Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.

IMPORTANCE

We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

A Case of Cobra Bite in a Term Pregnant Woman: The Obstetric and Wound Management Challenges

Snake envenomation is a rare incident during pregnancy and potentially challenging to manage. Snakebites in pregnancy may lead to several complications such as teratogenicity, miscarriage, antepartum hemorrhage, and even intrauterine fetal death. Here, we report a case of a pregnant woman who presented to our emergency department with signs of systemic envenomation following an Indian cobra bite on her foot, highlighting the key obstetric and wound management challenges. She complained of severe pain at the site of the bite and progressive swelling, abdominal pain, and multiple episodes of vomiting, which started 45 min after the bite. She received 10 vials of polyvalent antivenom from a primary hospital and was then referred to our center. The patient underwent emergency cesarean section and later fasciotomy with free-flap reconstruction at the bitten site due to local tissue necrosis. The case was successfully managed by a multidisciplinary team consisting of an emergency physician, obstetrician, and plastic surgeon, saving 2 lives and the limb of the patient.

Atmospheric Microplastics: Perspectives on Origin, Abundances, Ecological and Health Risks

Microplastic (MP) pollution has aroused a tremendous amount of public and scientific interest worldwide. MPs are found widely ranging from terrestrial to aquatic ecosystems primarily due to the over-exploitation of plastic products and unscientific disposal of plastic waste. There is a large availability of scientific literature on MP pollution in the terrestrial and aquatic ecosystems, especially the marine environments; however, only recently has greater scientific attention been focused on the presence of MPs in the air and its retrospective health implications. Besides, atmospheric transport has been reported to be an important pathway of transport of MPs to the pristine regions of the world. From a health perspective, existing studies suggest that airborne MPs are priority pollutant vectors, that may penetrate deep into the body through inhalation leading to adverse health impacts such as neurotoxicity, cancer, respiratory problems, cytotoxicity, and many more. However, their effects on indoor and outdoor air quality, and on human health are not yet clearly understood due to the lack of enough research studies on that and the non-availability of established scientific protocols for their characterization. This scientific review entails important information concerning the abundance of atmospheric MPs worldwide within the existing literature. A thorough comparison of existing sampling and analytical protocols has been presented. Besides, this review has unveiled the areas of scientific concern especially air quality monitoring which requires immediate attention, with the information gaps to be filled have been addressed.

Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family

Lactobacillaceae represent a large family of important microbes that are foundational to the food industry. Many genome sequences of Lactobacillaceae strains are now available, enabling us to conduct a comprehensive pangenome analysis of this family. We collected 3591 high-quality genomes from public sources and found that: 1) they contained enough genomes for 26 species to perform a pangenomic analysis, 2) the normalized Heap's coefficient λ (a measure of pangenome openness) was found to have an average value of 0.27 (ranging from 0.07 to 0.37), 3) the pangenome openness was correlated with the abundance and genomic location of transposons and mobilomes, 4) the pangenome for each species was divided into core, accessory, and rare genomes, that highlight the species-specific properties (such as motility and restriction-modification systems), 5) the pangenome of Lactiplantibacillus plantarum (which contained the highest number of genomes found amongst the 26 species studied) contained nine distinct phylogroups, and 6) genome mining revealed a richness of detected biosynthetic gene clusters, with functions ranging from antimicrobial and probiotic to food preservation, but ∼93% were of unknown function. This study provides the first in-depth comparative pangenomics analysis of the Lactobacillaceae family.

Anti-Biofilm: Machine Learning Assisted Prediction of IC50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing

Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC₅₀) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital-acquired infections. The virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze the biofilm production and antibiotic resistance of P. aeruginosa. Our analysis revealed: (i) 116 iModulons, 81 of which show strong association with known regulators; (ii) novel roles of regulators in modulating antibiotics efflux pumps; (iii) substrate-efflux pump associations; (iv) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; (v) differential activation of 'Cell Division' iModulon resulting from exposure to different beta-lactam antibiotics and (vi) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa virulence.

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the “Anti-HCV” platform using machine learning and quantitative structure–activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC₅₀/EC₅₀) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson’s correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The “Anti-HCV” predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.

Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa coordinates cellular processes in response to stimuli. We used 364 transcriptomes (281 publicly available + 83 in-house generated) to reconstruct the TRN of P. aeruginosa using independent component analysis. We identified 104 independently modulated sets of genes (iModulons) among which 81 reflect the effects of known transcriptional regulators. We identified iModulons that (i) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs), (ii) show increased expression of the BGCs and associated secretion systems in nutrient conditions that are important in cystic fibrosis, (iii) show the presence of a novel ribosomally synthesized and post-translationally modified peptide (RiPP) BGC which might have a role in P. aeruginosa virulence, (iv) exhibit interplay of amino acid metabolism regulation and central metabolism across different carbon sources and (v) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compared the identified iModulons of P. aeruginosa with those previously described in Escherichia coli to observe conserved regulons across two Gram-negative species. This comprehensive TRN framework encompasses the majority of the transcriptional regulatory machinery in P. aeruginosa, and thus should prove foundational for future research into its physiological functions.

Outcome Analysis of Dual Plating in Management of Unstable Bicondylar Tibial Plateau Fracture - A Prospective Study

Introduction:

Bicondylar tibial plateau fractures account for 10-30% of tibial plateau fractures. Despite recent advancements in the management of unstable bicondylar tibial plateau fractures, the outcomes are often poor. The present study aimed to evaluate the functional outcomes and complications of internal fixation of bicondylar tibial plateau fractures with the dual plating using two incisions.

Materials and methods:

The present study included 30 patients (26 males; 4 females, mean age 35.6 years; range, 19 to 65 years) with bicondylar tibial plateau fractures who were treated with dual plating between January 2017 to August 2019. Out of 30 patients, 5 patients had Schatzker type (V) and 25 patients had Schatzker type (VI) bicondylar tibial plateau fracture. All patients were treated with dual plating using two incisions. In all patient’s similar standard physical rehabilitation therapy was followed. All complications including intra and post-operative were assessed and recorded. The patients were followed-up for over 24 months. Functional outcomes were assessed with Rasmussen’s functional grading system, Oxford knee score, and range of motion of knee joint. Radiological outcomes were evaluated using Rasmussen’s radiological scoring system.

Result:

All fractures united with a mean time of 18 weeks. The average knee range of motion was 1.5° - 130° (range: 0° - 10° for extension lag, range: 100° -135° for flexion). Mean Rasmussen's functional grading score at the final follow-up was 26.75. All patients showed excellent or good radiographic results according to Rasmussen’s radiological scoring with a mean score of 8.5 (range 6-10). The post-operative radiographs showed mean MPTA was 84.3° and the mean PPTA was 6.2°. In the present study, complications were encountered in five patients. However, there were no cases of secondary loss of reduction, failure of the implant, malunion, or non-union.

Conclusion:

The surgical treatment of bicondylar tibial plateau fractures with dual locking represents a significant treatment option and provides rigid fixation in these fractures with good functional and radiological outcomes.

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC₅₀ values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T²⁷⁴) dataset. The robustness of the developed models was cross-evaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola . We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis

Viral epidemics and pandemics are considered public health emergencies. However, traditional and novel antiviral discovery approaches are unable to mitigate them in a timely manner. Notably, drug repurposing emerged as an alternative strategy to provide antiviral solutions in a timely and cost-effective manner. In the literature, many FDA-approved drugs have been repurposed to inhibit viruses, while a few among them have also entered clinical trials. Using experimental data, we identified repurposed drugs against 14 viruses responsible for causing epidemics and pandemics such as SARS-CoV-2, SARS, Middle East respiratory syndrome, influenza H1N1, Ebola, Zika, Nipah, chikungunya, and others. We developed a novel computational “drug-target-drug” approach that uses the drug-targets extracted for specific drugs, which are experimentally validated in vitro or in vivo for antiviral activity. Furthermore, these extracted drug-targets were used to fetch the novel FDA-approved drugs for each virus and prioritize them by calculating their confidence scores. Pathway analysis showed that the majority of the extracted targets are involved in cancer and signaling pathways. For SARS-CoV-2, our method identified 21 potential repurposed drugs, of which 7 (e.g., baricitinib, ramipril, chlorpromazine, enalaprilat, etc.) have already entered clinical trials. The prioritized drug candidates were further validated using a molecular docking approach. Therefore, we anticipate success during the experimental validation of our predicted FDA-approved repurposed drugs against 14 viruses. This study will assist the scientific community in hastening research aimed at the development of antiviral therapeutics.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

