A non-invasive method for concurrent detection of early-stage women-specific cancers.

We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across diverse age-groups and ethnicities. A two-step approach was employed wherein cancer-positive samples were first identified as a group. A second multi-class algorithm then helped to distinguish between the individual cancers of the group. The approach yielded high detection sensitivity and specificity, highlighting its utility for the development of multi-cancer detection tests especially for early-stage cancers.

Significant biopolymers and their applications in buccal mediated drug delivery.

Considerable research exercises have been directed towards the development of efficient and safe drug delivery systems. Various materials are used in different pharmaceutical formulations for the development of efficient drug delivery systems in the treatment of disease. Biopolymers are a choice of research as an excipient delivery system due to their biodegradability, low toxicity, safe, stable, and renewable nature. Biopolymers are naturally occurring polymers or polymer matrix composites, that are extracted from animals, bacteria, fungi, and plants. Cellulose, starches are carbohydrate-based polymers, and wool, silk, gelatin, and collagen are protein-based biopolymers. Biopolymers are obtained from various sources but biopolymers, that belong to the carbohydrate origin, have been found very promising in drug delivery through various routes. The review mainly focuses on the biopolymers currently in use for buccal-mediated pharmaceutical drug delivery systems because the buccal route is an efficient drug delivery system that allows direct systemic circulation of drugs. It also prevents the hydrolysis of the drug molecule in the gastrointestinal tract and thus increases the bioavailability of the drug. The present review discusses the overview of other drug delivery routes, challenges with conventional drug delivery systems, pharmaceutical applications of some biopolymers used in buccal drug delivery systems, that are published recently, currently in use, or used over the past decade.

Genome scale metabolic model driven strategy to delineate host response to Mycobacterium tuberculosis infection.

We analyze high throughput proteomics data reflecting the response of the Mφ-like THP1 cell line to Mycobacterium tuberculosis (M. tuberculosis) infection. M. tuberculosis's engagement with the host's metabolic pathways is a known strategy employed by the pathogen to shift the balance in its favour. Our study revisits this strategy through the integration of the temporal proteomics data in the genome-scale metabolic model (GSMM) giving context-specific GSMMs. THP1 cells were infected with H37Ra, H37Rv, BND433 and JAL2287 strains of M. tuberculosis and the host response was studied at 6, 18, 30 and 42 hours after infection. We have developed a modified flux balance analysis (FBA), which does not use an objective function, to find the fluxes of metabolic reactions in different strains and stages of infection and have revealed different functional modules. Hence, we have established a method of rewiring using GSMMs to explore potential strategies to change the flux state of virulent M. tuberculosis infected macrophages as against their avirulent counterparts. Our methodology gives a correlation between different flux states, the extent of which was interpreted as the extent of rewiring. The accuracy of the results from the proposed methodology was validated with gene knockout experimental data. We found that more than one reaction has to be rewired simultaneously to alter virulent to an avirulent response. The identified modules showed influence across the investigated strains and time points suggesting that these reactions could be therapeutically targeted. This novel methodology is now available for use in other systems.

Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models.

Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.

Consequences of Heterogeneous Crowding on an Enzymatic Reaction: A Residence Time Monte Carlo Approach.

Translational diffusion of a free substrate in crowded metabolically active spaces such as cell cytoplasm or mitochondrial matrix is punctuated by collisions and nonspecific interactions with soluble/immobile macromolecules/macrostructures in a variety of shapes/sizes. It is not understood how such disruptions alter enzyme reaction kinetics in such spaces. A novel Monte Carlo (MC) technique, "residence time MC", has been developed to study the kinetics of a simple enzyme-substrate reaction in a crowded milieu using a single immobile enzyme in the midst of diffusing substrates and products. The reaction time lost while the substrate nonspecifically interacts or is transiently trapped with ambient macromolecules is quantified by introducing the residence time "tau". Tau scales with the size of crowding macromolecules but makes the knowledge of their shape redundant. The residence time thus presents a convenient parameter to realistically mimic the sticky surroundings encountered by a diffusing substrate in heterogeneously crowded physiological spaces. Results reveal that for identical substrate concentration and excluded volume, increase in tau significantly diminished enzymatic product yield and reaction rate, slowed down substrate/product diffusion, and prolonged their relaxation times. A smooth transition from the anomalous subdiffusive motion to normal diffusion at long time limits was observed irrespective of the value of tau. The predictions from the model are shown to be in qualitative agreement with in vitro experimental data revealing the rate of alkaline phosphatase-catalyzed hydrolysis of p-nitrophenyl phosphate in the midst of 40/500/2000 kDa dextrans. Our findings from the residence time MC model also attempt to rationalize previously unexplained experimental observations in crowded enzyme kinetics literature. Furthermore, major insights to emerge from this study are the reasons why free diffusion of the substrate in crowded physiological spaces is detrimental to enzyme function. It is argued that organized enzyme clusters such as "metabolon" may perhaps exist to regulate the substrate translocation in such sticky physiological spaces to maintain optimal enzyme function. In summary, this work provides key insights explaining why absence of substrate channeling can dramatically slow down enzyme reaction rate in crowded metabolically active spaces.

Extracting proteins involved in disease progression using temporally connected networks.

BACKGROUND: Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease. RESULTS: In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap. CONCLUSIONS: The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.

Tracking disease progression by searching paths in a temporal network of biological processes.

Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time through the perturbations of biological processes. These perturbed biological processes usually work in an interdependent way. Systematic experiments tracking disease progression at gene level are usually conducted through a temporal microarray data. There is a need for developing methods to analyze such highly complex data to capture disease progression at the molecular level. In the present study, we have considered temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We first constructed a network between biological processes through common genes. We analyzed the data to obtain perturbed biological processes at each time point. Finally, we used the biological process network to find links between these perturbed biological processes. This enabled us to identify paths linking initial perturbed processes with final perturbed processes which capture disease progression. Using different datasets and statistical tests, we established that these paths are highly precise to the dataset from which these are obtained. We also established that the connecting genes present in these paths might contain some biological information and thus can be used for further mechanistic studies. The methods developed in our study are also applicable to a broad array of temporal data.

Extracting Genes Involved in Disease from a Connected Network of Perturbed Biological Processes.

Metabolic disorders such as obesity and diabetes are nowadays regarded as diseases affecting majority of population. These diseases develop gradually over time in an individual. Recently, systematic experiments tracking disease progression are conducted giving a high-throughput complex data. There is a pressing need for developing methods to analyze this complex data to capture the disease mechanism at molecular level. Diseases usually develop through perturbations of biological processes in an organism. In this study, we have tried to capture the interlinking between different biological processes that work together to regulate the disease phenotype. Here, we have considered a temporal microarray data from an experiment conducted to study obesity and diabetes in mice. We have analyzed the data to obtain perturbed biological processes and developed methods to establish link between these perturbed biological processes. We have derived a mathematical formula to score genes and identified a significant set of genes regulating such a complex process network. The methods developed in our study are also applicable to a broad array of data types.

A new method of finding groups of coexpressed genes and conditions of coexpression.

BACKGROUND: To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters. RESULTS: We present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters. CONCLUSIONS: Clustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.

Interactions among quorum sensing inhibitors.

Many pathogenic bacteria use quorum sensing (QS) systems to regulate the expression of virulence genes in a density-dependent manner. In one widespread QS paradigm the enzyme LuxI generates a small diffusible molecule of the acyl-homoserine lactone (AHL) family; high cell densities lead to high AHL levels; AHL binds the transcription factor LuxR, triggering it to activate gene expression at a virulence promoter. The emergence of antibiotic resistance has generated interest in alternative anti-microbial therapies that target QS. Inhibitors of LuxI and LuxR have been developed and tested in vivo, and can act at various levels: inhibiting the synthesis of AHL by LuxI, competitively or non-competitively inhibiting LuxR, or increasing the turnover of LuxI, LuxR, or AHL. Here use an experimentally validated computational model of LuxI/LuxR QS to study the effects of using inhibitors individually and in combination. The model includes the effect of transcriptional feedback, which generates highly non-linear responses as inhibitor levels are increased. For the ubiquitous LuxI-feedback virulence systems, inhibitors of LuxI are more effective than those of LuxR when used individually. Paradoxically, we find that LuxR competitive inhibitors, either individually or in combination with other inhibitors, can sometimes increase virulence by weakly activating LuxR. For both LuxI-feedback and LuxR-feedback systems, a combination of LuxR non-competitive inhibitors and LuxI inhibitors act multiplicatively over a broad parameter range. In our analysis, this final strategy emerges as the only robust therapeutic option.

Prediction by promoter logic in bacterial quorum sensing.

Quorum-sensing systems mediate chemical communication between bacterial cells, coordinating cell-density-dependent processes like biofilm formation and virulence-factor expression. In the proteobacterial LuxI/LuxR quorum sensing paradigm, a signaling molecule generated by an enzyme (LuxI) diffuses between cells and allosterically stimulates a transcriptional regulator (LuxR) to activate its cognate promoter (pR). By expressing either LuxI or LuxR in positive feedback from pR, these versatile systems can generate smooth (monostable) or abrupt (bistable) density-dependent responses to suit the ecological context. Here we combine theory and experiment to demonstrate that the promoter logic of pR - its measured activity as a function of LuxI and LuxR levels - contains all the biochemical information required to quantitatively predict the responses of such feedback loops. The interplay of promoter logic with feedback topology underlies the versatility of the LuxI/LuxR paradigm: LuxR and LuxI positive-feedback systems show dramatically different responses, while a dual positive/negative-feedback system displays synchronized oscillations. These results highlight the dual utility of promoter logic: to probe microscopic parameters and predict macroscopic phenotype.

Promoter reliability in modular transcriptional networks.

Synthetic biologists engineer systems with desired properties from simple and well-characterized biological parts. Among the most popular and versatile parts are tunable promoters and the transcription factors (TFs) that regulate them. Individual TFs can transduce physical or chemical signals to regulate gene expression; networks of TFs regulating each other's expression can filter signals, reduce noise, store memories, and oscillate. However, the biochemical parameters that describe TF-promoter interactions are often context dependent, making it challenging to build systems that reliably achieve specific outcomes. Here, we explore this problem using plasmid-borne transcriptional networks in Escherichia coli. We demonstrate that the expression properties of a positive-feedback module quantitatively and qualitatively change when this module is embedded within the context of a larger network, where the original TF is used to drive new outputs. A mathematical model suggests this might be due in part to the sequestration of the TF by additional copies of its cognate promoter. The parameters describing TF-promoter interactions (the Hill coefficient and half-saturation constant) can vary depending on promoter copy number. This problem is acute for plasmid-borne systems where promoter concentrations exceed the TF-promoter equilibrium constant. In this regime, we advocate the use of operator buffers: passive multimeric stretches of TF-binding sites that insulate promoter properties from context. If such buffers are included in a standard host chassis, promoters once characterized can be reliably integrated into larger networks.