Comprehensive viral oligonucleotide probe design using conserved protein regions

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.

Design of genomic signatures for pathogen identification and characterization

Once the genome of a microbial organism has been sequenced, it becomes possible to utilize portions of the genome, known as “signatures” to identify when that organism is present in a complex clinical or environmental sample. Genomic signatures can be at multiple levels of resolution depending on the questions being asked. (“Is this white powder anthrax?”; “Does this white powder match any of the anthrax samples taken from every laboratory in the United States that possesses anthrax?”) Multiple technologies exist to turn abstract genomic signatures into assays that can interrogate complex samples with varying degrees of speed, sensitivity, specificity, and cost. The recent flood of microbial genomic data has complicated the task of designing genomic signatures.

Capturing sequence diversity in metagenomes with comprehensive and scalable probe design

Metagenomic sequencing has the potential to transform microbial detection and characterization, but new tools are needed to improve its sensitivity. Here we present CATCH, a computational method to enhance nucleic acid capture for enrichment of diverse microbial taxa. CATCH designs optimal probe sets, with a specified number of oligonucleotides, that achieve full coverage of, and scale well with, known sequence diversity. We focus on applying CATCH to capture viral genomes in complex metagenomic samples. We design, synthesize, and validate multiple probe sets, including one that targets the whole genomes of the 356 viral species known to infect humans. Capture with these probe sets enriches unique viral content on average 18-fold, allowing us to assemble genomes that could not be recovered without enrichment, and accurately preserves within-sample diversity. We also use these probe sets to recover genomes from the 2018 Lassa fever outbreak in Nigeria and to improve detection of uncharacterized viral infections in human and mosquito samples. The results demonstrate that CATCH enables more sensitive and cost-effective metagenomic sequencing.

Bioinformatics and Microarray-Based Technologies to Viral Genome Sequence Analysis

Identification of microbial pathogen is an important event which lead to diagnosis, treatment, and control of infections produce by them. The high-throughput technology like microarray and new-generation sequencing machine are able to generate huge amount of nucleotide sequences of viral and bacterial genome of both known and unknown pathogens. Few years ago it was the DNA microarrays which had great potential to screen all the known pathogens and yet to be identified pathogen simultaneously. But after the development of a new generation sequencing, technologies and advance computational approach researchers are looking forward for a complete understanding of microbes and host interactions. The powerful sequencing platform is rapidly transforming the landscape of microbial identification and characterization. As bioinformatics analysis tools and databases are easily available to researchers, the enormous amount of data generated can be meaningfully handled for better understanding of the microbial world. Here in this chapter, we present commentary on how the computational method incorporated with sequencing technique made easy for microbial detection and characterization.

In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: towards accurate stroma targeting therapy assessment

Background

The current state of the art for measuring stromal response to targeted therapy requires burdensome and rate limiting quantitative histology. Transcriptome measures are increasingly affordable and provide an opportunity for developing a stromal versus cancer ratio in xenograft models. In these models, human cancer cells are transplanted into mouse host tissues (stroma) and together coevolve into a tumour microenvironment. However, profiling the mouse or human component separately remains problematic. Indeed, laser capture microdissection is labour intensive. Moreover, gene expression using commercial microarrays introduces significant and underreported cross-species hybridization errors that are commonly overlooked by biologists.

Method

We developed a customized dual-species array, H&M array, and performed cross-species and species-specific hybridization measurements. We validated a new methodology for establishing the stroma vs cancer ratio using transcriptomic data.

Results

In the biological validation of the H&M array, cross-species hybridization of human and mouse probes was significantly reduced (4.5 and 9.4 fold reduction, respectively; p < 2x10^-16 for both, Mann-Whitney test). We confirmed the capability of the H&M array to determine the stromal to cancer cells ratio based on the estimation of cellularity index of mouse/human mRNA content in vitro. This new metrics enable to investigate more efficiently the stroma-cancer cell interactions (e.g. cellularity) bypassing labour intensive requirement and biases of laser capture microdissection.

Conclusion

These results provide the initial evidence of improved and cost-efficient analytics for the investigation of cancer cell microenvironment, using species-specificity arrays specifically designed for xenografts models.

Detection of dengue group viruses by fluorescence in situ hybridization

Background

Dengue fever (DF) and dengue hemorrhagic fever (DHF) represent a global challenge in public health. It is estimated that 50 to 100 million infections occur each year causing approximately 20,000 deaths that are usually linked to severe cases like DHF and dengue shock syndrome. The causative agent of DF is dengue virus (genus Flavivirus) that comprises four distinct serotypes (DENV-1 to DENV-4). Fluorescence in situ hybridization (FISH) has been used successfully to detect pathogenic agents, but has not been implemented in detecting DENV. To improve our understanding of DENV infection and dissemination in host tissues, we designed specific probes to detect DENV in FISH assays.