DrugRepV: a compendium of repurposed drugs and chemicals targeting epidemic and pandemic viruses

Viruses are responsible for causing various epidemics and pandemics with a high mortality rate e.g. ongoing SARS-CoronaVirus-2 crisis. The discovery of novel antivirals remains a challenge but drug repurposing is emerging as a potential solution to develop antivirals in a cost-effective manner. In this regard, we collated the information of repurposed drugs tested for antiviral activity from literature and presented it in the form of a user-friendly web server named ‘DrugRepV’. The database contains 8485 entries (3448 unique) with biological, chemical, clinical and structural information of 23 viruses responsible to cause epidemics/pandemics. The database harbors browse and search options to explore the repurposed drug entries. The data can be explored by some important fields like drugs, viruses, drug targets, clinical trials, assays, etc. For summarizing the data, we provide overall statistics of the repurposed candidates. To make the database more informative, it is hyperlinked to various external repositories like DrugBank, PubChem, NCBI-Taxonomy, Clinicaltrials.gov, World Health Organization and many more. ‘DrugRepV’ database (https://bioinfo.imtech.res.in/manojk/drugrepv/) would be highly useful to the research community working to develop antivirals.

Identifying the effect of vancomycin on health care-associated methicillin-resistant Staphylococcus aureus strains using bacteriological and physiological media

BACKGROUND

The evolving antibiotic-resistant behavior of health care-associated methicillin-resistant Staphylococcus aureus (HA-MRSA) USA100 strains are of major concern. They are resistant to a broad class of antibiotics such as macrolides, aminoglycosides, fluoroquinolones, and many more.

FINDINGS

The selection of appropriate antibiotic susceptibility examination media is very important. Thus, we use bacteriological (cation-adjusted Mueller-Hinton broth) as well as physiological (R10LB) media to determine the effect of vancomycin on USA100 strains. The study includes the profiling behavior of HA-MRSA USA100 D592 and D712 strains in the presence of vancomycin through various high-throughput assays. The US100 D592 and D712 strains were characterized at sub-inhibitory concentrations through growth curves, RNA sequencing, bacterial cytological profiling, and exo-metabolomics high throughput experiments.

CONCLUSIONS

The study reveals the vancomycin resistance behavior of HA-MRSA USA100 strains in dual media conditions using wide-ranging experiments.

NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus

Nipah virus (NiV) is an emerging and priority pathogen from the Paramyxoviridae family with a high fatality rate. It causes various diseases such as respiratory ailments and encephalitis and poses a great threat to humans and livestock. Despite various efforts, there is no approved antiviral treatment available. Therefore, to expedite and assist the research, we have developed an integrative resource NipahVR (http://bioinfo.imtech.res.in/manojk/nipahvr/) for the multi-targeted putative therapeutics and epitopes for NiV. It is structured into different sections, i.e. genomes, codon usage, phylogenomics, molecular diagnostic primers, therapeutics (siRNAs, sgRNAs, miRNAs) and vaccine epitopes (B-cell, CTL, MHC-I and -II binders). Most decisively, potentially efficient therapeutic regimens targeting different NiV proteins and genes were anticipated and projected. We hope this computational resource would be helpful in developing combating strategies against this deadly pathogen. Database URL: http://bioinfo.imtech.res.in/manojk/nipahvr/.

aBiofilm: a resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance

Biofilms play an important role in the antibiotic drug resistance, which is threatening public health globally. Almost, all microbes mimic multicellular lifestyle to form biofilm by undergoing phenotypic changes to adapt adverse environmental conditions. Many anti-biofilm agents have been experimentally validated to disrupt the biofilms during last three decades. To organize this data, we developed the ‘aBiofilm’ resource (http://bioinfo.imtech.res.in/manojk/abiofilm/) that harbors a database, a predictor, and the data visualization modules. The database contains biological, chemical, and structural details of 5027 anti-biofilm agents (1720 unique) reported from 1988–2017. These agents target over 140 organisms including Gram-negative, Gram-positive bacteria, and fungus. They are mainly chemicals, peptides, phages, secondary metabolites, antibodies, nanoparticles and extracts. They show the diverse mode of actions by attacking mainly signaling molecules, biofilm matrix, genes, extracellular polymeric substances, and many more. The QSAR based predictor identifies the anti-biofilm potential of an unknown chemical with an accuracy of ∼80.00%. The data visualization section summarized the biofilm stages targeted (Circos plot); interaction maps (Cytoscape) and chemicals diversification (CheS-Mapper) of the agents. This comprehensive platform would help the researchers to understand the multilevel communication in the microbial consortium. It may aid in developing anti-biofilm therapeutics to deal with antibiotic drug resistance menace.

