PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics.

PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron).

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches.

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

The Solvation of the E. coli CheY Phosphorylation Site Mapped by XFMS.

The Escherichia coli CheY protein belongs to a large bacterial response regulator superfamily. X-ray hydroxy radical foot-printing with mass spectroscopy (XFMS) has shown that allosteric activation of CheY by its motor target triggers a concerted internalization of aromatic sidechains. We reanalyzed the XFMS data to compare polar versus non-polar CheY residue positions. The polar residues around and including the 57D phosphorylated site had an elevated hydroxy radical reactivity. Bioinformatic measures revealed that a water-mediated hydrogen bond network connected this ring of residues with the central 57D. These residues solvated 57D to energetically stabilize the apo-CheY fold. The abundance of these reactive residues was reduced upon activation. This result was supported by the bioinformatics and consistent with the previously reported activation-induced increase in core hydrophobicity. It further illustrated XFMS detection of structural waters. Direct contacts between the ring residues and the phosphorylation site would stabilize the aspartyl phosphate. In addition, we report that the ring residue, 18R, is a constant central node in the 57D solvation network and that 18R non-polar substitutions determine CheY diversity as assessed by its evolutionary trace in bacteria with well-studied chemotaxis. These results showcase the importance of structured water dynamics for phosphorylation-mediated signal transduction.

Host biomarkers for monitoring therapeutic response in extrapulmonary tuberculosis.

PURPOSE: The aim of this study was to explore the utility of inflammatory biomarkers in the peripheral blood to predict response to treatment in extrapulmonary tuberculosis (EPTB). METHODS: A Luminex xMAP-based multiplex immunoassay was used to measure 40 inflammatory biomarkers in un-stimulated plasma of 91 EPTB patients (48 lymphadenitis, and 43 pleuritis) before and at 2 and 6 months of treatment. RESULTS: Overall a significant change was observed in 28 inflammatory biomarkers with treatment in EPTB patients. However, MIG/CXCL9, IP-10/CXCL10, and CCL23 decreased in all patients' groups with successful treatment at both time points. At 2 months, 29/64 (45%) patients responded partially while 35/64 (55%) showed complete regress. Among good responders, a higher number of biomarkers (16/40) reduced significantly as compared to partial responders (1/40). Almost half (14/29) of partial responders required longer treatment than 6 months to achieve satisfactory response. The levels of MIG, IP-10, MIF, CCL22 and CCL23 reduced significantly among 80, 74, 60, 71, 51% good responders, as compared to 52, 52, 52, 59, 52% partial responders, respectively. A biosignature, defined by a significant decrease in any one of these five biomarkers, corresponded with satisfactory response to treatment in 97% patients at 2 month and 99% patients at 6 months of treatment. CONCLUSION: Change in inflammatory biomarkers correlates with treatment success. A five biomarker biosignature (MIG, IP-10, MIF, CCL22 and CCL23) could be used as an indicator of treatment success.

1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat.

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

Evolution of efficacious pangenotypic hepatitis C virus therapies.

Hepatitis C compromises the quality of life of more than 350 million individuals worldwide. Over the last decade, therapeutic regimens for treating hepatitis C virus (HCV) infections have undergone rapid advancements. Initially, structure-based drug design was used to develop molecules that inhibit viral enzymes. Subsequently, establishment of cell-based replicon systems enabled investigations into various stages of HCV life cycle including its entry, replication, translation, and assembly, as well as role of host proteins. Collectively, these approaches have facilitated identification of important molecules that are deemed essential for HCV life cycle. The expanded set of putative virus and host-encoded targets has brought us one step closer to developing robust strategies for efficacious, pangenotypic, and well-tolerated medicines against HCV. Herein, we provide an overview of the development of various classes of virus and host-directed therapies that are currently in use along with others that are undergoing clinical evaluation.

ELECANS--an integrated model development environment for multiscale cancer systems biology.

