; thus, they can be formulized as two types of variables with different learning manners in the optimization process.Second, neuromodulators can modulate synaptic plasticity on multiple temporal scales. Neuromodulators exhibit radically different evolution (learning) scales and individual functions from synapses40. On this basis, we decouple the learning process of 41 for optimizing To this end, we formulize the hyperparameters of LP, such as the learning rate and sliding threshold, as a group of meta-parameters 42 for handling the discontinuous dynamics of spiking neurons (see Methods). Then, we adopt the backpropagation through time (BPTT) algorithm as global learning for training SNNs. Because LP has an independent correlation-based updating manner, directly integrating the local modules with handcrafted parameters and global learning together is difficult to ensure convergence. To incorporate the impact of local weight updates into the entire optimization framework, we take a parametric modeling strategy  and neuromorphic datasets (including CIFAR10-DVS and DVS-gesture). The network architecture and training details are described in Methods.We analyzed the accuracy of the models on different types of datasets in Table 44 . Next, we show that HP SNNs can support rate coding and temporal rank coding schemes44 Fig. . Figure We further compared the convergence of the proposed approach with other related learning methods. Intuitively, since local learning and global learning have independent update methods, directly combining them cannot ensure convergence. Furthermore, regarding neuroscience-oriented learning models, such as STDP-based SNNs, systematically and generally configuring parameters over local learning rules has yet to be resolved. Although hand-designed or biosimulated hyperparameters can alleviate the above problems, it is time-consuming and difficult to ensure performance. By the parametrization of the local module, the HP approach can automatically optimize the local hyperparameters and achieve a synergic learning mode. As a demonstration, we comprehensively compared the loss curves and accuracies of a single LP network, a single GP network, a fine-tuning LP±GP network, and the proposed HP network in Fig. 45, it can act as a type of attention mechanism to the recent past with the strength being determined by the scalar product between the current hidden vector and earlier input stimulus. Combining the results of Table Figure 46 can quickly capture per-pixel brightness changes with low latency and a high dynamic range but suffer from the inherent noise of physical devices and the movements of external background objects, thereby affecting their practical performance. Next, we demonstrate that by virtue of Hebbian-based local modules les Fig.  and the les Fig.  and noisles Fig. . The modles Fig. . To obtales Fig.  and the les Fig.  between 11 reveal that the response of cortical neurons to a sensory stimulus can be reliably increased after just a few repetitions by virtue of local synaptic plasticity, indicating that such plasticity may play an important role in exploiting the correlation hidden in limited data. By integrating LP and GP learning, we expect that the synergic learning model can solve this problem through a two-fold mechanism: (1) abstract sufficiently discriminant representation of input data by the GP module; (2) find a useful inductive bias from a limited number of example pairs mainly by the LP module.We next investigate the potential of HP models for few-shot learning. In this case, the classifier must adapt to new classes not seen in the training phase when only given a limited number of samples from each class. To efficiently establish a mapping relationship from the limited data, it is vital to leverage prior knowledge or acquire inductive biases. The GP-based networks succeed in abstracting useful features; however, it is difficult for the networks to exploit the prior knowledge hidden in the limited datasets without resorting to other techniques. In contrast, the brain is highly efficient in learning from limited data. Neuroscience findings48 to abstract features and compared their performance. We also fed the training labels to the last layer as used by ref. 30, during the presentation time so as to help the network establish an input-to-output correlation via the LP module. In this manner, when a query sample is received, the LP module may provide an augmented signal based on the correlation-based inter-product of the query sample and the centers of each previously appeared sample. Figure Here, we used the Omniglot dataset to examine the performance of the proposed model. We adopted a widely used network structure49. Recent studies50 have shown that the motor cortex disinhibits a sparse subset of dendritic branches for new tasks, thereby reducing the disruption of network memory for previous tasks. It implies that the brain may multiplex some neuro-circuits while highly activating some synaptic connections to represent task-related information when solving new tasks. These motivate us to develop a distributed synergic learning paradigm, activating a sparse overlapping subset of weight connections by GP learning and modulating other synaptic connections by a task-sharing LP learning. Unlike the previous XdG method25 that uses a sub-network to solve a sub-task by masking parts of neurons in each task, our method uses a finer-grained synaptic modulation and a different synergic learning scheme. Here we allow the hybrid model to use a small number of overlapping connections to represent task-specific information, and LP learning to learn common features among tasks. By doing so, we expect to alleviate the disruption of network memory in different tasks and expand the learning capacity of the hybrid model to handle multiple tasks.To further explore the benefits that the HP model might provide, we further investigate the ability of HP models for continual learning, that is, an ability to learn new tasks without forgetting the previous tasks52. We ran all models five times and reported the testing results after fifty-task learning . Then we remodel the local weight update from the perspective of the optimization, and analyze the effectiveness of the approximate regularization based on hetero-associative memory (HAM)53. Similarly, Hebbian-based operations can help to encode the previous patterns triggering neuron concurrent firing behaviors into a local minimum in the energy landscape. On the one hand, GP learning ensures that the network can selectively activate neuron firing and realize the correct response to input patterns. This indicates that the neuron concurrent firing behaviors are more likely to represent an adequate response for the previous training patterns. On the other hand, LP learning can gradually decrease the energy surface at every update based on the concurrent firing behaviors . ThrougWe deliberate from the perspective of metric learning to discuss the model performance on few-shot learning. By clamping label signals into the local module, a correlation-based local module of the high-level input features and training labels can be established and a constraint can be placed with respect to the distribution of classes in the metric space. We prove that the local module can project an input pattern into a cosine-based embedding space and further produce a simple inductive bias by measuring the similarity between the query sample and the centers of each previously appeared sample (see Method). By doing so, the network is forced to learn from the embedding space representations to make the distance between samples within a class sufficiently small while the distance between samples from different classes is sufficiently large.Through the above analysis, we demonstrate that the LP and GP learning complement each other to form the synergic learning model. An interesting finding is that we can adapt the hybrid model to different tasks only with minor modifications to the local modules. It provides another way for the design of loss functions. Considering that the brain prominently uses local learning to perform tasks, transferring a part of the design of loss functions to local modules is instructive and can bring benefits from at least two folds: (1) it can reduce the number of manually designed hyperparameters in the overall loss functions, such as converting the original regularized weighting coefficients to model the learning rates of the local module; (2) the LP-based operation endows a network with online and high-parallelism properties, which especially facilitates the implementation on many-core neuromorphic hardware by the fine-grained parallelism architecture (see Discussion).By implementing our model on the Tianjic, next, we demonstrate that the proposed hybrid model is well suitable for implementing in the dedicated neuromorphic hardware. We first instantiated our model on the Tianjic neuromorphic chips (see Methods) and evaluated the efficiency of on-chip inferences. As indicated in Fig. 54. With the highly parallel and near-memory computing architecture of neuromorphic architecture, it