Application of multiplexed ion mobility spectrometry towards the identification of host protein signatures of treatment effect in pulmonary tuberculosis.

RationaleThe monitoring of TB treatments in clinical practice and clinical trials relies on traditional sputum-based culture status indicators at specific time points. Accurate, predictive, blood-based protein markers would provide a simpler and more informative view of patient health and response to treatment.ObjectiveWe utilized sensitive, high throughput multiplexed ion mobility-mass spectrometry (IM-MS) to characterize the serum proteome of TB patients at the start of and at 8 weeks of rifamycin-based treatment. We sought to identify treatment specific signatures within patients as well as correlate the proteome signatures to various clinical markers of treatment efficacy.MethodsSerum samples were collected from 289 subjects enrolled in CDC TB Trials Consortium Study 29 at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). Serum proteins were immunoaffinity-depleted of high abundant components, digested to peptides and analyzed for data acquisition utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS). Linear mixed models were utilized to identify serum protein changes in the host response to antibiotic treatment as well as correlations with culture status end points.ResultsA total of 10,137 peptides corresponding to 872 proteins were identified, quantified, and used for statistical analysis across the longitudinal patient cohort. In response to TB treatment, 244 proteins were significantly altered. Pathway/network comparisons helped visualize the interconnected proteins, identifying up regulated (lipid transport, coagulation cascade, endopeptidase activity) and down regulated (acute phase) processes and pathways in addition to other cross regulated networks (inflammation, cell adhesion, extracellular matrix). Detection of possible lung injury serum proteins such as HPSE, significantly downregulated upon treatment. Analyses of microbiologic data over time identified a core set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2) which change in response to treatment and also strongly correlate with culture status. A similar set of proteins at baseline were found to be predictive of week 6 and 8 culture status.ConclusionA comprehensive host serum protein dataset reflective of TB treatment effect is defined. A repeating set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2, among others) were found to change significantly in response to treatment, to strongly correlate with culture status, and at baseline to be predictive of future culture conversion. If validated in cohorts with long term follow-up to capture failure and relapse of TB, these protein markers could be developed for monitoring of treatment in clinical trials and in patient care

Gene Set Coregulated by the \u3ci\u3eSaccharomyces cerevisiae\u3c/i\u3e Nonsense-Mediated mRNA Decay Pathway

The nonsense-mediated mRNA decay (NMD) pathway has historically been thought of as an RNA surveillance system that degrades mRNAs with premature translation termination codons, but the NMD pathway of Saccharomyces cerevisiae has a second role regulating the decay of some wild-type mRNAs. In S. cerevisiae, a significant number of wild-type mRNAs are affected when NMD is inactivated. These mRNAs are either wild-type NMD substrates or mRNAs whose abundance increases as an indirect consequence of NMD. A current challenge is to sort the mRNAs that accumulate when NMD is inactivated into direct and indirect targets. We have developed a bioinformatics-based approach to address this challenge. Our approach involves using existing genomic and function databases to identify transcription factors whose mRNAs are elevated in NMD-deficient cells and the genes that they regulate. Using this strategy, we have investigated a coregulated set of genes. We have shown that NMD regulates accumulation of ADR1 and GAL4 mRNAs, which encode transcription activators, and that Adr1 is probably a transcription activator of ATS1. This regulation is physiologically significant because overexpression of ADR1 causes a respiratory defect that mimics the defect seen in strains with an inactive NMD pathway. This strategy is significant because it allows us to classify the genes regulated by NMD into functionally related sets, an important step toward understanding the role NMD plays in the normal functioning of yeast cells

Unified feature association networks through integration of transcriptomic and proteomic data

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different–omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease

Functional comparison of blood-stage Plasmodium falciparum malaria vaccine candidate antigens