MOTIVATION: Computational multiscale models help cancer biologists to study the spatiotemporal dynamics of complex biological systems and to reveal the underlying mechanism of emergent properties. RESULTS: To facilitate the construction of such models, we have developed a next generation modelling platform for cancer systems biology, termed 'ELECANS' (electronic cancer system). It is equipped with a graphical user interface-based development environment for multiscale modelling along with a software development kit such that hierarchically complex biological systems can be conveniently modelled and simulated by using the graphical user interface/software development kit combination. Associated software accessories can also help users to perform post-processing of the simulation data for visualization and further analysis. In summary, ELECANS is a new modelling platform for cancer systems biology and provides a convenient and flexible modelling and simulation environment that is particularly useful for those without an intensive programming background. AVAILABILITY AND IMPLEMENTATION: ELECANS, its associated software accessories, demo examples, documentation and issues database are freely available at http://sbie.kaist.ac.kr/sub_0204.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics.

Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative impact on glucose homeostasis, in particular, insulin sensitivity. Together, these factors contribute to the pathophysiology of diabetes (DM) and expanding landscape of its prevalence regionally and globally. The rapid rise in the prevalence of type 2 diabetes, therefore, underscores the need for its early diagnosis and treatment. In this work, we have evaluated the discriminatory capacity of different diagnostic markers including inflammatory biomolecules and RBC (Red Blood Cell) indices in predicting the risk of hyperglycemia and borderline hyperglycemia. For that, 208,137 clinical diagnostic entries obtained over five years from Chugtai Labs, Pakistan, were retrospectively evaluated. The dataset included HbA1c (n = 142,011), complete blood count (CBC, n = 84,263), fasting blood glucose (FBG, n = 35,363), and C-reactive protein (CRP, n = 9035) tests. Our results provide four glycemic predictive models for two cohorts HbA1c and FBG) each having an overall predictive accuracy of more than 80% (p-value < 0.0001). Next, multivariate analysis (MANOVA) followed by univariate analysis (ANOVA) was employed to identify predictors with significant discriminatory capacity for different levels of glycemia. We show that the interplay between inflammation, hyperglycemic-induced derangements in RBC indices, and altered glucose homeostasis could be employed for prognosticating hyperglycemic outcomes. Our results then conclude a glycemic predictor with high sensitivity and specificity, employing inflammatory markers coupled with RBC indices, to predict glycemic outcomes (ROC p-value < 0.0001). Taken together, this study outlines a predictor of glycemic outcomes which could assist as a prophylactic intervention in predicting the early onset of hyperglycemia and borderline hyperglycemia.

Association of epilepsy and neurological impairments with homozygous recessive missense mutations found in the genes responsible for ganglioside biosynthesis (ST3GAL5) and calcium voltage-gated channels (CACNA1H) - insights through molecular dynamic simulations.

With over 2.2 million cases, the incidence rate of epilepsy in Pakistan is far higher than the rest of the world due primarily to the frequent, traditionally imposed cousin marriages. In the present study, comprehensive whole exome sequencing (WES) analyses of a three-generation family with four affected members presenting 'unexplained' childhood absence epilepsy (CAE), seizures and dementia, was performed in a quest to identify heritable, epilepsy-causal gene variants to better aid in carrier screening and genetic counselling. The WES data was generated, analyzed, and validated through Sanger's sequencing, molecular dynamic simulation (MDS) analysis, and molecular mechanics with generalized Born and surface area solvation (MM/GBSA) studies. Two homozygous recessive, missense mutations in ST3GAL5 (c.311A > G, p. His104Arg) and CACNA1H (c.6230G > A, p. Arg2077His) genes, earlier regarded as benign or of uncertain significance, have been identified as a potential etiology. Comparative MDS and free binding energy calculations revealed substantial structural perturbations in mutant forms of ST3GAL5 leading to decreased binding and reduced catalytic activity of the p.His104Arg and two other functional variants (p.Val74Glu and p.Arg288Ter) when compared with wild type. Our findings reinforce that WES analyses may uncover 'hidden', heritable variants and together with MDS and MM/GBSA may provide plausible clues to answer the unexplained causes of epilepsy for an effective management and better patient outcome. Further, revisit of epilepsy-associated mutational landscape in population context is imperative as the variants with 'benign' tags may turn out to be 'non-benign', when exist in combination with other benign.Communicated by Ramaswamy H. Sarma.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Trends in antimicrobial resistance amongst pathogens isolated from blood and cerebrospinal fluid cultures in Pakistan (2011-2015): A retrospective cross-sectional study.