Computational Identification of Inhibitors Using QSAR Approach Against Nipah Virus

Nipah virus (NiV) caused several outbreaks in Asian countries including the latest one from Kerala state of India. There is no drug available against NiV till now, despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed the first “anti-Nipah” web resource, which comprising of a data repository, prediction method, and data visualization module. The database contains of 313 (181 unique) chemicals extracted from research articles and patents, which were tested for different strains of NiV isolated from various outbreaks. Moreover, the quantitative structure–activity relationship (QSAR) based regression predictors were developed using chemicals having half maximal inhibitory concentration (IC₅₀). Predictive models were accomplished using support vector machine employing 10-fold cross validation technique. The overall predictor showed the Pearson's correlation coefficient of 0.82 on training/testing dataset. Likewise, it also performed equally well on the independent validation dataset. The robustness of the predictive model was confirmed by applicability domain (William's plot) and scatter plot between actual and predicted efficiencies. Further, the data visualization module from chemical clustering analysis displayed the diversity in the NiV inhibitors. Therefore, this web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly web server is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/.

Anti-flavi: A Web Platform to Predict Inhibitors of Flaviviruses Using QSAR and Peptidomimetic Approaches

Flaviviruses are arboviruses, which comprises more than 70 viruses, covering broad geographic ranges, and responsible for significant mortality and morbidity globally. Due to the lack of efficient inhibitors targeting flaviviruses, the designing of novel and efficient anti-flavi agents is an important problem. Therefore, in the current study, we have developed a dedicated prediction algorithm anti-flavi, to identify inhibition ability of chemicals and peptides against flaviviruses through quantitative structure–activity relationship based method. We extracted the non-redundant 2168 chemicals and 117 peptides from ChEMBL and AVPpred databases, respectively, with reported IC₅₀ values. The regression based model developed on training/testing datasets of 1952 chemicals and 105 peptides displayed the Pearson’s correlation coefficient (PCC) of 0.87, 0.84, and 0.87, 0.83 using support vector machine and random forest techniques correspondingly. We also explored the peptidomimetics approach, in which the most contributing descriptors of peptides were used to identify chemicals having anti-flavi potential. Conversely, the selected descriptors of chemicals performed well to predict anti-flavi peptides. Moreover, the developed model proved to be highly robust while checked through various approaches like independent validation and decoy datasets. We hope that our web server would prove a useful tool to predict and design the efficient anti-flavi agents. The anti-flavi webserver is freely available at URL http://bioinfo.imtech.res.in/manojk/antiflavi.

Computational Exploration of Putative LuxR Solos in Archaea and Their Functional Implications in Quorum Sensing

LuxR solos are unexplored in Archaea, despite their vital role in the bacterial regulatory network. They assist bacteria in perceiving acyl homoserine lactones (AHLs) and/or non-AHLs signaling molecules for establishing intraspecies, interspecies, and interkingdom communication. In this study, we explored the potential LuxR solos of Archaea from InterPro v62.0 meta-database employing taxonomic, probable function, distribution, and evolutionary aspects to decipher their role in quorum sensing (QS). Our bioinformatics analyses showed that putative LuxR solos of Archaea shared few conserved domains with bacterial LuxR despite having less similarity within proteins. Functional characterization revealed their ability to bind various AHLs and/or non-AHLs signaling molecules that involve in QS cascades alike bacteria. Further, the phylogenetic study indicates that Archaeal LuxR solos (with less substitution per site) evolved divergently from bacteria and share distant homology along with instances of horizontal gene transfer. Moreover, Archaea possessing putative LuxR solos, exhibit the correlation between taxonomy and ecological niche despite being the inhabitant of diverse habitats like halophilic, thermophilic, barophilic, methanogenic, and chemolithotrophic. Therefore, this study would shed light in deciphering the role of the putative LuxR solos of Archaea to adapt varied habitats via multilevel communication with other organisms using QS.