can efficiently realize local learning and facilitate on-chip learning with high throughput , thereby evaluating the computational resources of on-chip hybrid learning. We evaluated the hardware efficiency using continual multitask learning. During this process, we mainly adopted four steps for the entire evaluation, including mapping design scheme, software tool configuration, simulating on-chip running process, and data arrangement (see Methods). Figure put Fig. .13, have embedded part of X86 cores in one single chip, which may provide a potential candidate for implementing GP in chips in the future. Thus, the combination of algorithm-hardware co-design is also a feasible direction to further develop a hybrid computing paradigm. For example, the main body of many-core structure can be used to perform local learning, while the embedded microprocessors are used to perform GP learning, which may improve hybrid on-chip efficiency and promote applications of the hybrid model.In addition, as indicated in Fig.In this work, we reported a spike-based hybrid model that endows SNNs with an efficient synergic learning capability for handling multiple learning scenarios. Guided by the hippocampal plasticity mechanism, we developed a brain-inspired meta-learning paradigm to integrate these two types of learning and further explore multiple synergic learning strategies that can quickly adapt the synergic learning model for solving different learning scenarios. Our results indicate that with small modifications of local modules, the hybrid synergic learning model can achieve significantly higher performances than single-learning models on sequential classification tasks, and three different learning scenarios. Finally, we implemented the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and demonstrated the advantages of the proposed hybrid model on neuromorphic chips.42 use the continuous relaxation of the gradients and provide a differentiable spiking network to update weights in a fully local computation. The e-prop7 algorithm combines the top-down supervision signals and local eligibility traces to approximate the backpropagation of signals through time. Unlike the previous studies, we use a meta-learning method to design the synergic learning circuit. We model the hyperparameters of local learning as a special type of meta-parameters that can be modified by top-down supervision signals and indirectly influence the behaviors of synapse plasticity. Furthermore, by deriving the implicit loss function from the LP, we prove that the local modules can act as a regularization over the network topology and temporal dynamics, indicating that the roles of LP and GP circuits are different from the perspective of optimization. We deliberate from the associative memory and metric learning as an illustration and demonstrate that by constructing different correlation-based modules, the hybrid synergic learning model can be linked with several existing powerful learning algorithms, thereby providing support for the effectiveness analysis.Understanding how the interconnected neurons in the brain combine top-down modulation information and bottom-up local information to learn to solve tasks is an active research area in both neuroscience and machine learning. The related surrogate gradient methods29 has proposed a basic framework that can optimize the parameterized local learning rule by global supervision signals. Several recent work55 use trainable connection weights of local modules and established large-scale non-SNNs. We adopt the basic idea of learnable LP as in refs. 55. One main difference from the previous works is that we optimize underlying weights and LP by the bilevel optimization, and develop a configurable hybrid strategy that can exploit synergic learning in different ways with greater flexibilities in solving many other learning scenarios. More importantly, we incorporate various spiking dynamics for establishing hybrid neuromorphic models and exploit the algorithm-and-hardware co-designs to develop feasible hardware implementations for online hybrid learning. Through the combination with neuromorphic computing, many unreported advantages of global-local learning are revealed, such as the fault-tolerance on neuromorphic sensors and the complementary computational efficiency on neuromorphic chips.Some meta-learning-based global-local learning methods have been developed for non-spiking models. In the early 1990s, ref. 56. By deriving from neuronal dynamics and incorporating various dynamic behaviors of spiking neurons, our model retains many prominent biological attributes and provides a general method to meta-learn spike-based LP  for acceleration and directly input them into SNNs. We applied the batch normalization (BN) technique to convolutional layers on the DVS-Gesture dataset by following the work62. We optimized the HP model in all datasets using the mean square error (MSE) and the adaptive moment estimation optimizer (ADAM). A four-layer MLP with [28-256FC-256FC-10] was trained on sequential MNIST, a six-layer CNN with [input-128C3-AP2-256C3-AP2-256C3-AP2-512FC-10] was trained on MNIST and Fashion-MNIST datasets, a nine-layer CNN with [input-64C3S2-BN-128C3S1-BN-256C3S1-BN-256C3S1-BN-256C3S1-BN-256C3S1-AP2-800 FC-512FC-11FC] was trained on DVS-Gesture datasets, and a nine-layer CNN with the CIFARNet structure59 was trained on CIFAR10 and CIFAR10-DVS. We took the local learning rules in Eq. , where nl value refers to the In the cropping experiment, we increased cropping area gradually on the center of each image or each NVS frame, denoted by 31 to configure the network parameters and divide the training sets and testing sets. During the training phase, we sampled three task episodes from the task distribution θ by re-sampling from the same tasks. To reduce the computation, we equipped the local module in the fully connected layers. The training label was fed into the last classifier layer by the one-hot coding scheme to guide the correct classifications. We adopted the encoding strategy as used ref. 59 to produce spike trains. We trained the network of 3,000,000 episodes and reported the best results over the last 1000 episodes.The Omniglot is a standard few-shot learning dataset that contains 1623 categories and each category contains 20 samples. In one training episode, we first randomly selected N classes and sampled S sample pairs from each class . Then, we fed the S-labeled samples (named by the presentation time) into the classifier. After that, we randomly sampled a new but unlabeled instance from the same N classes and queried the classifier for the labels. We used four convolutional layers with 3 × 3 kernel size and two strides, followed by a fully connected layer and an N-way classifier layer. We followed the work52 and applied to spiking models.The shuffled MNIST experiments include multiple image classification tasks. All tasks are to classify handwritten digits from zero to nine. Each task is a variant of the MNIST dataset with a different permutation. For each new task, the image pixels were randomly permuted with the same randomization across all digits and different randomization are used in different tasks. We trained each task by ten epochs. We adopted a four-layer spiking network with [784-1024-1024-10] structure and minimized the MSE loss by the ADAM. During the training process, we fixed the meta-parameters of the local module and randomly generated a sparse and fixed connection matrix to receive supervision signals. We used the BPTT to update these sparse weights and local learning to update other connections. After each task is learned, we fixed the weights and updated the hyperparameters of the local modules for one epoch. Other comparison methods were adopted from the corresponding publicationsThe Tianjic chip is a cross-paradigm neuromorphic computing platform that supports a broad spectrum of neural coding schemes, computational models, and network structures. It is fully digital and fabricated using 28-nm high-performance low-power technology. Each Tianjic chip contains 156 functional cores (FCores), which are arranged in a 2D-mesh manner. Each FCore contains a group of neurons, a group of axons, and synaptic connections between them. Among the FCores, spikes can be transmitted to one or more cores in the mesh through the routing network in a form of routing packets. At the same time, through the inter-chip communication interface, multiple chips can expand the internal routing network connections into a larger computing platform.We mapped the model onto multiple FCores. Different FCores are configured to perform different basic operations and transformations. Taking the MNIST dataset as an example, we deployed 70 FCores to implement a fully connected structure [784-1024-1024-10]. We tested the energy consumptions with different coding schemes. Because the rank coding shortens the average decision time, it can effectively reduce the on-chip inference latency and the average compute ratio, thereby alleviating average dynamic power consumption. The average on-chip inference latency required for rate coding and rank coding are 0.27 and 0.18 us, the compute ratios are 0.63, 0.45, and the dynamic power consumptions are 0.48 and 0.38 W, respectively. We reported the on-chip performance on MNIST, F-MNIST, and N-MNIST datasets and compared it with GPU-based running results in Supplementary Table The cycle-accurate simulator can well capture the hardware chip properties at runtime and is commonly used for chip evaluation. We based on Tianjic’s hybrid structure to design an on-chip hybrid learning scheme and a feasible cycle-accurate hardware simulation scheme to evaluate on-chip computational resources. Here we took an extended version of Tianjic chip with re-configurability and functionalities to support continuous execution of multiple operations (see Supplementary Note Supplementary Information


