Typhoid fever remains a major public health problem globally. While new Vi conjugate vaccines hold promise for averting disease, the optimal programmatic delivery remains unclear. We aimed to identify the strategies and associated epidemiologic conditions under which Vi conjugate vaccines would be cost-effective.We developed a dynamic, age-structured transmission and cost-effectiveness model that simulated multiple vaccination strategies with a typhoid Vi conjugate vaccine from a societal perspective. We simulated 10-year vaccination programs with (1) routine immunization of infants (aged <1 year) through the Expanded Program on Immunization (EPI) and (2) routine immunization of infants through the EPI plus a 1-time catch-up campaign in school-aged children (aged 5–14 years). In the base case analysis, we assumed a 0.5% case-fatality rate for all cases of clinically symptomatic typhoid fever and defined strategies as highly cost-effective by using the definition of a low-income country (defined as a country with a gross domestic product of $1045 per capita). We defined incidence as the true number of clinically symptomatic people in the population per year.Vi conjugate typhoid vaccines were highly cost-effective when administered by routine immunization activities through the EPI in settings with an annual incidence of >50 cases/100000  and when administered through the EPI plus a catch-up campaign in settings with an annual incidence of >130 cases/100000 . The incidence threshold was sensitive to the typhoid-related case-fatality rate, carrier contribution to transmission, vaccine characteristics, and country-specific economic threshold for cost-effectiveness.Typhoid Vi conjugate vaccines would be highly cost-effective in low-income countries in settings of moderate typhoid incidence . These results were sensitive to case-fatality rates, underscoring the need to consider factors contributing to typhoid mortality  in the global vaccination strategy. Salmonella enterica serovar Typhi (S. Typhi), transmitted through ingestion of food or drink contaminated by human feces containing these bacteria.Typhoid fever, also known as enteric fever, is an acute systemic infectious disease that has an estimated global incidence of 12–22 million cases annually, resulting in 130000–220000 deaths . The majHistorically, no global public health strategy has been implemented or scaled for control of typhoid fever in resource-poor settings. In settings of endemicity, typhoid fever is managed clinically through both formal and informal healthcare systems instead of through regular preventive vaccination or other vertical health programming. While fluoroquinolones  have been the first-line antibiotic therapy, the rise in antimicrobial resistance common in South Asia has made alternative antibiotics necessary , 7. As aWhile typhoid vaccines have been available for decades, important limitations have precluded their widespread adoption in settings of endemicity. The multidose live oral Ty21a and single-dose parenteral Vi polysaccharide have limited efficacy and provide a relatively short duration of protection , 11. FurNew Vi conjugate vaccines are promising and have many potential advantages over prior vaccines, including higher efficacy and longer duration of protection , 16. UnlWhile Vi conjugate vaccines offer a promising intervention to avert disease throughout low- and middle-income typhoid-endemic countries, the conditions and programmatic delivery strategies under which they should be delivered remain unclear. To address this need, we modeled typhoid transmission dynamics, vaccine costs, and outcomes across a range of settings to identify strategies and epidemiologic conditions under which Vi conjugate vaccines would be most cost-effective in settings of endemicity.S. Typhi informed by previous models , 2928, 2Intervention costs (in 2016 dollars) included direct and indirect sources and were computed from both the perspective of society and a national vaccination program. Direct programmatic costs included the cost of vaccine production, transportation, storage, wastage (10%), vaccination supplies, delivery, training, and worker salaries . We assuThe cost-effectiveness of each vaccination intervention was quantified by the incremental cost-effectiveness ratio . The ICER compares 2 strategies and is computed as the difference in cost between strategies divided by the difference in averted DALYs. We assumed a base case intervention of no vaccination since this is the current widespread practice. We defined strategies as highly cost-effective if the ICER was less than the per capita income for countries designated by the World Bank as low-income countries  , 37. HowWe tested the robustness of our primary study findings with scenario, 1-way sensitivity, and uncertainty analyses that varied key model parameters across a range of plausible values. The 1-way sensitivity analysis tested the effect of one model parameter on the ICER of each strategy, including different vaccine costs , case-fatality rates from typhoid fever, and a full range of willingness-to-pay thresholds. We also provided a conservative scenario analysis with lower vaccine efficacy (80%), shorter duration of immunity (10 years), and higher carrier contribution. We tested the inclusion of expanded societal benefits for vaccine-averted typhoid fever , 40. TheWe used an uncertainty analysis to generate a 95% uncertainty interval (UI) around the ICER of each strategy, by simultaneously varying multiple key model parameters for typhoid transmission, vaccination, and cost-effectiveness (Appendix). The 95% UI included sampling of different transmission trajectories from the posterior distribution of the MCMC parameter fits. The model code is available online .The fitted model reproduced the age distribution of incidence of typhoid fever for each example scenario of low endemicity (10 cases/100000), moderate endemicity (50 cases/100000), and high endemicity (200 cases/100000); the proportion of cases in younger age groups increased in higher-incidence settings . We measUnder the base case scenario, routine immunization through the EPI resulted in a gradual reduction in population incidence, ultimately reaching a plateau in incidence reduction . The comWhen assuming a 0.5% case-fatality rate from clinically symptomatic typhoid fever, the Vi conjugate typhoid vaccine was highly cost-effective by routine immunization through EPI in settings with an annual incidence of >50  cases/100000 and for the combined strategy (routine immunization through the EPI plus a one-time catch-up campaign in children aged 5–14 years) in settings with an annual incidence of >130  cases/100000. In the conservative scenario, the Vi conjugate typhoid vaccine was highly cost-effective by routine immunization through EPI in settings with an annual incidence of >70 cases/100000 and for the combined strategy (routine immunization through the EPI plus a one-time catch-up campaign in children aged 5–14 years) in settings with an annual incidence of >180 cases/100000. When assuming direct programmatic costs alone, we found that the EPI strategy was highly cost-effective in settings with an incidence of >70  cases/100000 and for the combined strategy (routine immunization through the EPI plus a one-time catch-up campaign) in settings with an incidence of >200  cases/100000.We tested an additional set of strategies, including expanded catch-up campaigns, in the model. In this scenario analysis, we found that routine immunization through the EPI plus a one-time catch-up campaign was highly cost-effective in children aged 1–14 years with an annual incidence of >110 cases/100000 and in people aged 1–30 years with an annual incidence of >350 cases/100000. Notably, routine immunization through the EPI plus a one-time catch-up campaign in school-aged children (age 5–14 years) was dominated in this scenario, while routine immunization through the EPI alone remained