Acute Rejection of a Kidney Transplant in a Patient With Common Variable Immunodeficiency: A Case Report

Common variable immunodeficiency is a primary immunodeficiency characterized by hypogammaglobulinemia and recurrent bacterial infections. We report a case of a 44-year-old male patient with end-stage renal disease and an established diagnosis of common variable immunodeficiency who underwent a living unrelated kidney transplant. He remained nearly infection free on maintenance immunoglobulin replacement. However, his posttransplant course was complicated by acute rejection that ultimately led to allograft loss. This case illustrates the challenge of transplantation in this patient population because of the delicate balance that must be achieved between maintaining adequate immunosuppression and minimizing the risk of infection.

ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system

Genome editing by sgRNA a component of CRISPR/Cas system emerged as a preferred technology for genome editing in recent years. However, activity and stability of sgRNA in genome targeting is greatly influenced by its sequence features. In this endeavor, a few prediction tools have been developed to design effective sgRNAs but these methods have their own limitations. Therefore, we have developed "ge-CRISPR" using high throughput data for the prediction and analysis of sgRNAs genome editing efficiency. Predictive models were employed using SVM for developing pipeline-1 (classification) and pipeline-2 (regression) using 2090 and 4139 experimentally verified sgRNAs respectively from Homo sapiens, Mus musculus, Danio rerio and Xenopus tropicalis. During 10-fold cross validation we have achieved accuracy and Matthew's correlation coefficient of 87.70% and 0.75 for pipeline-1 on training dataset (T(1840)) while it performed equally well on independent dataset (V(250)). In pipeline-2 we attained Pearson correlation coefficient of 0.68 and 0.69 using best models on training (T(3169)) and independent dataset (V(520)) correspondingly. ge-CRISPR (http://bioinfo.imtech.res.in/manojk/gecrispr/) for a given genomic region will identify potent sgRNAs, their qualitative as well as quantitative efficiencies along with potential off-targets. It will be useful to scientific community engaged in CRISPR research and therapeutics development.

MSLVP: prediction of multiple subcellular localization of viral proteins using a support vector machine

Knowledge of the subcellular location (SCL) of viral proteins in the host cell is important for understanding their function in depth.

SigMol: repertoire of quorum sensing signaling molecules in prokaryotes

Quorum sensing is a widespread phenomenon in prokaryotes that helps them to communicate among themselves and with eukaryotes. It is driven through quorum sensing signaling molecules (QSSMs) in a density dependent manner that assists in numerous biological functions like biofilm formation, virulence factors secretion, swarming motility, bioluminescence, etc. Despite immense implications, dedicated resources of QSSMs are lacking. Therefore, we have developed SigMol (http://bioinfo.imtech.res.in/manojk/sigmol), a specialized repository of these molecules in prokaryotes. SigMol harbors information on QSSMs pertaining to different quorum sensing signaling systems namely acylated homoserine lactones (AHLs), diketopiperazines (DKPs), 4-hydroxy-2-alkylquinolines (HAQs), diffusible signal factors (DSFs), autoinducer-2 (AI-2) and others. Database contains 1382 entries of 182 unique signaling molecules from 215 organisms. It encompasses biological as well as chemical aspects of signaling molecules. Biological information includes genes, preliminary bioassays, identification assays and applications, while chemical detail comprises of IUPAC name, SMILES and structure. We have provided user-friendly browsing and searching facilities for easy data retrieval and comparison. We have gleaned information of diverse QSSMs reported in literature at a single platform ‘SigMol’. This comprehensive resource will assist the scientific community in understanding intraspecies, interspecies or interkingdom networking and further help to unfold different facets of quorum sensing and related therapeutics.

Prediction and analysis of quorum sensing peptides based on sequence features

Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew’s correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T^200p+200n). Developed models performed equally well on the validation dataset (V^20p+20n). The server also integrates several useful analysis tools like “QSMotifScan”, “ProtFrag”, “MutGen” and “PhysicoProp”. Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm “QSPpred” (freely available at: http://crdd.osdd.net/servers/qsppred).