Several hematopoietic cells of the immune system store large amounts of proteases in cytoplasmic granules. The absolute majority of these proteases belong to the large family of chymotrypsin-related serine proteases. The chymase locus is one of four loci encoding these granule-associated serine proteases in mammals. The chymase locus encodes only four genes in primates, (1) the gene for a mast-cell-specific chymotryptic enzyme, the chymase; (2) a T-cell-expressed asp-ase, granzyme B; (3) a neutrophil-expressed chymotryptic enzyme, cathepsin G; and (4) a T-cell-expressed chymotryptic enzyme named granzyme H. Interestingly, this locus has experienced a number of quite dramatic expansions during mammalian evolution. This is illustrated by the very large number of functional protease genes found in the chymase locus of mice (15 genes) and rats (18 genes). A separate expansion has also occurred in ruminants, where we find a new class of protease genes, the duodenases, which are expressed in the intestinal region. In contrast, the opossum has only two functional genes in this locus, the mast cell (MC) chymase and granzyme B. This low number of genes may be the result of an inversion, which may have hindered unequal crossing over, a mechanism which may have been a major factor in the expansion within the rodent lineage. The chymase locus can be traced back to early tetrapods as genes that cluster with the mammalian genes in phylogenetic trees can be found in frogs, alligators and turtles, but appear to have been lost in birds. We here present the collected data concerning the evolution of this rapidly evolving locus, and how these changes in gene numbers and specificities may have affected the immune functions in the various tetrapod species. Cells from several of the hematopoietic cell lineages store large amounts of proteases within cytoplasmic granules . They armet-ase locus, the MC tryptase locus, the MC chymase locus and the T cell tryptase locus, also called the granzyme A/K locus. These loci are in mammals often located on four different chromosomes, indicating that some of them may originate from whole-genome duplications, so-called tetraploidizations. Two such tertraploidizations most likely occurred during early vertebrate evolution. The original locus was therefore most likely present before these evolutionary important events . Th. Th19]. T, whereas CTMCs express both tryptase and chymase and are named MCTC. Notably, MCTC also express cathepsin G and both MCT and MCTC can express CPA3 at the mRNA level, but CPA3 protein is only found in MCTC . T. T38]. TProteases can be classified based on their preference for cleaving certain types of amino acids in the P1 position of the substrate. For example, enzymes that cleave after aromatic amino acids  are generally classified as having chymotrypsin-like activity. These enzymes are among the hematopoietic serine proteases usually named chymases or stated to have ‘chymase activity’. Enzymes cleaving after basic positively charged amino acids such as arginine and lysine are instead classified as having trypsin-like activity and among hematopoietic serine proteases named tryptases (having ‘tryptase activity’). Moreover, enzymes that cleave after small aliphatic amino acids are named elastases (having ‘elastase activity’), enzymes that cleave after leucine are named leu-ases, enzymes that cleave after methionine are named met-ases, and finally enzymes that cleave after negatively charged amino acids such as aspartic acid or glutamic acids are called Asp-ases. However, there are exceptions to this rule, often for historical reasons. For example, cathepsin G is a chymase, whereas mMCP-5 and rMCP-5 are classified as α-chymases based on sequence homology to the human chymase, but they are elastases, due to mutations in the S1 pocket.Using phage display, we have determined the extended cleavage specificity of a number of the chymase-locus-encoded proteases. The extended specificity is a more detailed description of the specificity of a protease, also involving residues surrounding the cleavage site, often 4–5 amino acids N- and C-terminally of the actual cleavage site. Using phage display, the extended specificity has been determined for the human α-chymase, human cathepsin G, and the α-chymases of macaques and dogs ,32,33,39The specificity of a large number of the rodent chymase-locus-encoded enzymes have also been determined via phage display. The specificity of both α-and β-chymases from rats, mice and golden hamsters has been analyzed in detail ,43,44,45Interestingly when analyzing the β-chymase in rabbits and the α-chymase in guinea pigs (Cma1-like and Cma1), we can see that they have both become strict Leu-ases and are no longer classical chymases  49,50].,50.49,50Sheep have one α-chymase but no β-chymase . AnalysiIn the opossum there are only two enzymes encoded in the chymase locus, one chymase and one granzyme B homologue. Upon analysis of the opossum chymase via phage display we found that it has a similar specificity as the human α-chymase; however, it has one major difference, seen in the P1 position of the substrate, where the opossum enzyme prefers Trp over Phe and Tyr, whereas the human enzyme prefers Phe and Tyr  42]. Us. Us42]. All three chymase-encoded enzymes in the platypus have been studied by the use of a panel of recombinant substrates. Several attempts with phage displays did not produce sufficient amplification during the panning, for unknown reasons, which is why the recombinant substrates were used as an alternative method to obtain information on their extended specificity. We found that two of the platypus enzymes are chymases, named granzyme B and DDN-1-like, and exhibit very similar specificity to that of the human chymase. They both show preferences for Phe and Tyr over Trp and Leu in the P1 position and also show preferences for aliphatic amino acids both N- and C-terminally of the cleavage site . The thiWe do not yet have any information on the specificity of the reptile, bird and amphibian enzymes of the chymase locus even though we have produced three of them as recombinant enzymes: Xenopus CTSG and GzmH, and Chinese alligator MCP1A-like enzymes. Several attempts, using either phage displays or chromogenic and recombinant substrates, have not resulted in any information of value, which is why more detailed information concerning the extended specificity of these enzymes has only been obtained for their mammalian counterparts.The P1 specificity of the mammalian enzymes can be predicted based on the fact that enzyme specificity amongst the members of the large family of chymotrypsin-related serine proteases has been found to strongly correlate with the substrate binding pocket (the S1 pocket), where residues 189, 216 and 226, according to chymotrypsinogen numbering, play a key role. These residues are highly complementary to the substrates, determining the primary specificity see B. For exIn a recent study we have looked into the new subfamily of chymase-locus-encoded proteases in ruminants and pigs, the duodenases . These pchymase locus, we have performed a larger phylogenic analysis of the majority of the chymase locus genes included in To study the relatedness in sequence between the different enzymes encoded from the All the chymase locus genes form a larger branch, separated from enzymes of the other hematopoietic serine protease loci, including the genes within the met-ase, the T cell tryptase and the mast cell tryptase loci A. The sechymase-locus-encoded enzymes, we have aligned their amino acid sequences and looked at the three residues  forming the S1 pocket . Moreover, the importance of these additional proteases is strongly indicated by the seemingly independent amplifications of these genes in several rodents, including mice, rats, hamsters and guinea pigs. The large difference in the numbers of genes and positions of genes within the chymase, mMCP-8-like, cathepsin G and granzyme subfamilies strongly indicate independent duplication processes. On the other hand, the opossum, a marsupial with a very similar lifestyle to that of rodents, have only two chymase-locus-encoded genes, the mast cell chymase and granzyme B. The apparent question is then why the opossum, with a similar lifestyle to that of rodents, can manage with only two chymase-locus-encoded serine protease