unchanged.The incidence threshold was highly sensitive to the typhoid-related case-fatality rate, which contributed the majority of the disease burden. As the case-fatality increased, both vaccination strategies became more cost-effective over time . CorrespIn 1-way sensitivity analyses, the cost-effective incidence threshold, above which each vaccination strategy was highly cost-effective, were most sensitive to typhoid case-fatality rate, cost of vaccine and delivery, vaccine efficacy and duration of protection, carrier contribution, and willingness-to-pay threshold . In simuS. Typhi or lower access to healthcare, vaccination would be substantially more cost-effective, meaning that lower-incidence settings may consider vaccination.In this study, we used a dynamic transmission and cost-effectiveness model to identify incidence thresholds above which to use targeted strategies with new Vi conjugate vaccines against typhoid fever. While global estimates for typhoid fever incidence are uncertain, many studies suggest that typhoid fever in South Asia and Africa is highly endemic (>100 cases/100000), although there is considerable within-country variation , 4. WhenFor routine immunization through the EPI, we found substantial reductions in the incidence in many settings and that this strategy would be highly cost-effective in settings of moderate endemicity. For routine immunization through the EPI plus a catch-up campaign in school-aged children, we found larger reductions in the number of cases and the disease burden, compared with EPI alone. We found that the incidence threshold for cost-effective vaccination through the EPI plus a school catch-up campaign was higher than the incidence threshold for immunization through the EPI alone, although there was greater uncertainty around this estimate. This means that higher-incidence settings should consider adding a one-time catch-up campaign to their typhoid control strategy. Notably, when considering expanded catch-up campaigns, we found that routine immunization through the EPI with an expanded catch-up program  dominated routine immunization through the EPI with a school-based catch-up program (5–14 years) and would be preferred in higher-incidence settings. Additionally, the average age at infection increased following a vaccination campaign. Given an increased risk for long-term carriage at older ages, mass vaccination could possibly contribute to an increased risk of long-term carriage that continues to drive transmission.The effectiveness results from our study broadly aligned with data from a prior modeling study that simulated the effect of routine immunization in Vellore, India . In partA key finding was that the case-fatality rate of typhoid fever was a critical and uncertain parameter that drives the incidence thresholds of various vaccination strategies. Most composite estimates have been in the range of 0.5% to 1%, but this could be biased upward, because most studies are hospital based, or downward, because of unascertained deaths at home, and it also is likely to vary by country and setting. Furthermore, as antimicrobial resistance increases and available treatments become ineffective, there is concern about an associated increase in the case-fatality rate over time. The typhoid case-fatality rate is also driven by setting-specific factors, including access to and quality of healthcare and, possibly, interaction with other infectious diseases  . To addrWe optimized vaccine strategies to the economic willingness-to-pay threshold of a low-income country, although this may be too low or high for some settings. In South Asia, many countries have transitioned into middle-income status and may refine their vaccination decision within this economic context. To support a programmatic decision, we performed a scenario analysis and provided the cost-effectiveness acceptability curves, which provide model results across a wide range of willingness-to-pay thresholds  for more-context-specific decision-making. In higher-income countries with correspondingly higher economic thresholds, more-aggressive strategies were more often highly cost-effective than in low-income countries.In the sensitivity and uncertainty analyses, we varied multiple key model parameters related to the biology of typhoid transmission, vaccination, and cost-effectiveness. We identified some sensitive model inputs, many of which are likely to be setting specific and can be refined within a particular context with data on case-fatality rate, economic status , vaccine cost, and other epidemiologic factors. Importantly, we generated 95% UIs based on a range and distribution of possible model inputs, and this interval should be conceptualized as a broad range of possible outcomes for a given setting, given assumptions on tested ranges and distribution of parameters. For a 2-dose vaccination schedule, which would double the vaccine cost, we found the incidence threshold to intuitively become higher because of an increased cost of the program. However, broadly these analyses found that vaccination can still be highly cost-effective in settings of moderate endemicity when taking into account uncertainty and variation in the value of certain key parameters.The goal of this study was to provide incidence thresholds to guide vaccine implementation, but the typhoid incidence itself is often unknown and varies within a country and across time; therefore, surveillance and, perhaps, new strategies are first required to ascertain the typhoid incidence for a given setting before these study results can be used. Furthermore, while we found the typhoid Vi conjugate vaccine to be cost-effective in many settings of endemicity, the total budgetary requirements may still be too large. The aim is to support country prioritization among many competing health needs. Given the within-country heterogeneity in typhoid fever burden , countriS. Typhi transmission, particularly the relative contribution of short versus long-cycle transmission and long-term carriers, and in vaccine efficacy and duration of protection. To address this, we applied Bayesian model calibration to age-structured data to infer unknown parameters  and generated a set of model inputs to create a 95% UI for all transmission projections; additionally, we performed sensitivity analyses to ensure our primary results were broadly robust. We assumed homogenous mixing within each subpopulation, no migration, exponential waning of immunity, and 1 environmental water reservoir following common modeling practice for waterborne infections, although these factors are more complex in reality [S. Typhi, except through the indirect benefits of vaccination. Typhoid vaccines have the potential to provide value outside of health outcomes alone  [S. Paratyphi, which contributes to global enteric fever cases and will require additional intervention to address [The findings of this study should be understood within the limitations of the model assumptions, uncertainty in the biology of typhoid transmission, and country-specific heterogeneities. To construct broadly generalizable incidence thresholds, we used an approximate epidemiologic relationship between incidence and the age distribution of cases, and we recognize that this may oversimplify complex setting-specific heterogeneities and uncertainties. Importantly, there is substantial uncertainty in the biology of  reality , 25, 26.tourism) , 40. Whitourism) . We did  address .The new Vi conjugate typhoid vaccines pose great potential to address typhoid fever in the low- and middle-income countries and have renewed interest in developing a global public health strategy using vaccination. This study supports the introduction of the vaccine as a public health intervention in some settings of endemicity to address the global disease burden of typhoid fever.The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at Supplementary MaterialClick here for additional data file.


The correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public sequence databases hampered so far a finer grain discrimination, including also intra-organelle compartments.We describe DeepMito, a novel method for predicting protein sub-mitochondrial cellular localization. Taking advantage of powerful deep-learning approaches, such as convolutional neural networks, our method is able to achieve very high prediction performances when discriminating among four different mitochondrial compartments . The method is trained and tested in cross-validation on a newly generated, high-quality dataset comprising 424 mitochondrial proteins with experimental evidence for sub-organelle localizations. We benchmark DeepMito towards the only one recent approach developed for the same task. Results indicate that DeepMito performances are superior. Finally, genomic-scale prediction on a highly-curated dataset of human mitochondrial proteins further confirms the effectiveness of our approach and suggests that DeepMito is a good candidate for genome-scale annotation of mitochondrial protein subcellular localization.http://busca.biocomp.unibo.it/deepmito. A standalone version of DeepMito is available on DockerHub at https://hub.docker.com/r/bolognabiocomp/deepmito. DeepMito source code is available on GitHub at https://github.com/BolognaBiocomp/deepmitoThe DeepMito web server as well as all datasets used in this study are available at Bioinformatics online. Mitochondria are double-membrane bound organelles present in all Eukaryotic cells and performing very important biological functions, which include energy production, calcium signaling, regulation of cell metabolism and apoptosis  architecture to extract relevant patterns from primary features. DeepMito discriminates four different sub-mitochondrial compartments and our implementation outperforms the only method previously described . We first selected all non-fragment protein sequences with evidence at protein level and endowed with experimentally determined subcellular localization (evidence code ECO: 0000269) in one of the four sub-mitochondrial compartments: outer membrane (SL-0172), intermembrane space (SL-0169), inner membrane (SL-0168) and matrix (SL-0170). For sake of selecting the best possible set of annotations, proteins that are also localized in compartments other than mitochondria were excluded.In order to obtain a non-redundant set of protein sequences, we performed clustering using the CD-HIT program  with gloAfter this filtering procedure, we ended-up with 424 mitochondrial proteins sharing at most 40% sequence identity computed at a global level. The dataset comprises 193 proteins from Metazoa, 166 from fungi, 60 from plants, 4 from Euglenozoa and 1 from Amoebozoa. Overall, the dataset comprises 74 outer membrane, 190 inner membrane, 25 intermembrane and 135 matrix proteins .http://busca.biocomp.unibo.it/deepmito/datasets.On our dataset (SM424-18) we adopted a 10-fold cross-validation. In order to avoid any possible bias between training and testing, we applied the following clustering procedure to generate cross-validation sets. First, the 424 protein sequences were cross-compared running all-against-all pairwise blastp with e-value threshold set to 0.001. From blast output, we built a similarity graph where nodes are protein sequences and edges among pairs of nodes were added if at least one blast hit with more than 30% sequence identity was found (no coverage threshold was set). On this graph, single-linkage clustering was performed computing connected components. Finally, all proteins falling in the same cluster were assigned to the same cross-validation set. In this way, we eliminated any possible sequence identity bias among training and testing, confining any residual sequence redundancy  in the same cross-validation set. SM424-18 is available for download at http://proteininformatics.org/mkumar/submitopred/download.html) was derived from UniprotKB/SwissProt release 2014_10, selecting protein sequences with the following criteria:Full-length proteins (no fragments) with experimental existence evidence.Protein length > 50 residues.Experimental sub-mitochondrial subcellular localization, retaining only proteins localized into a single compartment.Dataset internal redundancy reduced at 40% sequence identity using CD-HIT.SubMitoPred is a dataset previously introduced to train and test the most recent approach for sub-mitochondrial localization prediction, addressing a four-compartment discrimination . The Cell Atlas database assigns a label to each annotation, representing its quality. Four different labels are defined : Enhanced, Supported, Approved and Uncertain. For the 1074 mitochondrial proteins considered in this study, Enhanced and Supported annotations cover about 50% of the dataset (165 + 347 = 512), 506 annotations are Approved while only a small fraction are Uncertain (56 annotations). In order to obtain a sufficient number of sequences, here, we decided to retain all annotations available for mitochondrial proteins.ENSEMBL gene identifiers for the 1074 mitochondrial proteins were mapped to UniprotKB entries: after this step we were able to map 1050 ENSEMBL identifiers to UniprotKB entries. Twenty-four ENSEMBL genes were excluded because of non-clear or multiple mapping to UniprotKB.https://www.ebi.ac.uk/QuickGO/). We focused only on annotations of the GO Cellular Component, endowed with experimental evidence (ECO: 0000269) and derived from any source databank. As a result, 179 out of 1050 gene products were annotated with GO terms that are equal to or descendants of any of the four mitochondrial compartments considered in this study: 19 proteins in mitochondrial outer membrane (GO: 0005741), 67 in mitochondrial inner membrane (GO: 0005743), 12 in mitochondrial intermembrane space (GO: 0005758) and 81 in mitochondrial matrix (GO: 0005759). Proteins that were annotated with GO terms related to multiple mitochondrial compartments were filtered-out form this set.On this set, we extracted available Gene Ontology term information using the QuickGO website (We refer to the full Cell Atlas dataset comprising 1050 protein sequences as Mito-CA-Full and to the subset of 179 annotated as Mito-CA-Annotated.L is the length of the sequence.Residue one-hot encoding (SEQ), where each residue in a protein sequence is encoded using a 20-dimensional vector with all zero components except for the one representing the residue. Overall, each protein sequence is represented by a matrix with L is the length of the sequence.Residue physical–chemical properties (PROP), where each residue in a protein sequence is encoded using the 10 different numerical values