While antimicrobial resistance (AMR) continues to be a major public health problem in Pakistan, data regarding trends of resistance among pathogenic bacteria remains scarce, with few studies presenting long-term trends in AMR. This study was therefore designed to analyze long-term AMR trends at a national level in Pakistan. We report here results of a comprehensive analysis of resistance, among pathogens isolated from blood and cerebrospinal fluid (CSF), between 2011 and 2015. Susceptibility data was obtained from a local laboratory with collection points all across Pakistan (Chughtai Laboratory). Resistance proportions to most commonly used antimicrobials were calculated for each pathogen over a period of five years. While Acinetobacter species demonstrated highest resistance rates to all tested antimicrobials, a sharp increase in carbapenem resistance was the most noticeable (50%-95%) between 2011-2015. Our results also highlight the presence of third and fourth generation cephalosporins resistance in Salmonella enterica serovar Typhi in Pakistan. Interestingly, where rise in AMR was being observed in some major invasive pathogens, decreasing resistance trends were observed in Staphylococcus aureus, against commonly used antimicrobials. Overall pathogens isolated from blood and CSF between 2011-2015, showed an increase in resistance towards commonly used antimicrobials.

CRISPR-Cas13a mediated targeting of hepatitis C virus internal-ribosomal entry site (IRES) as an effective antiviral strategy.

Hepatitis C is an inflammatory liver disease caused by the single-stranded RNA (ssRNA) hepatitis C virus (HCV). The genetic diversity of the virus and quasispecies produced during replication have resulted in viral resistance to direct-acting antivirals (DAAs) as well as impediments in vaccine development. The recent adaptation of CRISPR-Cas as an alternative antiviral approach has demonstrated degradation of viral nucleic acids in eukaryotes. In particular, the CRISPR-effector Cas13 enzyme has been shown to target ssRNA viruses effectively. In this work, we have employed Cas13a to knockdown HCV in mammalian cells. Using a computational screen, we identified several potential Cas13a target sites within highly conserved regions of the HCV internal ribosomal entry site (IRES). Our results demonstrate significant inhibition of HCV replication as well as translation in huh-7.5 cells with minimal effects on cell viability. These findings were validated using a multi-modality approach involving qRT-PCR, luciferase assay, and MTT cell viability assay. In conclusion, the CRISPR-Cas13a system efficiently targets HCV in vitro, suggesting its potential as a programmable therapeutic antiviral strategy.

An evaluation of lipid profile and pro-inflammatory cytokines as determinants of cardiovascular disease in those with diabetes: a study on a Mexican American cohort.

Sedentary life styles coupled with high-calorie diets and unhealthy social habits such as smoking, have put an ever-increasing number of people at risk of cardiovascular disorders (CVD), worldwide. A concomitant increase in the prevalence of type 2-diabetes (hyperglycemia), a risk factor for CVD, has further contributed towards escalating CVD-related mortalities. The increase in number of cases of type 2-diabetes underscores the importance of early diagnosis of cardiovascular disease in those with diabetes. In this work, we have evaluated the sensitivity and specificity of dyslipidemia and proinflammatory cytokines to be used as biomarkers for predicting the risk of CVD in those with diabetes. We hypothesize that interplay between dyslipidemia and diabetes-induced low-grade inflammation in those with type 2-diabetes increases the risk of CVD. A total of 215 participants were randomly recruited from the Cameron County Hispanic Cohort (CCHC). Of these, 99% were Mexican Americans living on Texas-Mexico border. Levels of cytokines, adipokines and lipid profile were measured. Cardiovascular disease (CVD) for this study was defined as prior diagnosis of heart attack, angina and stroke, while diabetes was defined by fasting blood glucose (FBG) of > 100 mg/dL and HbA1c of > 6.5, in accordance with American Diabetes Association (ADA) guidelines. Depending on type and distribution of data, various statistical tests were performed. Our results demonstrated higher rates of heart attack (14% vs 11.8%) and stroke (19.8% vs 10%) in those with diabetes as compared to non-diabetes. The odds of having a heart attack were eight times higher in the presence of elevated triglycerides and pro-inflammatory markers (TNFα and IL6) as compared to presence of pro-inflammatory markers only. The odds for heart attack among those with diabetes, increased by 20 fold in presence of high levels of triglycerides, TNFα, and IL6 when coupled with low levels of high-density lipid cholesterol (HDL-C). Lastly, our analysis showed that poorly controlled diabetes, characterized by HbA1c values of > 6.5 increases the odds of stroke by more than three fold. The study quantifies the role of lipid profile and pro-inflammatory markers in combination with standard risk factors towards predicting the risk of CVD in those with type 2-diabetes. The findings from the study can be directly translated for use in early diagnosis of heart disease and guiding interventions leading to a reduction in CVD-associated mortality in those with type 2-diabetes.