genes. We have some indications by PCR analysis of skin and intestinal tissue samples that gene amplifications may have occurred in other serine protease loci to compensate for the low number of proteases in the chymase locus of the opossum. However, this indication needs to be further analyzed before we can make any more definitive conclusions. In the opossum skin samples, we have found PCR fragments for proteases related to pancreatic digestive enzymes. Interestingly, an apparent expression of pancreatic elastase has been demonstrated in human skin tissue, indicating a similar process in primates, which also have fewer chymase locus genes . The horA crucial step in uncovering the beneficial role of these evolutionary “new” genes of the chymase locus will be to identify their true biological targets. For example, we found that the new mucosal mast cell specific β-chymases, mMCP-1 in mice and rMCP-2 in rats, can cleave cell adhesion proteins, and so they are most likely involved in the opening of the intestinal mucosa as part of the defense against infections . The relThe beneficial effects of the increase in the number of protease genes in the chymase locus of ruminants may be easier to understand. Cattle and sheep have three and five additional functional duodenase genes, respectively. These duodenases are all located within the chymase locus and they all belong to a completely new subfamily of chymase-locus-encoded protease genes ,60. ThesThis and earlier studies of the chymase locus have shown that a large number of gene amplifications and subsequent diversification in cleavage specificity have occurred among these enzymes during tetrapod evolution, showing that this locus is actively responding to changes in lifestyle and environment. Although large differences between species are seen in the overall pattern of this locus, the mast cell chymase and granzyme B are two genes that seem to be highly conserved, especially during mammalian evolution. These enzymes exhibit similar specificities in all studied mammalian species from platypus to humans, with two exceptions being rabbits and guinea pigs  49]. In. In49]. Another topic of interest is the presence of two mMCP-8 like genes in the horse that cluster with the rodent mMCP-8-like genes in the phylogenetic tree . This isDetailed analyses of the extended cleavage specificities of a large number of the chymase-locus-encoded proteases from different species have resulted in initial tools to predict the in vivo substrates of these proteases. However, sensitivity to folding and thereby accessibility to potential cleavage sites also play a major role in determining which targets will be cleaved or not. A more detailed analysis of the cleavage of potential targets is therefore needed before we can say with certainty that they are likely targets. This was particularly evident during the analysis of the cleavage by the human mast cell chymase and human cathepsin G of a large panel of cytokines and chemokines . Only thBy analyzing the three amino acids forming the S1 pocket of the serine proteases encoded from the chymase locus of amphibians and reptiles, we can see that a number of these enzymes have triplets, indicating that they are true granzyme B homologs. Such potential homologs were found in four out of the five species studied, the two frog species, the Chinese alligator and the G-Turtle . This st


Complex inter-bacterial interactions largely influence the structure and function of the gut microbial community. Though several host-associated phenomena have often been shown to be involved in the stability, structure, and function of the gut microbial community, the implication of contact-dependent and contact-independent inter-bacterial interactions has been overlooked. Such interactions are tightly governed at multiple layers through several extracellular organelles, including contact-dependent inhibition (CDI), nanotubes, type VI secretion system (T6SS), and membrane vesicles (MVs). Recent advancements in molecular techniques have revealed that such extracellular organelles function beyond exhibiting competitive behavior and are also involved in manifesting cooperative behaviors. Cooperation between bacteria occurs through the sharing of several beneficial molecules including nucleic acids, proteins, metabolites, and nutrients among the members of the community, while competition occurs by means of multiple toxins. Intrinsic coordination between contact-dependent and contact-independent mechanisms collectively provides a fitness advantage and increased colonization resistance to the gut microbiota, where molecular trafficking plays a key role. This review is intended to provide a comprehensive view of the salient features of the different bacterial interactions and to highlight how microbiota deploy multifaceted organelles, for exerting both cooperative and competitive behaviors. We discuss the current knowledge of bacterial molecular trafficking and its impact on shaping the gut microbial community. Interactions between bacteria are a prerequisite for the trading of QS traits or for antagonistic molecules to maintain the healthy physiology of the host, thereby mitigating the effect of the pathogens.The gut consists of a dynamic environment that accommodates polymicrobial communities. Bacteria within the polymicrobial communities change their behavior in response to the fluctuating signals and metabolites.6 Genes that are present on mobile genetic elements (MGEs) could confer adaptive attributes to the microbiome, such as antibiotic resistance, detoxification of bile salt, degradation of mucus, biosynthesis of capsular polysaccharides, utilization of polysaccharides, and sporulation.7 According to the Black Queen hypothesis, bacteria usually undergo genome reduction when they acquire essential nutrients from the surrounding environment, which may be supplied by the other members of the community.8 Conceivably, there has been a correlation between nutrient availability and bacterial interaction, wherein abundant nutrient concentrations lead to negative interactions between microbes.9 In support of this, sequence analysis has revealed that gut bacteria lack at least one metabolic pathway in their genome, due to which 64% of the tested gut bacteria have been found to be auxotrophs, leading them to depend on prolific external resources for their survival and growth.11 Therefore, the acquisition or loss of a specific biosynthetic pathway in bacteria has been found to cause metabolic dependency on the surrounding environment,13 which is likely to implicate obligate cross-feeding mechanisms. It is therefore tempting to speculate that the functionality of the microbiome tends to change with respect to the acquisition of genetic material/loss of essential genes. Consequently, genotypic heterogeneity emerges within the same species, which results in metabolic interdependencies.Intrinsic interactions between microbiota enable them to display synchronized group behavior to produce several molecules, including polysaccharide-utilizing enzymes, siderophores, toxins, biofilm, and other QS traits. Such public goods are meant to be shared among the members of the community, which provides fitness to the participating members of the community. Additionally, genome plasticity also critically determines the physiology of the microbiota. The gut microbiome evolves in response to the changing environment by acquiring foreign DNA, which either increases the fitness of the microbiota through the adaptation of novel metabolic genes or escalate detrimental effects by disseminating antibiotic resistance genes to pathogens.14 Since QS signals get diffused in the fluid flow, bacteria at the outer edge of the community need to utilize an alternate mechanism for successfully initiating QS. Further, owing to the existence of such diverse and heterogeneous chemical environments, cells within the spatially organized bacterial population are unlikely to have equal access to their essential nutrients,15 which forces them to depend on each other for nutrients. Therefore, heterogeneity among the clonal population has necessitated inter-bacterial interactions for the sharing of multiple essential commodities.The gut is attributed to have peristalsis with a fluid flow. Bacteria residing in such a dynamic environment experience more fluid flow than those at the center of the community, leading to heterogeneity in the QS.viz., contact-dependent and contact-independent. Major secretory machineries of the bacteria, which include CDI, T6SS, T7SS, nanotubes, and MVs, have been previously considered as weapons that could merely translocate toxins into the target cells. However, with the advent of recent molecular techniques, our current knowledge has improved to elucidate the cooperative behaviors of such machineries, affirming that they are being utilized by bacteria for exerting dual behaviors. To the best of our knowledge, until now, no comprehensive review has been published on the multiple secretory machineries of bacteria. Specifically, the possible existence of such machineries in human gut-associated microbes for exerting