Methods

Oligonucleotide probes were designed to hybridize with RNA from the broadest range of DENV isolates belonging to the four serotypes, but not to the closest Flavivirus genomes. Three probes that fit the criteria defined for FISH experiments were selected, targeting both coding and non-coding regions of the DENV genome. These probes were tested in FISH assays against the dengue vector Aedes albopictus (Diptera: Culicidae). The FISH experiments were led in vitro using the C6/36 cell line, and in vivo against dissected salivary glands, with epifluorescence and confocal microscopy.

Results

The three 60-nt oligonucleotides probes DENV-Probe A, B and C cover a broad range of DENV isolates from the four serotypes. When the three probes were used together, specific fluorescent signals were observed in C6/36 infected with each DENV serotypes. No signal was detected in either cells infected with close Flavivirus members West Nile virus or yellow fever virus. The same protocol was used on salivary glands of Ae. albopictus fed with a DENV-2 infectious blood-meal which showed positive signals in the lateral lobes of infected samples, with no significant signal in uninfected mosquitoes.

Conclusion

Based on the FISH technique, we propose a way to design and use oligonucleotide probes to detect arboviruses. Results showed that this method was successfully implemented to specifically detect DENV in a mosquito cell line, as well as in mosquito salivary glands for the DENV-2 serotype. In addition, we emphasize that FISH could be an alternative method to detect arboviruses in host tissues, also offering to circumvent the discontinuity of antibodies used in immunofluorescent assays.

Universal oligonucleotide microarray for sub-typing of Influenza A virus

A universal microchip was developed for genotyping Influenza A viruses. It contains two sets of oligonucleotide probes allowing viruses to be classified by the subtypes of hemagglutinin (H1-H13, H15, H16) and neuraminidase (N1-N9). Additional sets of probes are used to detect H1N1 swine influenza viruses. Selection of probes was done in two steps. Initially, amino acid sequences specific to each subtype were identified, and then the most specific and representative oligonucleotide probes were selected. Overall, between 19 and 24 probes were used to identify each subtype of hemagglutinin (HA) and neuraminidase (NA). Genotyping included preparation of fluorescently labeled PCR amplicons of influenza virus cDNA and their hybridization to microarrays of specific oligonucleotide probes. Out of 40 samples tested, 36 unambiguously identified HA and NA subtypes of Influenza A virus.

[Oligonucleotide microarray for subtyping of influenza virus A neuraminidase]

Microarray for influenza A neuraminidase subtyping was presented. Selection of oligoprobes proceeded in two steps. First step included selection of peptides specific for each subtype of neuraminidase. At the second step oligoprobes were calculated using found peptides structures with the subsequent additional selection of the most specific and representative probes. From 19 to 24 probes were used for determination of each subtype of neuraminidase. Microchip testing for 19 samples with the most widespread types (N1 and N2) specifies in unequivocal definition 18 of them and only one isolate has not been identified.

A microbial detection array (MDA) for viral and bacterial detection

Background

Identifying the bacteria and viruses present in a complex sample is useful in disease diagnostics, product safety, environmental characterization, and research. Array-based methods have proven utility to detect in a single assay at a reasonable cost any microbe from the thousands that have been sequenced.

Methods

We designed a pan-Microbial Detection Array (MDA) to detect all known viruses (including phages), bacteria and plasmids and developed a novel statistical analysis method to identify mixtures of organisms from complex samples hybridized to the array. The array has broader coverage of bacterial and viral targets and is based on more recent sequence data and more probes per target than other microbial detection/discovery arrays in the literature. Family-specific probes were selected for all sequenced viral and bacterial complete genomes, segments, and plasmids. Probes were designed to tolerate some sequence variation to enable detection of divergent species with homology to sequenced organisms, and to have no significant matches to the human genome sequence.

Results

In blinded testing on spiked samples with single or multiple viruses, the MDA was able to correctly identify species or strains. In clinical fecal, serum, and respiratory samples, the MDA was able to detect and characterize multiple viruses, phage, and bacteria in a sample to the family and species level, as confirmed by PCR.

Conclusions

The MDA can be used to identify the suite of viruses and bacteria present in complex samples.

Stromal microenvironment processes unveiled by biological component analysis of gene expression in xenograft tumor models

Background

Mouse xenograft models, in which human cancer cells are implanted in immune-suppressed mice, have been popular for studying the mechanisms of novel therapeutic targets, tumor progression and metastasis. We hypothesized that we could exploit the interspecies genetic differences in these experiments. Our purpose is to elucidate stromal microenvironment signals from probes on human arrays unintentionally cross-hybridizing with mouse homologous genes in xenograft tumor models.

Results

By identifying cross-species hybridizing probes from sequence alignment and cross-species hybridization experiment for the human whole-genome arrays, deregulated stromal genes can be identified and then their biological significance were predicted from enrichment studies. Comparing these results with those found by the laser capture microdissection of stromal cells from tumor specimens resulted in the discovery of significantly enriched stromal biological processes.