Successful autotransplantation of a mature mesiodens to replace a traumatized maxillary central incisor

AIM

This case describes the successful transplantation of a mature mesiodens tooth to replace a traumatized maxillary central incisor.

SUMMARY

A 17-year-old male attended 1 week after a traumatic injury to his left maxillary central incisor (tooth 21). Radiographs revealed a horizontal root fracture and a poor prognosis. The tooth was atraumatically removed and replaced with a mesiodens lying in the same region. After stabilization, root canal treatment was performed and aesthetics were restored with a tooth coloured restoration. A 2-year follow-up revealed the tooth had good aesthetics and function.

KEY LEARNING POINTS

A supernumerary nonfunctional tooth such as a mesiodens can be successfully used to replace a missing permanent tooth by autotransplantation. Autotransplantation has a high success rate if case selection is good, appropriate surgery is carried out and excellent hygiene is maintained. Autotransplantation should be considered as one of the most biologic techniques for replacing a missing tooth with minimal cost. Autotransplantation can be carried out even after complete root formation in the donor tooth.

Identification of FUS p.R377W in essential tremor

BACKGROUND AND PURPOSE

Exome sequencing analysis has recently identified a nonsense mutation in fused in sarcoma (FUS) segregating with essential tremor (ET) within a large French-Canadian family. Further characterization of FUS resulted in the identification of additional mutations in ET patients; however, their pathogenicity still remains to be confirmed. The role of FUS in an independent cohort of ET patients from Canada was evaluated.

METHODS

The entire coding sequence of FUS in 217 patients diagnosed with ET was analyzed and two missense variants in 219 healthy controls were genotyped by Sanger sequencing.

RESULTS

Sequencing of FUS identified a previously reported non-pathogenic mutation p.G174_G175del in one ET patient and two healthy controls, and a novel p.R377W in one patient with family history of disease. This mutation is highly conserved and strongly predicted to be damaging by in silico analysis.

CONCLUSION

This study has identified a novel FUS p.R377W substitution in ET patients. Additional genotyping studies in a large number of ET patients and controls are necessary to conclusively define its pathogenicity.

Abstract P3-03-01: 3D mapping of total choline in human breast cancer using high-speed MR spectroscopic imaging at 3T: initial experience during neoadjuvant therapy

OBJECTIVE: To assess the feasibility of quantitative high-speed MR spectroscopic imaging (MRSI) of total Choline (tCho) as an adjunct to dynamic-contrast enhanced MRI to improve lesion characterization and monitor treatment response in patients undergoing neoadjuvant chemotherapy (NAC).

METHODS: Twelve patients with infiltrating ductal carcinoma (Table 1) were studied using a clinical 3T MR scanner (Siemens, Erlangen, Germany) equipped with 8- and 16-channel breast array (Hologic Inc., Bedford, MA). Four patients were studied before NAC. Four patients were studied once during NAC. Two patients were studied before and within 2–7 days of treatment initiation. One of these patients participated in an additional scan after 5 months of treatment. Two additional patients were studied at 3 time points during NAC. Measurements were performed using PRESS prelocalized 3D Proton-Echo-Planar-Spectroscopic-Imaging (PEPSI) using TR/TE=2000ms/135ms, matrix size up to 32×16×8, voxel size = 1cc, and total acquisition time of 10 minutes (including water reference scan). Additional data were collected at TE 60 ms to enhance sensitivity for detecting tCho and J-coupled resonances. TE-averaging (8 steps, DTE: 2.5 ms) was employed to minimize gradient sideband artifacts. Quantification of tCho in reference to tissue water was performed using spectral fitting and relaxation correction.

RESULTS: Strongly elevated tCho with maximum concentration up to 5.3 mmol/kg was measured in 9 patients with enhancing lesions larger than 2 cc volume (Table 2). Decreases in tCho were measured in all four patients who were followed during neoadjuvant chemotherapy. Decreases in tCho were measurable during the first week of neoadjuvant treatment in responders, consistent with previous studies. Our preliminary data also indicate that the combination of concentration and spatial extent of detectable tCho may be the most sensitive marker of treatment response.