both cooperative and antagonistic behaviors. In the present review, we aimed at collating current knowledge of complex inter-bacterial interactions that are likely to occur in the gut-associated microbial community through multiple machineries. We also provide evidence for the impact of such secretory machineries on shaping the gut microbial community.To survive in such metabolically heterogenic environments, bacteria adopt various mechanisms to interact with neighboring cells. These strategies have been broadly classified into two categories 15 Cell-to-cell interactions thus become an imperative attribute to coordinate metabolism and division of labor. However, cell-to-cell interactions and kin recognition are essential for the coordination of multicellular function, for the microbiota to co-localize their potential and suitable partners for establishing stable and continuous interaction over a period of time. Spatial organization of the gut microbiota is important for establishing physical interaction that determines the function of the community.16 The following sections essentially discuss the different types of contact-dependent interactions reported in bacteria.The contact-dependent interactions are very essential and are considered as primary means of successful community existence and functions. The exchange of essential metabolites between cells is likely to occur when the group of cells persists as aggregates.17 Bacteria utilize the CDI system to translocate the toxin domain of CdiA into the neighboring cells, once cognate receptors are recognized on the surface of the target cells.18 Self-bacteria are successful in neutralizing the effector proteins with the aid of immunity proteins, whereas non-self types are susceptible to the toxins. Bacteria utilize such CDI system for exerting both inhibitory activity and kin recognition. It was believed that CDI-mediated interaction occurs between closely related bacteria, whereas recent report suggests that cross-species effector delivery also occurs due to the promiscuous nature of the class II CdiA receptor-binding domain.19 They found that Enterobacter cloacae deliver effectors into diverse Enterobacteriaceae species such as Escherichia, Klebsiella, Enterobacter, and Salmonella spp. Thereby, CDI shapes the community composition of Enterobacteria spp. in the niche, suggesting that CDI mediates cross-species interaction for kin recognition and competition, and might thereby determine the structure and function of the local community.Contact-dependent inhibition (CDI) is considered as a well-known example of a subset of type V secretion system (T5SS).26 Such CDI mediated transcriptional changes have been termed as contact-dependent signaling (CDS). It was perceived that CDS might function as a fine-tuning mechanism, playing a pivotal role in structuring the bacterial community.26Multiple studies in this domain demonstrated that the CDI system is also found to be involved in social behaviors, which are distinct from their regular competitive behavior . A positcdi loci are present on the genomic island, revealing that cdi toxin/immune system is likely to be transferred between bacteria through HGT.18 This was confirmed by the presence of the CDI system in probiotic Escherichia coli (Nissle 1917).27 It could be conceived that the CDI system has been the characteristic attribute of not only pathogens but also of gut microbiota. As a consequence of HGT, bacterial genome have frequently been found to consist of many orphan cdiA/cdiI modules at the downstream of the CDI system,28 denoting that CDI system provides an additional fitness advantage to the harboring cells. The presence of multiple cdi loci in a cell corroborates that they work together synergistically and exhibit strong activity against competitors even at the low expression of CdiA.29 The CDI system was found to be different among cells and within species due to the polymorphic nature, which preferentially selects members of the local community.20 As a result of HGT, cdi genes are widely present in genetically unrelated species.30 The CDI system was found to be involved in aggregation, where the adhesion of CdiA–CdiA exoprotein allows the aggregation of CDI+ harboring cells.31 Thus, such receptor-independent auto-aggregation co-localizes genetically diversified bacteria, where cdi genes-acquired cells score over other CDI‒ cells. It could be conceived that CDI-mediates both inter- and intra-species interaction. However, CDI-mediated inter-kingdom interaction has not yet been studied. The cooperative phenotypes effectively communicate with each other if they are present in an aggregated form in the niche. Therefore, it is postulated that CDI also helps in QS by assorting the CDI+ cells at a niche, where secreted QS signaling molecules could reach the intended bacteria to activate QS. The CDI-mediated biofilm induction denotes the intrinsic interconnection between the CDI and QS. CDI-mediated competition is effective when CDI+ cells are present in the niche at high cell density. This was in turn found to cause increased cell–cell interaction and a swift inhibitory effect on target cells, thereby restricting their expansion and influencing the composition and spatial arrangement of the bacterial community.17 Therefore, co-localization and cell-to-cell contact between bacteria are crucial factors in determining the physiology and ecology of the microbial communities.Interestingly, + cells in the niche by means of CdiA–CdiA interaction and CdiA-receptor interaction, which subsequently facilitates the delivery of effectors to the competitors of the niche, thereby promoting initial colonization.31 (ii) Protecting microcolonies from invading competitors through inter-species toxin delivery, for which bacteria utilize class II CDI system.19 By using several bacteria as model organisms, it was found that the CDI system is essential for structuring the community.33 It is evidently proved that bacteria, which are defective in the CDI system, are unable to architect biofilm.33 Thus, the CDI system governs spatial arrangement, composition of the community, as well as the behavior of bacteria, which includes cell–cell aggregation, biofilm formation, and modulation of transcriptomes in the recipient cells, besides stress tolerance. Therefore, the CDI system might be an unexplored phenomenon in gut microbiota, which provides fitness to CDI+ cells by regulating both cooperative and competitive behaviors. Though CDI-mediated interaction has been demonstrated in governing the composition of the local community, the impact on the entire gut-microbial community in an in vivo condition has not yet been explored. Therefore, understanding the impact of CDI in manipulating the structure and function of the gut microbial community will broaden our knowledge toward developing novel strategies.CDI could play an essential role in governing community composition and function through two mechanisms: (i) colocalization of high-density CDI34 T7SS utilizes effectors-immunity (EI) repertoires that usually occur with large variations within the same species, revealing their role in kin recognition and competition.34 Comparative genomic analysis of Streptococcus spp. revealed that T7SS encoding machinery is located on genomic islands,35 suggesting that T7SS might exist in different species of gram-positive bacteria, allowing cross-species interaction.Contact-dependent mechanisms have also been found in host-associated gram-positive bacteria. Gram-positive phylum Firmicutes possess T7SS, which is functionally equivalent to T6SS of gram-negative bacteria.23 The LXG proteins-mediated antagonism effectively defines the composition of the community that is rich in Firmicutes. Commensal Streptococcus intermedius produces three different LXG family of polymorphic toxins such as TelA, TelB, and TelC, along with their respective immunity proteins TipA, TipB, and TipC to neutralize self-intoxication. Toxin-antitoxin systems thus provide a fitness advantage to the producer organisms. Wild-type cells have been found to utilize T7SS to intoxicate TelC-susceptible cells in a contact-dependent manner.23 T7SS-mediated antagonistic effect has been observed between closely related gram-positive organisms such as S. intermedius (defective in producing immunity proteins such as tipA and tipB), S. pyogenes, and E. faecalis, whereas such activity has not been shown significantly against gram-negative species belonging to the proteobacteria and bacteroidetes phylum.23 Thus, T7SS mediates inter- and intra-species interaction, suggesting their role in determining the composition of gram-positive bacteria in the community. Due to its versatile nature, T7SS also mediates interaction with host cells for the exchange of virulence factors.36 Numerous immunity proteins are accumulated in a taxonomically distinct group of microbiota, suggesting that microbiota increase their survival fitness by avoiding intoxication using polyimmunity loci or polyimmunity proteins.37 Thus, diverse toxin-antitoxin modules, acquired through HGT, serve as a common reservoir for several secretory systems utilized by bacteria