Conclusions

Using this method, in addition to their primary endpoints, researchers can leverage xenograft experiments to better characterize the tumor microenvironment without additional costs. The Xhyb probes and R script are available at http://www.lussierlab.org/publications/Stroma

Nonparametric methods for the analysis of single-color pathogen microarrays

BACKGROUND

The analysis of oligonucleotide microarray data in pathogen surveillance and discovery is a challenging task. Target template concentration, nucleic acid integrity, and host nucleic acid composition can each have a profound effect on signal distribution. Exploratory analysis of fluorescent signal distribution in clinical samples has revealed deviations from normality, suggesting that distribution-free approaches should be applied.

RESULTS

Positive predictive value and false positive rates were examined to assess the utility of three well-established nonparametric methods for the analysis of viral array hybridization data: (1) Mann-Whitney U, (2) the Spearman correlation coefficient and (3) the chi-square test. Of the three tests, the chi-square proved most useful.

CONCLUSIONS

The acceptance of microarray use for routine clinical diagnostics will require that the technology be accompanied by simple yet reliable analytic methods. We report that our implementation of the chi-square test yielded a combination of low false positive rates and a high degree of predictive accuracy.

Metagenomics for the discovery of novel human viruses

Modern laboratory techniques for the detection of novel human viruses are greatly needed as physicians and epidemiologists increasingly deal with infectious diseases caused by new or previously unrecognized pathogens. There are many clinical syndromes in which viruses are suspected to play a role, but for which traditional microbiology techniques routinely fail in uncovering the etiologic agent. In addition, new viruses continue to challenge the human population owing to the encroachment of human settlements into animal and livestock habitats, globalization, climate change, growing numbers of immunocompromised people and bioterrorism. Metagenomics-based tools, such as microarrays and high-throughput sequencing are ideal for responding to these challenges. Pan-viral microarrays, containing representative sequences from all known viruses, have been used to detect novel and distantly-related variants of known viruses. Sequencing-based methods have also been successfully employed to detect novel viruses and have the potential to detect the full spectrum of viruses, including those present in low numbers.

Z proteins of New World arenaviruses bind RIG-I and interfere with type I interferon induction

The retinoic acid-inducible gene I product (RIG-I) is a cellular sensor of RNA virus infection that regulates the cellular beta interferon (IFN-beta) response. The nucleoproteins (NP) of arenaviruses are reported to antagonize the IFN response by inhibiting interferon regulatory factor 3 (IRF-3). Here, we demonstrate that the Z proteins of four New World (NW) arenaviruses, Guanarito virus (GTOV), Junin virus (JUNV), Machupo virus (MAVC), and Sabia virus (SABV), bind to RIG-I, resulting in downregulation of the IFN-beta response. We show that expression of the four NW arenavirus Z proteins inhibits IFN-beta mRNA induction in A549 cells in response to RNA bearing 5' phosphates (5'pppRNA). NW arenavirus Z proteins interact with RIG-I in coimmunoprecipitation studies and colocalize with RIG-I. Furthermore, expression of Z proteins interferes with the interaction between RIG-I and MAVS. Z expression also impedes the nuclear factor kappa light chain enhancer of activated B cells (NF-kappaB) and IRF-3 activation. Our results indicate that NW arenavirus Z proteins, but not Z protein of the Old World (OW) arenavirus lymphocytic choriomeningitis virus (LCMV) or Lassa virus, bind to RIG-I and inhibit downstream activation of the RIG-I signaling pathway, preventing the transcriptional induction of IFN-beta.

Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays

Background

To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging.

Methods

In this study, we conceived an automated method based on the hypergeometric distribution for identifying pathogens in multiplexed arrays and compared it to five other methods. We evaluated these metrics: 1) accurate prediction, whether the top ranked prediction(s) match the real virus(es); 2) four accuracy scores.

Results

Though accurate prediction and high specificity and sensitivity can be achieved with several methods, the method based on hypergeometric distribution provides a significant advantage in term of positive predicting value with two to sixty folds the positive predicting values of other methods.

Conclusion

The proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.

Real-time molecular methods to detect infectious viruses

Waterborne transmitted viruses pose a public health threat due to their stability in aquatic environment and the easy transmission with high morbidity rates at low infectious doses. Two major challenge of virus analysis include a lack of adequate information in infectivity and the inability to cultivate certain epidemiologically important viruses in vitro. The use of fluorescent probes in conjunction with fluorescence microscopy allows us to reveal dynamic interactions of the viruses with different cellular structures in living cells that are impossible to detect by immunological or PCR-based experiments. Real-time viral detection in vivo provides sufficient information regarding multiple steps in infection process at molecular level, which will be valuable for the prevention and control of viral infection.