Gamma-aminobutyric acid (GABA)-B receptor 1 in cerebellar cortex of essential tremor

Some reports suggest cerebellar dysfunction as the basis of essential tremor (ET). Several drugs with the action of gamma-aminobutyric acid (GABA) are known to improve ET. Autopsy studies were performed on brains from nine former patients followed at the Movement Disorders Clinic Saskatchewan, Canada, and compared with five normal control brains. We aimed to measure the concentration of GABA B receptor 1 (GBR1) in the brains of patients who had had ET and to compare them to the GABA concentration in brains of controls. Western blot was used to determine the expression of GBR1 in cerebellar cortex tissue. We found that compared to the controls, the ET brains had three different patterns of GBR1 protein concentration--two with high, four comparable, and three with marginally low levels. There was no association between the age of onset, severity or duration of tremor, the response to alcohol or other drugs and GBR1 level. Thus, we conclude that our study does not support that GBR1 is involved in ET. Further studies are needed to verify these results.

P2-10-04: 3D Mapping of Total Choline in Human Breast Cancer Using High-Speed MR Spectroscopic Imaging at 3T: A Feasibility Study

PURPOSE: To assess the feasibility of quantitative high-speed MR spectroscopic imaging (MRSI) of total Choline (tCho) as an adjunct to dynamic-contrast enhanced (DCE) MRI to improve lesion characterization and monitor treatment response in patients undergoing neoadjuvant chemotherapy.

METHOD AND MATERIALS: Seven patients with biopsy-confirmed, infiltrating ductal carcinoma were studied using a clinical 3T MR scanner (Siemens Medical Solutions, Erlangen, Germany) equipped with 8- and 16-channel breast array (Hologic Inc., Bedford, MA). Measurements were performed using PRESS prelocalized 3D Proton-Echo-Planar-Spectroscopic-Imaging (PEPSI) using TR/TE=2000ms/135ms, matrix size up to 32×16x8, voxel size=1cc, and total acquisition time of 10 minutes (including water reference scan). Additional data were collected at TE 60 ms to enhance sensitivity for detecting tCho and J-coupled resonances. TE-averaging (8 steps, ΔTE: 2.5 ms) was employed to minimize gradient sideband artifacts. Quantification of tCho in reference to tissue water was performed using spectral fitting and relaxation correction.

RESULTS: Strongly elevated tCho with maximum concentration ranging from 0.3 to 4.1 mmol/kg was measured in five patients with single and multi-centric enhancing lesions larger than 2 cc volume (Table 1). The measured tCho concentration in Grade 3 tumors was higher than in lower grade untreated and treated tumors. Strong decreases in tCho concentration were measured in 2 patients undergoing neoadjuvant therapy in a follow-up scan. At short TE an additional resonance was detected that was elevated in enhancing lesions and tentatively assigned to Taurine. Two patients had lesions smaller than 2 cc with surgical clips in which tCho was not detectable due to line broadening. MRSI data sets were preferentially collected before contrast injection, since it increased spectral line width by up to 50%.

CONCLUSION: This study demonstrates feasibility of quantitatively mapping tCho in invasive breast carcinoma using high-speed MRSI. The long-term goals are to utilize high-speed MRSI as an early predictor of treatment failure in women undergoing neoadjuvant therapy (i.e. chemotherapy, endocrine therapy or biologic therapy) for breast cancer and to develop an improved screening protocol for high-risk patients. Grant support: 1RC1EB010617-01

Abstract P2-06-11: Ramifications of HER2/ER/PR Guidelines from ASCO/CAP for Translational Cancer Research Using a Cohort from a Tertiary Care Centre in Ontario

Background: A transdisciplinary team from basic science, pathology, clinical and biostatistics was assembled to establish a framework with which to take novel laboratory biomarkers and targets to clinical validation. Human epidermal growth factor receptor (HER2), estrogen (ER) and progesterone (PR) receptor are of important prognostic and predictive value and drivers of systemic therapy for breast cancer (BC). As a first step, the current ASCO/CAP guidelines were used to re-assign centrally reviewed tumour specimens and compare to the clinically assigned scores for ER/PR and HER2.