for the selection of members in the community.38 For instance, the MuF toxin family has been found in the temperate phages of Firmicutes.39 The mining of toxin diversity in gut microbiome and identifying their unknown biochemical function could help us to develop a novel strategy to curb pathogens. However, the global impact of T7SS machinery on the structure and function of gut microbiota has not been studied so far. Unraveling the link between gut microbiota and T7SS will provide novel insights that could spur our research in a new dimension.Genes belonging to the LXG protein family are abundantly present in different species of Clostridiales, Bacillales, and Lactobacillales, in which most of the gut microbiota are classified.40 Such tubes serve as intercellular conduits for the exchange of various cytoplasmic molecules, thereby displaying co-operative or antagonistic behaviors toward neighboring bacteria.40 Certain biomolecules are unable to cross the bacterial membrane due to their unique biochemical characteristics.15 In such cases, nanotubes might serve as a channel for the transportation of chemically diverse molecules between cells. The membrane integrated proteins of the CORE complex, components of type III secretion system (T3SS), were found to serve as a platform for the assembly of both nanotubes as well as flagella in gram-positive bacteria.41 Interestingly, the orthologue of CORE complex has been involved in injectisome formation in gram-negative bacteria.42 Since CORE complex is functionally conserved among different bacterial species, nanotubes-like structure were reported in diverse commensal guts.43–46 Not surprisingly, several enteric pathogens were also reported to produce nanotubes-like structure,47–50 denoting that nanotube formation could be an inherent nature of many bacteria. It has been frequently observed that microbiota eliminate enteropathogens from the niche in a contact-dependent manner. For instance, contact-dependent interaction between Salmonella enterica subsp. enterica serovar Typhimurium and fecal bacteria has led to the loss of viability of S. Typhimurium.51 Similarly, Lactococcus piscium was found to inhibit the growth of Listeria monocytogenes in a contact-dependent fashion.53 Perhaps commensals kill those pathogens by translocating toxic molecules through the nanotube network. However, the stability and functions of nanotubes for active molecular trading in vivo conditions needs to be further elucidated. Apart from molecular trading, nanotubes are perceived as an anchoring factor required for cell-surface and cell–cell attachment.54Recently, a novel type of bacterial communication has been discovered, in which bacteria establish physical contact with the neighboring cells through conduits.21 Moreover, auxotrophs acquire essential amino acids from the donor cells through nanotubes, which results in the restriction of feedback inhibition of the respective amino acid biosynthetic pathway. Thus, auxotrophs enable the overproduction of amino acids in donor cells.55 Therefore, the network of nanotubes provides a selective fitness advantage to bacteria to adapt to the environment.56 In line with other sensing mechanisms, nanotubes are also likely to be involved in discriminating self and non-self cells through EI repertoires , in which GA1 and GA2 are often seen on the conserved integrative conjugative elements and disseminated among different species of gut Bacteroidales.60 Metagenomic analysis of human gut samples revealed that GA3 has been found specifically in Bacteroides fragilis.61 They also found that GA3 containing B. fragilis is more common in the gut microbiome of infants than in adult gut samples, suggesting that T6SS critically governs the gut microbiome composition at an early stage of life. The protein composition of T6SS of proteobacteria differs from that of Bacteroidetes,62 suggesting that pathogens and commensals could be relatively discriminated based on architectural proteins.T6SS is a complex and well-characterized nano-machinery in gram-negative bacteria, which was found to intoxicate target cells in a contact-dependent manner. It was believed that T6SS is restricted to proteobacteria, which are minor members of the gut microbial community. Recently, T6SS was also found rich in the order of Bacteroidales revealed that GA1 and GA3 loci have been found in the same genome of the bacteria, whereas GA2 T6SSs have not been observed in the genome along with either GA1 or GA3.63 It is therefore conceivable that the presence of multiple T6SS loci in the genome could confer strong protection against invaders. GAs of T6SS loci of gut microbiota have been found to contain variable regions that encode diverse toxins, which appear to be functionally different from the known repertoires.63 The distribution of T6SS EI pair in human gut microbiome samples encourages the notion that this pathway plays a large role in defining the members of the community through competition and selection. Due to its versatile nature, T6SS has been found to mediate microbe–microbe interaction in an intra- and inter-species manner, as well as host–microbe interaction.64B. fragilis has been known to utilize T6SS to discriminate closely related organisms.25 For instance, symbiotic non-toxigenic Bacteroides fragilis has been reported to exhibit colonization resistance against enterotoxigenic B. fragilis through strain-specific competition using GA3 T6SS, thus protecting the host from the disease colitis.65 It could therefore be postulated that T6SS-mediated interaction confers protection by eliminating local competitors and precisely manipulating the community composition. T6SS has been found to exhibit a profound impact on the gut microbial community, in which GA3 T6SS mediated firing of effectors exceeds 109 times min−1 gram−1 of colonic content.66 It is, therefore, speculated that GA3 can precisely manipulate the composition of the Bacteroidales in the community. However, the role of GA1 and GA2 in determining the structure and function of the community is not yet understood clearly.Genome analysis of ‒ cells. GA3 type of T6SS favors the abundance of Bacteroides in the niche. The abundance of Bacteroides is closely associated with the presence of T6SS+ cells, whereas Oscillospira, Faecalibacterium, and Ruminococcus from the Firmicutes phylum are negatively associated with T6SS+ cells.61 Thus, T6SS is critically involved in determining the composition of the gut bacterial community. T6SS also delivers toxins in a contact-independent manner,67 which allows them to exhibit antagonistic activity against a broad range of bacteria. This implies that irrespective of kin and non-kin cells, T6SS can deliver effector proteins to all the nearby cells. Kin cells produce immunity proteins that neutralize cognate toxins. Thus, T6SS increases the fitness of the producer cell through competition with other bacteria for niche and nutrients. Thus, T6SS precisely manipulates the composition of Bacteroidales through antagonistic activity and kin selection.Recent study using metagenomic data suggests that the members of the bacterial community differ between the samples that contain T6SS+ cells and T6SSBacteroidales members harbor T6SS encoding machinery along with acquired inter-bacterial defence (AID) gene clusters that provide immunity against different T6SS effector proteins in inter-species and intra-species manners.68 Orphan immunity genes that confer protection against T6SS-mediated antagonism are widely present in the human gut microbiome, probably as a result of HGT. Nevertheless, surprisingly, more than 50% of the searched Bacteroidales genomes are known to possess recombinase associated AID, which provides ecological fitness to the immunity genes harboring cells. Therefore, the acquisition and preservation of an orphan immunity system in the genome of the Bacteroidales is a common mechanism to inhibit competing bacteria.68It has been well documented in the literature that certain 22Proteus mirabilis is a low abundant gut microbiota in some human beings, which can swarm outward and exhibit a visible boundary when they meet non-self cells. However, swarms of the same group of cells merge on the solid medium.69 Such cell–cell recognition is typically governed by ids and idr gene clusters, which encode self-identity proteins and rhs-related products, respectively.70 It was found that proteins such as IdsD and IdE function as strain-specific self-identity determinants.69 Interestingly, it may be noted that T6SS is involved in such cell–cell recognition by exporting self-identity determinants from one cell to another cell.70 The recognition of self-cells occurs when IdsD from the producer cells interact with IdsE of the sibling cells. Thus, the binding of such nonresident IdsD with resident IdsE leads to the merging of the population.71 In contrast, nonresident IdsD remain unbound in non-self cells, resulting in the shift of their lifestyle of incompatibility to co-exist with self-cells. Consequently, non-self cells transiently become tolerant to antibiotics and also display differential gene expression that keeps away those