introduced by L is the length of the sequence. Internally, the program computes the matrix by considering the MSA obtained by (i) stacking all pairwise alignments between query and similar sequences found after each iteration and (ii) removing MSA columns corresponding to gaps in the query sequence. In this way, the PSSMs have always a number of rows that coincides with the length of the query sequence. Raw PSSM values extracted from the PSI-BLAST checkpoint file (generated by the program after each iteration) were mapped in the range [0-1] using a sigmoid function, defined as:Evolutionary information, in the form of Position Specific Scoring Matrices (PSSM) as computed, for each sequence in the datasets, by running the PSI-BLAST  to the prediction of protein sub-mitochondrial localization, when provided in input to the DeepMito convolutional network. In particular, the following features were considered:Feature descriptors are combined protein-wise with simple concatenation along the sequence axis. For instance, combining PSSM and PROP matrices of dimensions CNNs have their main application domain in the Computer Vision area  or at a global level (i.e. sequence classification).Each residue in a sequence is represented with low-level features, e.g. residue properties, one-hot encoding or sequence profiles. The number of features encoding for a given residues is referred to as channels in the CNN context.A typical CNN is a feed-forward architecture comprising two different types of layers: convolutional and pooling layers. Formers are used to extract salient features from the input by means of filters or motif detectors, whose parameters are learnt during training and are used to scan the input. Pooling layers are instead parameter-free, and they are used for downsampling, namely to reduce the input dimensionality by selecting/aggregating the most relevant features extracted by convolutional layers according to some predefined function .More formally, let be A convolutional layer is a collection Using a sliding-window approach, the Sequence termini are handled by adding explicit zero-padding at the beginning and end of the input sequence Overall, a convolutional layer endowed with Pooling layers following convolutions reduce the dimensionality of the matrix local pooling with pool size In global pooling, the pool size In Common pooling functions are average, sum, maximum and minimum.Overall, one or more successive applications of convolution-pooling layers transform the input Finally, the hidden layer is mapped to the output layer comprising In summary, a CNN architecture for sequence classification can be divided into two parts: the first part, where convolution-pooling layers are applied, is devised to feature extraction and selection; the second part, consisting in the final fully connected network, performs the actual classification of the sequence into Prediction of protein sub-mitochondrial localization can be naturally defined as a multi-class sequence classification problem where each input protein sequence is classified as belonging to one of The input protein sequence where each residue is encoded as a The two pooling layers are then concatenated into a single vector and provided to the final classification network with The DeepMito CNN was trained using a Stochastic Gradient Descent optimizer by minimizing the cumulative binary cross-entropy loss function. More formally, consider a training set The cumulative binary cross-entropy loss function is defined as:A CNN architecture like the one depicted in In order to find optimal hyperparameters, we defined a set of possible values for each hyperparameter (see https://keras.io) deep-learning library with Tensorflow v. 1.11 (https://www.tensorflow.org) as backend.DeepMito was implemented in Python 2.7 and using the Keras v. 2.2.4 (Performances of our method were scored by computing the multi-class confusion matrix Single-class predictions were scored using the Matthews’ Correlation Coefficient (MCC) for each class Moreover, we adopted the GCC, described in Then we define the following matrix The GCC is then defined as:The GCC value ranges from −1 to 1 and a GCC equal to 0 corresponds to predictions no better than random.In order to build an optimal predictor for protein sub-mitochondrial compartment prediction, we first assessed the predictive power of each type of feature. In this study, as already detailed in Section 2.2, three basic type of features were considered: protein primary sequence (encoded using the standard residue one-hot encoding), protein physical–chemical attributes and evolutionary information in the form of PSSMs.These three basic feature types were then combined and provided in the input to the DeepMito CNN architecture. In doing this, we considered protein primary sequence and PSSMs as alternative choices, scoring them individually or combined with protein physical–chemical attributes. For each evaluated feature set, a different CNN has been trained in cross-validation on the SM424-18 dataset, optimizing network architecture as explained in Section 2.5. Performance scores are reported in Our results indicate that both primary sequence and protein attributes, when taken alone, are not sufficiently informative and both lead to limited prediction performances, with protein attributes slightly outperforming primary sequence .As expected, evolutionary information plays a major role in improving prediction performance. In fact, when considered alone, the PSSM input significantly improves prediction performance, leading to a generalized improvement observable in all scoring indices and, in particular in GCC, raising it up to 0.50. When PSSM is combined with protein attributes, performances further improve reaching 0.54 of GCC. We then adopted this feature set for DeepMito and for all subsequence analyses.The optimal CNN architecture comprises 256 convolutional motif detectors of width 19 and 256 hidden units in the fully connected hidden layer (see Section 2.4 for details on the DeepMito CNN architecture).Having selected the best CNN architecture and features set, we analyzed in detail DeepMito predictions on the SM424-18 dataset. This allowed to highlight strengths and limitations of our method.Two aspects were taken into consideration: (i) how prediction performance varies across different taxonomic kingdoms and (ii) how DeepMito performs on different types of membrane proteins, namely single-pass (SP), multi-pass (MP) and peripheral membrane (PM) proteins.Concerning the first issue, performance scores obtained on the different subsets of animals, plants and fungi proteins are reported in Comparing results in In P), the number of outer and