Methods: With REB approval, a cohort of 62 cases of non-metastatic invasive BC with banked tumour specimens was assembled between 2005 and 2007. Clinico-pathological information for each case was retrospectively obtained from the medical file and entered into an anonymized database. Full section slides were originally stained by routine immunohistochemistry (IHC). Categorical clinical scores for ER/PR (negative-neg/weak/positive-pos) were compared to the continuous scores assigned in a blinded fashion using ASCO/CAP criteria (% pos/H-score). Categorical clinical scores obtained with duplicate IHC antibody staining of full sections for HER2 (neg/equivocal-eq/pos) were compared to those obtained from IHC assessments of triplicate 6mm cores in a tissue microarray (TMA) that were assigned to be neg/eq/pos using ASCO/CAP criteria. A senior breast pathologist adjudicated discordant specimens. Exact Fisher tests were used to compare the two sets of categorical assessments.

Results: Mean age was 43.5 years, (range 29-49). The majority of the cohort (59.7%) had N0 disease and received adjuvant chemotherapy (74.2%); 72.6% of the cohort was alive at the time of this analysis. Score means and ranges of ER/PR are displayed below. Two of 16 clinically ER neg cases (12.5%) were rescored as pos and 0/43 clinically ER pos cases were rescored as neg, P<0.0001. Two of 13 clinically PR neg cases (15.4%) were rescored as pos and 4/46 clinically PR pos cases (8.7%) were rescored as neg, (P<0.0001). HER2 status was reassessed for 51 cases, 41 of which (80%) had concordant scores (P<0.0001). Thirty-nine (76%) cases were classified as HER2 neg on TMA, 7 of which (18%) were eq on routine IHC and neg by fluorescence in situ hybridization. In routine IHC, 15.7% of tumours were eq. Four TMA cases were eq (7.8%%); with routine IHC, one of these was neg, one eq, and two were pos. Eight patients were HER2 pos in both assessments.

ER/PR scores

Conclusions: Systemic therapy recommendations could be impacted in a small but substantive number of cases by the methodology used for biomarker assessment and scoring, particularly near threshold values. This study illustrates that the scoring criteria used may be an important contributor to variability in correlative biomarker studies. Consideration should be given to routine systematic reassessment with continuous scoring for biomarker data proposed for use in correlative science studies.

Death-associated protein kinase 1 variation and Parkinson's disease

BACKGROUND AND PURPOSE

  Mutations of the LRRK2 gene are now recognized as major risk factors for Parkinson's disease. The Lrrk2 protein is a member of the ROCO family, which also includes Lrrk1 and Dapk1. Functional genetic variants of the DAPK1 gene (rs4877365 and rs4878104) have been previously associated with Alzheimer's disease.

METHODS

  Herein, we assessed the role of DAPK1 variants (rs4877365 and rs4878104) in risk of Parkinson's disease with Sequenom iPLEX genotyping, employing one Taiwanese series (391 patients with Parkinson's disease, 344 controls) and five separate Caucasian series' (combined sample size 1962 Parkinson's disease patients, 1900 controls).

RESULTS

  We observed no evidence of association for rs4877365 and rs4878104 and risk of Parkinson's disease in any of the individual series or in the combined Caucasian series under either an additive or recessive model.

CONCLUSION

  These specific DAPK1 intronic variants do not increase the risk of Parkinson's disease. However, further functional studies are required to elucidate the potential therapeutic implications with the dimerization of the Dapk1 and Lrrk2 proteins.

Meeting the 12 lymph node (LN) benchmark in colon cancer

BACKGROUND

Examining >or=12 LN in colon cancer has been suggested as a quality metric. The purpose of this study was to determine whether the 12 LN benchmark is achieved at NCCN centers compared to a US population-based sample.

METHODS

Patients with stage I-III disease resected at NCCN centers were identified from a prospective database (n = 718) and were compared to 12,845 stage I-III patients diagnosed in a SEER region. Age, gender, location, stage, number of positive nodes were compared for NCCN and SEER data in regards to number of nodes evaluated. Multivariate logistic regression models were developed to identify factors associated with evaluating 12 LNs.

RESULTS

92% of NCCN and 58% of SEER patients had >or=12 LN evaluated. For patients treated at NCCN centers, factors associated with not meeting the 12 LN target were left-sided tumors, stage I disease and BMI >30.

CONCLUSIONS

>or=12 LN are almost always evaluated in NCCN patients. In contrast, this target is achieved in 58% of SEER patients. With longer follow-up of the NCCN cohort we will be able to link this quality metric to patterns of recurrence and survival and thereby better understand whether increasing the number of nodes evaluated is a priority for cancer control.