non-self-cells from participating in the production of social traits.72 The metagenome of the human gut microbiome has revealed that several variants of IdrD-like genes exist at the lower level, suggesting that IdrD could be the hallmark of low abundant bacteria to survive in a competitive environment.73 The display of self-identity determinants provides competitive advantages to the cells due to cell–cell interaction that collectively provides strength in yielding coordination across the clonal population for exerting territorial behavior.70Surprisingly, T6SS is also involved in exhibiting social behaviors. Bacteria are capable of perceiving T6SS-mediated attacks from neighboring cells, by which they might determine the surrounding population to exert social behaviors.74In silico analysis has revealed that the genomes of proteobacteria and Bacteroidetes harbor genes for public goods production, which is positively correlated with the increasing number of T6SS.74 Notably, secretion of essential proteins and metabolites through the T6SS machinery possibly allows access to other clonemates, regardless of their contribution in the production of those proteins.75–77 Thus, sharing of public goods among the members of the community also facilitates cooperation. The T6SS-mediated biofilm formation and motility have also been reported in different sets of bacteria.79 Thus, the involvement of T6SS has been confirmed in social behavior through cell–cell communication, biofilm formation, securing public goods from cheater cells, nutrient acquisition, and HGT. Since T6SS was prevalently found in the order of Bacteroidales, such group behaviors could also exist in gut commensal bacteria. Therefore, studying T6SS-mediated cooperation among gut microbiota will delineate its physiological role in the complex microbial community. Further, identification of the factors required for T6SS-mediated inter-bacterial interaction will provide an opportunity to develop novel tool kits to control pathogen expansion. Since T6SS manipulate the composition of Bacteroidales, which is the most abundant gut microbial community, utilization of T6SS for a therapeutic purpose could be a venture with future potential. Altogether, contact-dependent interactions have unique attributes  directly utilizes imported inulin for its fitness. It is surprising to note that neighboring Bacteroides vulgatus (BV) utilize digested products of inulin available in the niche due to outer surface glycoside hydrolases of BO. Further, reciprocally, BV has been found to provide a beneficial effect to BO by detoxifying inhibitory molecules or releasing growth-promoting factors.113 Cross-feeding of nutrients has been demonstrated between Bifidobacterium adolescentis and butyrate-producing anaerobes.114 Such reciprocal cross-feeding might be facilitated by MVs. The fitness of the gut microbiota increases when they utilize MVs associated public goods. A large number of Bacteroides species have been found to produce MVs which carry surface-associated cephalosporinase that degrade β-lactam antibiotics in the vicinity. Thereby, MVs protect commensals and pathogens from β-lactam antibiotics.115 In response to β-lactam antibiotic imipenem, Stenotrophomonas maltophilia secrete β-lactamase containing MVs that degrade β-lactam from the vicinity and confer protection not only to their clonemates but also cohabitants. Thus MVs provide protection in an intra- and inter-species manner.117 QS signal Pseudomonas quinolone signal (PQS) also functions as an iron scavenger. Notably, MVs specifically deliver the cargo to the target cell. Enterobacterium Buttiauxella agrestis produces MVs that selectively interact with the same genus.118 Likewise, P. aeruginosa secretes probable T6SS substrate TseF, that directly binds with PQS-harboring MVs. The interaction of TseF bound MVs with Fe(III)-pyochelin receptor FptA and the porin OprF, facilitates the delivery of iron molecules to specific cells.84 It is, therefore, speculated that MVs could be delivered to the intended bacteria in the complex polymicrobial community. As with other secretory machineries, the formation and secretion of MVs is tightly regulated by QS mechanisms.119 Reciprocally, MVs mediate QS regulation through the delivery of structurally diverse QS signaling molecules, including cyclic PQS and acylated lactone signals,120 which tempted researchers to speculate that MVs could facilitate QS in a dynamic environment like the gut  and nutrients cross-feeding among close relatives. MVs are also implicated in determining the community composition by disseminating toxins to kill competitors, thereby shaping the microbial community structure. Thus, gut microbiota-derived MVs are involved in maintaining the proper health of the host.124 The sequencing data of the human microbiome has revealed that 30% of the small proteins appear to be associated with cell–cell communication, in which 9% of the gut metagenome is found to be transmembrane proteins or secretory proteins.125 This finding has confirmed that gut microbiota has a sophisticated system for inter-bacterial communication. The microbiota must communicate with the co-operative phenotypes for producing QS traits (public goods), whereas solitary cells are unable to produce enough public goods. Thus, the presence of co-operative phenotypes at high cell density is the prerequisite for the higher production of public goods. For example, probiotic Lactobacillus plantarum exerts higher production of QS-mediated bacteriocin only at high cell density, which could effectively eliminate the competitors from the vicinity108  is a normal resident of cattle rumen. LuxR homolog SdiA is essential for EHEC for successful colonization in the gut, where SdiA senses AHLs produced by other bacteria. Thereby, EHEC regulate their own gene expression to adapt to the gut environment.135 Likewise, commensal Enterobacter cloacae utilize sdiA for sensing the AHLs of other bacteria.136E. coli senses interspecies signal indole through SdiA receptor, which results in the decrease of biofilm formation.137Gram-positive and gram-negative bacteria utilize AHL type of signaling molecules and peptides-type of signals, respectively.luxR sequence suggests the possible occurrence of HGT.133 The presence of LuxR homologs in non-proteobacteria could be the result of HGT to eavesdrop signals from other bacteria. The acquisition of LuxR through HGT could be considered an evolutionary strategy of the bacteria to adapt to the fluctuating environment, where the receptor recognizes the prevailing QS signals, regardless of their origin. Due to the presence of orphan LuxRs, bacteria can interact with other bacteria in an intra- and inter-species manner. Thus, LuxR solos mediate crosstalk between genetically unrelated bacteria thereby broadens the communication network for long-term persistence. Thus, HGT and QS could determine the functions of the bacterial community. Though multiple LuxR homologs are found in the genome of bacteria, the functional role of LuxR homolog in determining the structure of the gut microbial community is largely unveiled.The existence of transposases-encoding gene adjacent to the luxS in their genome,106 which also implies that the composition of the microbiota could be controlled by QS. Interference of the QS system of the pathogen through analogues of QS signals is considered an elegant approach to curb the process of pathogenesis.138 Native gut microbiota Blautia obeum effectively inhibits the colonization of V. cholerae through the production of AI-2 synthase (luxS),126 whereas V. cholerae successfully establishes colonization in the absence of B. obeum  that has been widely reported in different species of bacteria. It has been found that more than 80% of the Firmicutes have AI-2 encoding gene B. obeum . It has  pathway . Since A pathway . AI-2 ha139 Though the human gut consists of diverse bacteria capable of producing acyl-homoserine lactone (AHL), classical AHLs have not been detected in the gut environment.140 This might be due to the detection limitation of the sensor organism and lack of advanced technology for identifying AHLs from gut samples. Gut microbiota might harbor novel types of QS signals and receptor systems for interbacterial communication. In support of this, gut microbiota produces a novel type of AHL 3-oxo-C12:2, which is predominantly found in healthy individuals.134 The guts of patients with inflammatory bowel disease (IBD) have restricted biodiversity and reduction of Firmicutes diversity. During dysbiosis, such novel signal is reduced in the patient (0.25 ± 0.15 nmol/g of feces), whereas healthy individuals harbor abundant AHL (2.62 ± 0.80 nmol/g of feces). Despite Firmicutes  is positively correlated with an increased amount of 3-oxo-C12:2, classical LuxI/LuxR homologues are not detected in Firmicutes, suggesting that Firmicutes phylum harbor undiscovered novel QS regulatory genes. Applying appropriate methodology for the extraction of AHL from feces could provide an opportunity to detect all ranges of AHLs present in the gut ecosystem. The administration of AHLs in a murine model will allow us to identify