inner membrane proteins , the fraction of proteins correctly predicted in either inner or outer membrane . In contrast, only 22 out of 61 (36%), PM proteins are correctly localized into membranes. This suggests that PM proteins are endowed with features that are more similar to proteins of matrix and/or intermembrane space. Thirty-nine PM proteins are incorrectly classified into globular compartments: out of these, 32 are annotated in UniprotKB as residing in the matrix side of the membrane. Interestingly DeepMito correctly assigns 28 out of 32 PM proteins to the matrix compartment.Performing a comparison among different approaches for sub-mitochondrial localization prediction is a challenging task, mainly because different methods are trained/tested using different datasets and many of the methods presented so far are no more available via respective web servers. For these reasons, we decided to carry out a direct comparison between DeepMito and the most recent approach described for the same four-compartment discriminative task . In particular, DeepMito was trained using the entire SM424-18 dataset and predictions generated for all the 1050 proteins included in the Mito-CA-Full dataset.First, we assessed DeepMito predictions with respect to available experimental annotations: for this, we extracted predictions on the Mito-CA-Annotated dataset comprising 179 sequences endowed with experimental GO terms relative to sub-mitochondrial compartments. http://busca.biocomp.unibo.it/deepmito/hpa.In http://busca.biocomp.unibo.it/deepmito. The server supports the analysis of up to 200 sequences per submission: for each input protein, the server provides the predicted sub-mitochondrial compartment (as Gene Ontology Cellular Component term) along with a score associated with the prediction. Even though the server is intended to be used with proteins already known to be mitochondrial and for which the user is interested to know the precise localization inside the organelle, there is the possibility that users provide in input proteins that are not mitochondrial. In order to cope with this issue, the server performs a scanning of input proteins using two state-of-the-art predictors of mitochondrial localization: TPpred3 .The adoption of more complex architectures like recurrent layers may improve prediction performance in this task. However, in our experiments (data not shown), recurrent approaches lead to poor performance. This fact is maybe due to the scarcity of data which hampers proper training of complex architectures.DeepMito well performs also on proteome-scale analysis, carried out on high-quality human proteins from the Cell Atlas database  .Conflict of Interest: none declared.btz512_Supplementary_MaterialClick here for additional data file.


Lactobacillus are commonly employed for this task, of which a subset are known to possess surface-layers (S-layers). S-layers are two-dimensional crystalline arrays of repeating proteinaceous subunits that form the outermost coating of many prokaryotic cell envelopes. Their periodicity and abundance have made them a target for numerous biotechnological applications. In the following review, we examine the multi-faceted S-layer protein (Slp), and its use in both heterologous protein expression systems and mucosal vaccine delivery frameworks, through its diverse genetic components: the strong native promoter, capable of synthesizing as many as 500 Slp subunits per second; the signal peptide that stimulates robust secretion of recombinant proteins; and the structural domains, which can be harnessed for both cell surface display of foreign peptides or adhesion enhancement of a host bacterium. Although numerous studies have established vaccine platforms based on one or more components of the Lactobacillus S-layer, this area of research still remains largely in its infancy, thus this review is meant to not only highlight past works, but also advocate for the future usage of Slps in biotherapeutic research.Lactic acid bacteria (LAB) are frequently harnessed for the delivery of biomolecules to mucosal tissues. Several species of  Lactic acid bacteria (LAB) are Gram-positive, anaerobic or microaerophilic, non-sporulating microorganisms that inhabit diverse environments including milk and plant surfaces, as well as the mouth, gastrointestinal tract, and vaginal tract of humans and animals . TraditiLactococcus lactis and select species of the Lactobacillus genus  (The LABs most frequently chosen for vaccine delivery are us genus . However-layers) . S-layer-layers) . They ar-layers) . The S-l-layers) . Thus fa-layers) . Additio-layers) . Their h-layers) .Figure 1A). Despite the existence of several recombinant protein expression systems based on one or more components of the Lactobacillus S-layer, this area of research still remains largely underexploited. Thus, the purpose of this review is to not only shed light on past S-layer studies, but also to advocate for future utilization of Slps in mucosal vaccine and biotherapeutic delivery research.In the following review, we examine Slp applications in recombinant protein expression and biotherapeutic delivery via their distinct genetic building blocks: the strong native promoter, which can synthesize as many as 500 Slp subunits per second; the signal peptide, that can trigger robust secretion of target molecules; and the structural domains, which can be harnessed for both cell surface display of heterologous proteins or enhancement of host adhesion . Within the Lactobacillus genus, this research has predominately been limited to slp promoters of Lactobacillus acidophilus and Lactobacillus brevis.The bacterial S-layer array is composed of an estimated 5 × 10acterium . During acterium . In ordeLactobacillus S-layer promoters for driving heterologous protein expression in various LAB hosts. Lactobacilli regularly possess multiple slp genes within the same strain that are not all concurrently active  in shuttle vectors based on oriV1 and oriV2 replicons  reporter activity within L. plantarum was 30-fold higher compared to the Lactobacillus helveticus PepN native host and composed a staggering 28% of the total cellular protein during late exponential growth phase. In summary, Lactobacillus slp promoters are effective tools for driving recombinant protein expression, but optimization based on host and growth conditions is essential.Several studies have investigated the versatility of y active . Althougent SlpB . When thh phases . However reuteri . The L. eplicons . Similararacasei . The funer genes . The S-lin situ. Plasmid instability and antibiotic markers can complicate the use of these constructs when moving into human clinical trials or