A Personalized Therapeutics Approach Using an In Silico Drosophila Patient Model Reveals Optimal Chemo- and Targeted Therapy Combinations for Colorectal Cancer.

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data, can aid in the development of novel personalized cancer therapeutic strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate such preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with colorectal cancer patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). We employed cell-type-specific RNA-seq gene expression data from the FlyGut-seq database to annotate and then validate these networks. Next, we developed three literature-based colorectal cancer case studies to evaluate cell fate outcomes from the model. Results obtained from analyses of the proposed DPM help: (i) elucidate cell fate evolution in colorectal tumorigenesis, (ii) validate cytotoxicity of nine FDA-approved CRC drugs, and (iii) devise optimal personalized treatment combinations. The personalized network models helped identify synergistic combinations of paclitaxel-regorafenib, paclitaxel-bortezomib, docetaxel-bortezomib, and paclitaxel-imatinib for treating different colorectal cancer patients. Follow-on therapeutic screening of six colorectal cancer patients from cBioPortal using this drug combination demonstrated a 100% increase in apoptosis and a 100% decrease in proliferation. In conclusion, this work outlines a novel roadmap for decoding colorectal tumorigenesis along with the development of personalized combinatorial therapeutics for preclinical translational studies.

Expression of cyanobacterial genes enhanced CO2 assimilation and biomass production in transgenic Arabidopsis thaliana.

BACKGROUND: Photosynthesis is a key process in plants that is compromised by the oxygenase activity of Rubisco, which leads to the production of toxic compound phosphoglycolate that is catabolized by photorespiratory pathway. Transformation of plants with photorespiratory bypasses have been shown to reduce photorespiration and enhance plant biomass. Interestingly, engineering of a single gene from such photorespiratory bypasses has also improved photosynthesis and plant productivity. Although single gene transformations may not completely reduce photorespiration, increases in plant biomass accumulation have still been observed indicating an alternative role in regulating different metabolic processes. Therefore, the current study was aimed at evaluating the underlying mechanism (s) associated with the effects of introducing a single cyanobacterial glycolate decarboxylation pathway gene on photosynthesis and plant performance. METHODS: Transgenic Arabidopsis thaliana plants (GD, HD, OX) expressing independently cyanobacterial decarboxylation pathway genes i.e., glycolate dehydrogenase, hydroxyacid dehydrogenase, and oxalate decarboxylase, respectively, were utilized. Photosynthetic, fluorescence related, and growth parameters were analyzed. Additionally, transcriptomic analysis of GD transgenic plants was also performed. RESULTS: The GD plants exhibited a significant increase (16%) in net photosynthesis rate while both HD and OX plants showed a non-significant (11%) increase as compared to wild type plants (WT). The stomatal conductance was significantly higher (24%) in GD and HD plants than the WT plants. The quantum efficiencies of photosystem II, carbon dioxide assimilation and the chlorophyll fluorescence-based photosynthetic electron transport rate were also higher than WT plants. The OX plants displayed significant reductions in the rate of photorespiration relative to gross photosynthesis and increase in the ratio of the photosynthetic electron flow attributable to carboxylation reactions over that attributable to oxygenation reactions. GD, HD and OX plants accumulated significantly higher biomass and seed weight. Soluble sugars were significantly increased in GD and HD plants, while the starch levels were higher in all transgenic plants. The transcriptomic analysis of GD plants revealed 650 up-regulated genes mainly related to photosynthesis, photorespiratory pathway, sucrose metabolism, chlorophyll biosynthesis and glutathione metabolism. CONCLUSION: This study revealed the potential of introduced cyanobacterial pathway genes to enhance photosynthetic and growth-related parameters. The upregulation of genes related to different pathways provided evidence of the underlying mechanisms involved particularly in GD plants. However, transcriptomic profiling of HD and OX plants can further help to identify other potential mechanisms involved in improved plant productivity.