the role of these AHLs in restructuring the gut microbial communities.134 Hence, the restoration of the bacterial community structure is possible with QS mechanisms. Exploring the correlation between bacterial diversity and QS signal repertoires could aid us to develop novel therapeutics for restoring bacterial diversity during dysbiosis.It was found that LuxS of EHEC is also involved in the production of previously uncharacterized autoinducer-3 QS signal.Bacillus sp. capable of producing fengycin, a type of lipopeptide, restricts Staphylococcus aureus colonization through QS inhibition.127 Fengycin reduces Agr-mediated QS signaling in S. aureus. Perhaps, fengycin acts as an analog of the autoinducer peptide, which competitively binds with the respective receptor for inhibiting the QS mechanism of S. aureus  modulate the gene expression of E. coli and Fusobacterium nucleatum.151 Hence, host miRNAs could manipulate the gut microbiome for the benefit of the host’s health.Apart from bacterial metabolites, the host has a multitude of molecules that directly influence the gut microbiota. Host cells produce several chemically diverse molecules, including serotonin, nitric oxide, autoinducer-2 mimic, epinephrine, ethanolamine and dynorphin, which could interfere with QS mechanisms of host-associated bacteria.152 Intrinsic interactions between bacteria precisely determine the composition of the community and increase the fitness of the host.153 Human gut microbiome is strongly conserved across hosts, but the taxonomic composition is diversified in each individual,154 thus confirming that the community function is not associated with specific microbial diversity. Though bacteria within the gut microbial community is organized depending on functional genes, factors involving in the selection and assembly of bacteria are not yet understood. We propose that molecular trafficking between bacteria might also play a pivotal role in the assembly of functional diversity. If we ask why spatial stratification and community assembly are essential for the function of the community, it might be due to the following reasons: i) to secure public goods from the surrounding exploiting cheater cells; ii) proximate localization of kin cells allows QS signals to bind with cognate receptors, eventually resulting in higher production of QS traits; iii) heterogeneous population of kin cells help each other for nutrient acquisition and iv) exporting essential commodities to sibling cells.There is a strong correlation between the composition of gut microbiota and human health. Perturbation in the composition of the gut microbial community is always associated with several diseases, including obesity, cardiovascular disease, type 2 diabetes, and irritable bowel syndrome.155 While CDI and T6SS mediate interactions between gram-negative bacteria, T7SS contributes to the interaction between gram-positive bacteria. Besides, MVs, nanotubes and QS mechanisms mediate communication in both gram-positive and gram-negative bacteria. Now, the question arises as to how the EI reservoir is prevalent in both gram-positive and gram-negative bacteria. HGT plays an indispensable role for the existence of the EI resource in both bacterial systems. It is highly possible that nanotubes function as a bridge between genetically unrelated bacteria for sharing the molecular pool. Since T6SS and nanotubes mediate HGT, intrinsic crosstalk is likely between such machineries. Another example of the cross talk is that likely to occur between nanotubes and T6SS in P. mirabilis. For instance, discrimination of non-self cells occurs through the exchange of IdsD, where the transport is mediated by T6SS. It is frequently found that the inner diameter of the T6SS Hcp tube is ~40 Å.156–158 Though the export of IdsD is dependent on T6SS, how such bigger sized protein pass through the Hcp tube of T6SS is yet to be deciphered.71 Since the width of the nanotubes is shown to be more than 40 nm,57P. mirabilis might be utilizing nanotubes for the exchange of IdsD, as a result of cross-talk between T6SS machinery and nanotubes. Hence, gaining knowledge of these critical bacterial interactions is the need of the hour.Microbiota gain survival fitness by acquiring different types of molecules from the neighboring cells through conduits or diffusion. The cross-talk between these machineries might be required to maintain microbial balance. Machineries such as CDI, T6SS, T7SS and nanotubes are likely to have similar attributes in terms of utilizing the common pool of EI pairs. Though each secretory system has a unique architecture, all systems deliver effectors that usually target conserved essential features of the competitors.Bacteroidales, T7SS contributes in the structuring of Firmicutes diversity. Besides, CDI is likely to determine proteobacterial diversity. Despite CDI, T7SS, nanotubes and MVs have been shown to govern the local microbial community, the impact of these systems on the entire gut microbial community is not yet revealed. Therefore, an in-depth study on these secretory systems will usher in a new era in the field of human gut microbiota and provide ample opportunities to develop therapeutics in a new dimension. By understanding their survival strategies, potential pathogens could be selectively eliminated from the community. Since pathogens tend to survive in the host by producing virulence factors through the QS mechanism, it is, therefore, possible that targeted therapy could be achieved by targeting the communication system of the pathogens, which is key to their survival. In P. aeruginosa, both CDI and T6SS are regulated by RsmA regulator.159 Considering such a common regulator of secretory systems of the pathogens could be a potential therapeutic intervention to restrict pathogenesis. Many seminal research findings have highlighted that these machineries serve as a double-edged sword, having the potential to exhibit both co-operative and competitive behaviors. However, many key questions, like the following, are yet to be clearly answered. How does the CdiA-toxin domain get into the recipient cell? Does CDI-mediated aggregation induce QS of the microbiota in a highly dynamic environment, like gut? Does T7SS confer cooperative behaviors on microbiota? Does T7SS form a conduit for the translocation of toxins into the recipient cells? What are the receptor proteins required for establishing interaction through the T6SS?Based on the existing scientific evidence, both microbiota and pathogens possess multiple machineries, namely, CDI, T6SS, T7SS, nanotubes, MVs and QS, which function either in a contact-dependent or independent manner. In line with recent findings, it could be concluded that T6SS and QS mechanisms manipulate the human gut microbial composition. It was demonstrated that QS signals such as AI-2 and 3-oxo-C12:2 precisely restructure the composition of the Firmicutes community. While T6SS governs the composition of 160 Fecal microbial transplantation (FMT) is being used to restore microbial balance in the gut of IBD patients. FMT provides beneficial effects to the patients; however, imparting excess nutrients and amino acids could possibly favor the expansion of pathogens.161 Therefore, the donor’s microbial population has to be metabolically analyzed before FMT. Though antibiotic treatment appears to be effective for microbial restoration in IBD patients, the emergence of antimicrobial resistance (AMR) in pathogens poses a grave threat to global health. World Health Organization urges that AMR is an emerging threat to global health, food security and development. Dissemination of AMR genes carrying plasmids between pathogens162 allows them to thrive in a complex environment, like gut. Traditionally, type IV secretion system (T4SS) has been viewed as a machinery that can facilitate the spread of AMR genes. The presence of T4SS machinery encoding genes on conjugative plasmid163 suggests that pathogens recruit T4SS for conjugation. Recently, it was found that bacteria utilize machineries such as nanotubes, T6SS and MVs for spreading adaptive resistance genes,164 thus contributing to the emergence of multidrug-resistance (MDR) super-bugs. We believe that the manipulation of bacterial secretory machineries will allow us to fight against AMR. Identifying the proteins and metabolites responsible for inter-bacterial interactions paves the way to identify target pathogens and to develop novel methods to prevent the spread of AMR genes through HGT. Hence, venturing on novel therapies will enable us to restrict the overexploitation of antibiotics. To summarize, inter-bacterial interactions in the polymicrobial community largely influence the health status of the host. Hence, exploring the correlation between inter-bacterial molecular trafficking mechanisms and gut microbiome’s structure and function will lead to a holistic understanding as well as future medical solutions in this domain.Since gut microbes produce several previously undescribed molecules, utilization of such potential gut microbes for the restoration of microbial balance is a nuanced approach.Click here for additional data file.