to market, thus chromosomal insertions have gained immense popularity. For L. acidophilus NCFM, a pORI-based upp counterselective gene replacement system (gusA) reporter downstream of the slpA gene  secretion system  over a low pH tolerating, vertebrate-adapted organism , may have also contributed , thus this peptide is frequently harnessed for biotechnological applications.All  pathway . By clonL. brevis ATCC 8287 slpA signal sequence, along with the promoter and transcription terminator, were used to drive expression and secretion of the Escherichia coli β-lactamase (bla) reporter gene in L. lactis, L. brevis, L. plantarum, L. gasseri, and L. casei, using a low-copy-number plasmid derived from pGK12 .The om pGK12 . In all th phase . Since tL. lactis mucosal vaccine against porcine post-weaning diarrhea and edema disease, signal peptides from the L. lactis major extracellular protein, Usp45, and L. brevis SlpA, were used to stimulate the secretion of an E. coli F18 fimbrial adhesion protein (FedF) fused to a proteinase PrtP cell wall anchor , fused to a dendritic cell (DC)-targeting peptide, was previously shown to induce protective immunity when delivered on a plasmid via L. acidophilus NCFM  enables direct interaction of target peptides with host mucosal tissues while simultaneously protecting them from degradation , and a highly conserved peptidoglycan anchor  in conjunction with the Slp promoter and secretion signal, were used for surface display of a GFP on several Lactobacillus chicken isolates  was attached to a GFP reporter, then produced and purified in E. coli. The SAC-GFP fusion protein was capable of binding lithium chloride-pretreated surfaces of wild-type L. acidophilus, L. helveticus, and L. crispatus . The presentation of an exogenous protein within the context of the Slp was first achieved in Caulobacter crescentus through the random introduction of a pilin peptide from Pseudomonas aeruginosa strain K. Eleven potential sites of successful insertion were identified, demonstrating for the first time the capacity of the Slp to act as a carrier for foreign epitopes . Both the native SLAP promoter and secretion signal can be harnessed for this process with the intent to maintain high expression, secretion, and surface localization coupled with the display and delivery of a significantly larger and therefore more potent epitope.The  complex , 2015. I complex . Recentltrometry . Althougtrometry . The surL. lactis and the pORI-based upp counterselective gene replacement system in L. acidophilus. There is also growing interest surrounding the use of CRISPR-Cas technology, though its application in bacterial genome editing remains relatively underrepresented  system in resented .L. lactis  as a counterselection marker to positively select for double crossover homologous recombination events. The method has been adapted for numerous Slp studies ranging from reporter integration , which does not possess an active CRISPR system  together with CRISPR-associated (Cas) proteins, form the prokaryotic adaptive immune system which provides DNA-encoded, RNA-guided, sequence-specific protection against viral invaders . The curL. casei  and L. l. lactis , frequenR system , but hasClostridium and archaea  advocates for its continued pursuit. The slp promoter and signal peptide are undoubtedly adept at driving robust expression and secretion of target proteins, while the structural domains have successfully displayed foreign epitopes and improved the adhesive capacity of host cells. More complex and novel display strategies, such as direct integration into the Slp or SLAP fusions, are innovative approaches for cell surface presentation that also exploit the inherent properties of S-layer-forming lactobacilli. In general, eliciting consistent immune responses via the mucosal route of administration is hindered by rapid elimination or inability to make contact with M cells and other mucosal tissues involved in antigen uptake and processing (The Slp is a multi-faceted engineering target with both biotherapeutic and biotechnological applications . Howeverelopment ; nonetheocessing . Thus thocessing .Lactobacillus Slp, however, there remains a disconnect between delivery and efficacy. Despite extensive reviews touting the effectiveness of the LAB-based vaccines in vivo (Numerous studies have established vaccine platforms based on one or more components of the  in vivo , few S-l in vivo , whereasCK and RB wrote and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Epithelial-mesenchymal transition (EMT) and cancer stem cell (CSCs) formation are two fundamental and well-studied processes contributing to cancer metastasis and tumor relapse. Cells can undergo a partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype or a complete EMT to attain a mesenchymal one. Similarly, cells can reversibly gain or lose 'stemness'. This plasticity in cell states is modulated by signaling pathways such as Notch. However, the interconnections among the cell states enabled by EMT, CSCs and Notch signaling remain elusive. Here, we devise a computational model to investigate the coupling among the core decision-making circuits for EMT, CSCs and Notch. Our model predicts that hybrid E/M cells are most likely to associate with stem-like traits and enhanced Notch-Jagged signaling – a pathway implicated in therapeutic resistance. Further, we show that the position of the 'stemness window' on the 'EMT axis' is varied by altering the coupling strength between EMT and CSC circuits, and/or modulating Notch signaling. Finally, we analyze the gene expression profile of CSCs from several cancer types and observe a heterogeneous distribution along the 'EMT axis', suggesting that different subsets of CSCs may exist with varying phenotypes along the epithelial-mesenchymal axis. We further investigate therapeutic perturbations such as treatment with metformin, a drug associated with decreased cancer incidence and increased lifespan of patients. Our mechanism-based model explains how metformin can both inhibit EMT and blunt the aggressive potential of CSCs simultaneously, by driving the cells out of a hybrid E/M stem-like state with enhanced Notch-Jagged signaling. Metastatic spread of cancer cells claims the highest number of fatalities, accounting for over 90% of cancer-related deaths . StudiesTo enter the bloodstream as clusters of CTCs, epithelial cancer cells in primary solid tumors typically partially lose their cell-cell adhesion with their neighbors, and simultaneously acquire mesenchymal traits of motility and invasion. Such a hybrid epithelial/mesenchymal (E/M) phenotype, also referred to as a partial epithelial-mesenchymal transition (pEMT) state, facilitates clustered or collective cell migration , 5. A coOnce individual CTCs and/or CTC clusters exit the bloodstream at a distant organ, they need to form secondary tumors. All three phenotypes – epithelial, mesenchymal, and hybrid E/M - have been correlated with possessing stemness, i.e. tumor-initiation ability –12, in dAdditionally, cell-cell signaling through the Notch signaling pathway has been implicated in modulating EMT, enhancing therapeutic resistance , expandiHere, we develop a mechanism-based mathematical model to elucidate the interconnections between EMT, CSCs and Notch signaling, by investigating the emergent dynamics due to the coupling among the EMT, STEM and NOTCH modules. We find that stemness traits tend to co-exist with a hybrid epithelial-mesenchymal (E/M) phenotype and strong Notch-Jagged signaling. Modulation by external signaling pathways can decouple the abovementioned correlation, suggesting that stem-like traits need not be exclusively correlated with a specific EMT phenotype. To validate this prediction, we examine the gene expression profile of CSCs from several cancer subtypes and find a heterogeneous distribution for the ‘stemness window’ along the ‘EMT axis’, thus enabling the existence of subsets of CSCs with epithelial, hybrid E/M or mesenchymal phenotypes. Lastly, we apply our formalism to model the action of metformin in targeting CSCs and inhibiting EMT, providing a mechanism-based explanation for several experimental findings, including decreased Notch1 levels in metformin-treated pancreatic cancer cells , metformTo elucidate the outcome of interconnections among Notch signaling, EMT and stemness, we hereby develop a mechanism-based mathematical framework that integrates the experimentally identified connections among these three crucial pro-metastatic modules, and explore any correspondence between the cell states enabled independently by these three modules.3 states combining the ,  and  states. Coupling between the modules can, however, introduce correlations which lower this to a much smaller number. The three modules are connected as follows .Given that each of the modules–Notch, EMT and STEM–can give rise to three different cell states independently, their coupling can possibly give rise to as much as 27 = 3NAIL > 1 , 30; (b)NAIL > 1 –33; and NAIL > 1 , thus, mTo investigate the cell-fate dynamics of this coupled network, we set up a mathematical model for the EMT-STEM-Notch coupled circuit (see Methods). The output of our model is a cell phenotype defined as a combination of  phenotype, depending on baseline conditions and strengths of interactions among these three modules.EXTL (either Delta or Jagged).As the first step to investigate the coupling among states of Notch signaling, EMT, and stemness (i.e. STEM module), we examined how an epithelial cell responds to varying levels of external Notch ligands Following the binding of Delta or Jagged to Notch, Notch signaling is activated and stimulates EMT, thereby decreasing the levels of miR-34 and miR-200, while increasing those of SNAIL and ZEB Figure . This prTaken together, these results highlight a strong correlation between a hybrid S/R Notch state, a hybrid E/M phenotype, and the expression of stem-like traits, or a D/U state. In other words, cells in a hybrid E/M phenotype are highly likely to exhibit stem-like properties and show enhanced Notch-Jagged signaling. Indeed, cells co-expressing various epithelial and mesenchymal genes display enhanced JAG1 levels in circulating tumor cell (CTC) clusters and in drug-tolerant breast cancer cells . Thus, tIn the following sections, we will refer to the modes of the EMT module  as states because they have a one to one correspondence with the mathematical solutions of the model. In other words, each branch of the bifurcation diagram of Figure LIMλ−28), and the cellular production fold-change of ZEB due to inhibition by let-7 (ZEBλ)   , 15. Base traits .We modelled the effect of Metformin on the coupled Notch-EMT-stemness circuit via shifted Hill functions  that negWhere M represent a constant level or intensity of metformin treatment. We are not conducting a detailed dose-response analysis here, thus, including metformin effectively results in a decrease in production rates for SNAIL and LIN-28. The parameters corresponding to the effect of metformin have been chosen empirically, and are considered to be the same for both LIN-28 and SNAIL . DetaileWe developed all source code in Python and used the numerical library PyDSTool  to compui =  that characterizes the probability of group membership. Categorization was assigned based on the maximal value of this ordered triple. Si was then projected onto  by use of the EMT metric. The metric places epithelial  samples close to 0 (resp. 2) while maximally hybrid E/M samples are assigned values close to 1.The EMT Metric previously described  was appl


As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at  RNA-protein interactions (RPIs) have a critical influence on post-transcriptional gene regulation ,2,3, virproteins ; severalproteins ; lncRNAsproteins ; lncRNA proteins . Hence, The experimental determination of RPIs remains expensive and time-consuming ,19,20, bIn this paper, we classified RNA-protein pairs as interacting or non-interacting by integrating derived kernel with regularized least squares (RLS) . The motk continuous amino acids, while the template set l continuous nucleic acids. In order to extract discriminant subsequence features from amino acid sequences and nucleotide sequences, we explored the effect on RPI prediction over a range of choices for the template sizes of protein and RNA. The training set RPI2662 was used to determine these parameters. In our case, we used different template sizes of protein and RNA chosen from set k and l, respectively. The experiment results showed that when the protein template size We considered the derived kernel with two-layer architecture, hence there were two template sets, denoted as eriments , therefoIn order to evaluate the reliability and robustness of RPiRLS and RPiRLS-7G, we compared them with other two state-of-the-art methods RPI-Pred and IPMiner. The RPI2241 and RPI369 data sets after removing overlapping RPIs with the training data were evaluated. Both the RPiRLS and RPiRLS-7G classifiers were trained on the RPI2662 data set, and tested on the RPI2241 and RPI369 data sets, respectively.As shown in Similar results were observed on the RPI2241 data set in Furthermore, we evaluated the RPiRLS classifier on large-scale RNA-protein pairs in the currently available RPIntDB data base. The RPiRLS method correctly predicted 35980 out of 43010 RPIs, reaching