The malaria genome encodes over 5,000 proteins and many of these have also been proposed to be potential vaccine candidates, although few of these have been tested clinically. RH5 is one of the leading blood-stage Plasmodium falciparum malaria vaccine antigens and Phase I/II clinical trials of vaccines containing this antigen are currently underway. Its likely mechanism of action is to elicit antibodies that can neutralize merozoites by blocking their invasion of red blood cells (RBC). However, many other antigens could also elicit neutralizing antibodies against the merozoite, and most of these have never been compared directly to RH5. The objective of this study was to compare a range of blood-stage antigens to RH5, to identify any antigens that outperform or synergize with anti-RH5 antibodies. We selected 55 gene products, covering 15 candidate antigens that have been described in the literature and 40 genes selected on the basis of bioinformatics functional prediction. We were able to make 20 protein-in-adjuvant vaccines from the original selection. Of these, S-antigen and CyRPA robustly elicited antibodies with neutralizing properties. Anti-CyRPA IgG generally showed additive GIA with anti-RH5 IgG, although high levels of anti-CyRPA-specific rabbit polyclonal IgG were required to achieve 50% GIA. Our data suggest that further vaccine antigen screening efforts are required to identify a second merozoite target with similar antibody-susceptibility to RH5

Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples

We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network.  It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented.  For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations.  We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent.  We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines.  Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website

An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples.

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination

Gene Set Coregulated by the Saccharomyces cerevisiae Nonsense-Mediated mRNA Decay Pathway

The nonsense-mediated mRNA decay (NMD) pathway has historically been thought of as an RNA surveillance system that degrades mRNAs with premature translation termination codons, but the NMD pathway of Saccharomyces cerevisiae has a second role regulating the decay of some wild-type mRNAs. In S. cerevisiae, a significant number of wild-type mRNAs are affected when NMD is inactivated. These mRNAs are either wild-type NMD substrates or mRNAs whose abundance increases as an indirect consequence of NMD. A current challenge is to sort the mRNAs that accumulate when NMD is inactivated into direct and indirect targets. We have developed a bioinformatics-based approach to address this challenge. Our approach involves using existing genomic and function databases to identify transcription factors whose mRNAs are elevated in NMD-deficient cells and the genes that they regulate. Using this strategy, we have investigated a coregulated set of genes. We have shown that NMD regulates accumulation of ADR1 and GAL4 mRNAs, which encode transcription activators, and that Adr1 is probably a transcription activator of ATS1. This regulation is physiologically significant because overexpression of ADR1 causes a respiratory defect that mimics the defect seen in strains with an inactive NMD pathway. This strategy is significant because it allows us to classify the genes regulated by NMD into functionally related sets, an important step toward understanding the role NMD plays in the normal functioning of yeast cells

Unified feature association networks through integration of transcriptomic and proteomic data.

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease

Six Genes Are Preferentially Transcribed by the Circulating and Sequestered Forms of Plasmodium falciparum Parasites That Infect Pregnant Women▿ †

In areas of stable malaria transmission, susceptibility to Plasmodium falciparum malaria increases during first pregnancy. Women become resistant to pregnancy malaria over successive pregnancies as they acquire antibodies against the parasite forms that sequester in the placenta, suggesting that a vaccine is feasible. Placental parasites are antigenically distinct and bind receptors, like chondroitin sulfate A (CSA), that are not commonly bound by other parasites. We used whole-genome-expression analysis to find transcripts that distinguish parasites of pregnant women from other parasites and employed a novel approach to define and adjust for cell cycle timing of parasites. Transcription of six genes was substantially higher in both placental parasites and peripheral parasites from pregnant women, and each gene encodes a protein with a putative export sequence and/or transmembrane domain. This cohort of genes includes var2csa, a member of the variant PfEMP1 gene family previously implicated in pregnancy malaria, as well as five conserved genes of unknown functions. Women in East Africa acquire antibodies over successive pregnancies against a protein encoded by one of these genes, PFD1140w, and this protein shows seroreactivity similar to that of VAR2CSA domains. These findings suggest that a suite of genes may be important for the genesis of the placental binding phenotype of P. falciparum and may provide novel targets for therapeutic intervention