A cyanobacterial photorespiratory bypass model to enhance photosynthesis by rerouting photorespiratory pathway in C3 plants.

Plants employ photosynthesis to produce sugars for supporting their growth. During photosynthesis, an enzyme Ribulose 1,5 bisphosphate carboxylase/oxygenase (Rubisco) combines its substrate Ribulose 1,5 bisphosphate (RuBP) with CO2 to produce phosphoglycerate (PGA). Alongside, Rubisco also takes up O2 and produce 2-phosphoglycolate (2-PG), a toxic compound broken down into PGA through photorespiration. Photorespiration is not only a resource-demanding process but also results in CO2 loss which affects photosynthetic efficiency in C3 plants. Here, we propose to circumvent photorespiration by adopting the cyanobacterial glycolate decarboxylation pathway into C3 plants. For that, we have integrated the cyanobacterial glycolate decarboxylation pathway into a kinetic model of C3 photosynthetic pathway to evaluate its impact on photosynthesis and photorespiration. Our results show that the cyanobacterial glycolate decarboxylation bypass model exhibits a 10% increase in net photosynthetic rate (A) in comparison with C3 model. Moreover, an increased supply of intercellular CO2 (Ci) from the bypass resulted in a 54.8% increase in PGA while reducing photorespiratory intermediates including glycolate (- 49%) and serine (- 32%). The bypass model, at default conditions, also elucidated a decline in phosphate-based metabolites including RuBP (- 61.3%). The C3 model at elevated level of inorganic phosphate (Pi), exhibited a significant change in RuBP (+ 355%) and PGA (- 98%) which is attributable to the low availability of Ci. Whereas, at elevated Pi, the bypass model exhibited an increase of 73.1% and 33.9% in PGA and RuBP, respectively. Therefore, we deduce a synergistic effect of elevation in CO2 and Pi pool on photosynthesis. We also evaluated the integrative action of CO2, Pi, and Rubisco carboxylation activity (Vcmax) on A and observed that their simultaneous increase raised A by 26%, in the bypass model. Taken together, the study potentiates engineering of cyanobacterial decarboxylation pathway in C3 plants to bypass photorespiration thereby increasing the overall efficiency of photosynthesis.

Estimation of hepatitis C prevalence in the Punjab province of Pakistan: A retrospective study on general population.

BACKGROUND: Hepatitis C virus (HCV) infections are amongst the leading public health concerns in Pakistan with a high disease burden. Despite the availability of effective antiviral treatments in the country the disease burden in general population has not lowered. This could be attributed to the asymptomatic nature of this infection that results in lack of diagnosis until the late symptomatic stage. To better estimate and map HCV infections in the country a population-based analysis is necessary for an effective control of the infection. METHODS: Serologic samples of ~66,000 participants from all major cities of the Punjab province were tested for anti-HCV antibodies. The antibody-based seroprevalence was associated with socio-demographic variables including geographical region, age, gender and sex, and occupation. RESULTS: Overall serological response to HCV surface antigens was observed in over 17% of the population. Two of the districts were identified with significantly high prevalence in general population. Analysis by occupation showed significantly high prevalence in farmers (over 40%) followed by jobless and retired individuals, laborers and transporters. A significant difference in seroprevalence was observed in different age groups amongst sex and genders (male, female and transgender) with highest response in individuals of over 40 years of age. Moreover, most of the tested IDUs showed positive response for anti-HCV antibody. CONCLUSION: This study represents a retrospective analysis of HCV infections in general population of the most populated province of Pakistan to identify socio-demographic groups at higher risk. Two geographical regions, Faisalabad and Okara districts, and an occupational group, farmers, were identified with significantly high HCV seroprevalence. These socio-demographic groups are the potential focused groups for follow-up studies on factors contributing to the high HCV prevalence in these groups towards orchestrating effective prevention, control and treatment.

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data.

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.