the predictive accuracy of 84%.Homo sapiens and Saccharomyces cerevisiae, RPI-Pred performed best for Drosophila melanogaster, Escherichia coli and Mus musculus, and both methods obtained the same predictive accuracy for the Caenorhabditis elegans. RPiRLS outperformed RPiRLS-7G over all six organisms. For 13153 ncRNA-protein pairs, the RPiRLS method achieved an accuracy of 91% compared to 76% for RPiRLS-7G and 88% for the RPI-Pred method. We further tested RPiRLS and RPiRLS-7G on the LNRPI12114 data set which was a subset of the NRPI13153 data set and consisted of only lncRNA-protein interactions (lncRPIs). Our model achieved an overall accuracy of 92% compared to 77% for RPiRLS-7G and 89% for the RPI-Pred classifier as shown in To explore the effectiveness of the proposed method on predicting ncRNA-protein interactions, a large-scale ncRNA-protein interaction data set  was retrieved from the NPInter data base . We traiCaenorhabditis elegans, Drosophila melanogaster, Escherichia coli and Saccharomyces cerevisiae, consisting of 4, 61, 78 and 437 lncRPIs, respectively.Predicting lncRNA-protein interaction networks is useful to explore the molecular mechanisms that are regulated by lncRNAs ,39. In tCaenorhabditis elegans, the RPiRLS method correctly identified all 4 lncRPIs (blue edges) while the RPI-Pred method correctly identified 3 out of 4. As shown in Drosophila melanogaster. These 6 out of 7 incorrect predictions which were observed between two proteins P49963 and Q9VSS2 (yellow rectangle) and three signal recognition particle (SRP) RNAs n5330, n5333 and n389 (green ellipse), formed the SRP RNA-protein interactions. For Escherichia coli, the RPiRLS classifier made much more errors than the RPI-Pred method, with predictive accuracies of 45% vs. 86%. The performance of RPiRLS for Escherichia coli was much poorer than that for the other five organisms. In order to analyze why RPiRLS had relative poor performance on Escherichia coli, we estimated the amino acid composition of Escherichia coli compared with that of the other five organisms. As shown in Escherichia coli had relative higher observing frequencies of Alanine and Valine as well as much lower content of Serine compared with that of the other five organisms. The amino acid composition bias in Escherichia coli probably leaded to poor results. As shown in Saccharomyces cerevisiae, as showed in For Mammalian cells contain more than 1000 different proteins interacting with RNA . NormallThe work presented here provided a computational method, called RPiRLS, to classify RNA-protein pairs as interacting or non-interacting by integrating a sequence-based derived kernel with regularized least squares. The derived kernel exploited the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. Our results demonstrated that only the sequence structures of RNAs and proteins provide sufficient information to accurately predict RNA-protein interactions, especially long non-coding RNA-protein interactions. Specifically, the RPiRLS classifier considered each protein sequence comprising up to 20 diverse amino acids, while the RPiRLS-7G classifier encoded each protein sequence by using the 7-letter reduced alphabets according to amino acid physiochemical properties. The computational results showed that the RPiRLS classifier was superior to the RPiRLS-7G classifier in reliability and effectiveness, indicating that the diversity of amino acids at a sequence has critical impact on the function of RNA-binding proteins. On two non-redundant benchmark data sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Compared with RPI-Pred and IPMiner, the RPiRLS method obtained a reasonable sensitivity at a lower false positive rate. Further, RPiRLS achieved an accuracy of 92% compared to 89% for RPI-Pred on the prediction of lncRNA-protein interactions. The RPiRLS method can be extended to construct RNA-protein interaction networks and therefore helps us to gain insights into post-transcriptional gene regulation.k-mer frequencies alone captured rich statistical information on the repetitive conserved motif of RNA-protein pairs and the diversity of amino acids at a sequence has also contributed to an observed improvement contrast to RPI-Pred which just applied 1-letter frequency for both protein and RNA. Finally, a kernel works as a measure of similarity and supports the application of powerful machine learning algorithms such as regularized least squares which we used in this paper. The RLS mehod enables us to efficiently search for an optimized parameter The reason for the good performance of the proposed method may be due to several factors. Firstly, the use of similarity scores is a significant conceptual change in protein/RNA evaluation, quantifying the overall similarity between proteins, RNAs and their interactions. Combining kernels by tensor product for the set of RNA-protein pairs allowing to share information across the RNA-binding proteins considerably improved the prediction, especially in the case of RNAs with few known binding proteins. Secondly, we have found that contiguous nal cost . FurtherThe increase of the number of protein-RNA complexes in Protein Data Bank  has openSeveral data sets were employed to evaluate the performance of the proposed methods.Our RPiRLS method was first evaluated using two popular non-redundant data sets of RPIs studied in  . The RPhttp://pridb.gdcb.iastate.edu/RPISeq/download.php). This data set consisted of 43,010 experimentally validated RPIs from several sources, including the RPIDB, NPInter data base and high-throughput experiments published in literature.Next, we tested the performance of the RPiRLS method on a large scale data set extracted from the RNA-Protein Interaction Data Base (RPIntDB)  if Step 1.k. Set an initial kernel Step 2.f, so here f at t :k making up the template set f, denoted as Let Step 3.Compute the second layer derived kernel by normalizing the inner product of two neural responses:With correlation normalization:Suppose This process can continue if appropriate higher level templates are defined. At each layer  derived kernels are built by recursively pooling over previously defined local kernels. Here, for the 2-layer derived kernel, pooling is accomplished by taking an average over a set of transformations which calculating the frequency of a template that occurs in a sequence.K which satisfies the Mercer Condition, are known to be an instance of reproducing kernels. Next with correlation normalization, In this paper we deal with inner product kernels The kernel function is symmetric  is further employed to measure the predictive performance, which is 1 for perfect prediction and 0.5 for random prediction.