Identification and classification of antiviral defence systems in bacteria and archaea with PADLOC reveals new system types

To provide protection against viral infection and limit the uptake of mobile genetic elements, bacteria and archaea have evolved many diverse defence systems. The discovery and application of CRISPR-Cas adaptive immune systems has spurred recent interest in the identification and classification of new types of defence systems. Many new defence systems have recently been reported but there is a lack of accessible tools available to identify homologs of these systems in different genomes. Here, we report the Prokaryotic Antiviral Defence LOCator (PADLOC), a flexible and scalable open-source tool for defence system identification. With PADLOC, defence system genes are identified using HMM-based homologue searches, followed by validation of system completeness using gene presence/absence and synteny criteria specified by customisable system classifications. We show that PADLOC identifies defence systems with high accuracy and sensitivity. Our modular approach to organising the HMMs and system classifications allows additional defence systems to be easily integrated into the PADLOC database. To demonstrate application of PADLOC to biological questions, we used PADLOC to identify six new subtypes of known defence systems and a putative novel defence system comprised of a helicase, methylase and ATPase. PADLOC is available as a standalone package (https://github.com/padlocbio/padloc) and as a webserver (https://padloc.otago.ac.nz).     

Systems analysis of immune responses to attenuated P. falciparum malaria sporozoite vaccination reveals excessive inflammatory signatures correlating with impaired immunity

Immunization with radiation-attenuated sporozoites (RAS) can confer sterilizing protection against malaria, although the mechanisms behind this protection are incompletely understood. We performed a systems biology analysis of samples from the Immunization by Mosquito with Radiation Attenuated Sporozoites (IMRAS) trial, which comprised P. falciparum RAS-immunized (PfRAS), malaria-naive participants whose protection from malaria infection was subsequently assessed by controlled human malaria infection (CHMI). Blood samples collected after initial PfRAS immunization were analyzed to compare immune responses between protected and non-protected volunteers leveraging integrative analysis of whole blood RNA-seq, high parameter flow cytometry, and single cell CITEseq of PBMCs. This analysis revealed differences in early innate immune responses indicating divergent paths associated with protection. In particular, elevated levels of inflammatory responses early after the initial immunization were detrimental for the development of protective adaptive immunity. Specifically, non-classical monocytes and early type I interferon responses induced within 1 day of PfRAS vaccination correlated with impaired immunity. Non-protected individuals also showed an increase in Th2 polarized T cell responses whereas we observed a trend towards increased Th1 and T-bet+ CD8 T cell responses in protected individuals. Temporal differences in genes associated with natural killer cells suggest an important role in immune regulation by these cells. These findings give insight into the immune responses that confer protection against malaria and may guide further malaria vaccine development.Trial registration: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01994525","term_id":"NCT01994525"}}NCT01994525.     

lociNGS: A Lightweight Alternative for Assessing Suitability of Next-Generation Loci for Evolutionary Analysis

Genomic enrichment methods and next-generation sequencing produce uneven coverage for the portions of the genome (the loci) they target; this information is essential for ascertaining the suitability of each locus for further analysis. lociNGS is a user-friendly accessory program that takes multi-FASTA formatted loci, next-generation sequence alignments and demographic data as input and collates, displays and outputs information about the data. Summary information includes the parameters coverage per locus, coverage per individual and number of polymorphic sites, among others. The program can output the raw sequences used to call loci from next-generation sequencing data. lociNGS also reformats subsets of loci in three commonly used formats for multi-locus phylogeographic and population genetics analyses – NEXUS, IMa2 and Migrate. lociNGS is available at https://github.com/SHird/lociNGS and is dependent on installation of MongoDB (freely available at http://www.mongodb.org/downloads). lociNGS is written in Python and is supported on MacOSX and Unix; it is distributed under a GNU General Public License.     

A Real-Time All-Atom Structural Search Engine for Proteins

Protein designers use a wide variety of software tools for de novo design, yet their repertoire still lacks a fast and interactive all-atom search engine. To solve this, we have built the Suns program: a real-time, atomic search engine integrated into the PyMOL molecular visualization system. Users build atomic-level structural search queries within PyMOL and receive a stream of search results aligned to their query within a few seconds. This instant feedback cycle enables a new “designability”-inspired approach to protein design where the designer searches for and interactively incorporates native-like fragments from proven protein structures. We demonstrate the use of Suns to interactively build protein motifs, tertiary interactions, and to identify scaffolds compatible with hot-spot residues. The official web site and installer are located at http://www.degradolab.org/suns/ and the source code is hosted at https://github.com/godotgildor/Suns (PyMOL plugin, BSD license), https://github.com/Gabriel439/suns-cmd (command line client, BSD license), and https://github.com/Gabriel439/suns-search (search engine server, GPLv2 license).

This is a PLOS Computational Biology Software Article

     

Germinal Center Kinases SmKIN3 and SmKIN24 Are Associated with the Sordaria macrospora Striatin-Interacting Phosphatase and Kinase (STRIPAK) Complex

The striatin-interacting phosphatase and kinase (STRIPAK) complex is composed of striatin, protein phosphatase PP2A and protein kinases that regulate development in animals and fungi. In the filamentous ascomycete Sordaria macrospora, it is required for fruiting-body development and cell fusion. Here, we report on the presence and function of STRIPAK-associated kinases in ascomycetes. Using the mammalian germinal center kinases (GCKs) MST4, STK24, STK25 and MINK1 as query, we identified the two putative homologs SmKIN3 and SmKIN24 in S. macrospora. A BLASTP search revealed that both kinases are conserved among filamentous ascomycetes. The physical interaction of the striatin homolog PRO11 with SmKIN3 and SmKIN24 were verified by yeast two-hybrid (Y2H) interaction studies and for SmKIN3 by co-Immunoprecipitation (co-IP). In vivo localization found that both kinases were present at the septa and deletion of both Smkin3 and Smkin24 led to abnormal septum distribution. While deletion of Smkin3 caused larger distances between adjacent septa and increased aerial hyphae, deletion of Smkin24 led to closer spacing of septa and to sterility. Although phenotypically distinct, both kinases appear to function independently because the double-knockout strain ΔSmkin3/ΔSmkin24 displayed the combined phenotypes of each single-deletion strain.     

UniqTag: Content-Derived Unique and Stable Identifiers for Gene Annotation

When working on an ongoing genome sequencing and assembly project, it is rather inconvenient when gene identifiers change from one build of the assembly to the next. The gene labelling system described here, UniqTag, addresses this common challenge. UniqTag assigns a unique identifier to each gene that is a representative k-mer, a string of length k, selected from the sequence of that gene. Unlike serial numbers, these identifiers are stable between different assemblies and annotations of the same data without requiring that previous annotations be lifted over by sequence alignment. We assign UniqTag identifiers to ten builds of the Ensembl human genome spanning eight years to demonstrate this stability. The implementation of UniqTag in Ruby and an R package are available at https://github.com/sjackman/uniqtag sjackman/uniqtag. The R package is also available from CRAN: install.packages ("uniqtag"). Supplementary material and code to reproduce it is available at https://github.com/sjackman/uniqtag-paper.     

SWALO: scaffolding with assembly likelihood optimization

Scaffolding, i.e. ordering and orienting contigs is an important step in genome assembly. We present a method for scaffolding using second generation sequencing reads based on likelihoods of genome assemblies. A generative model for sequencing is used to obtain maximum likelihood estimates of gaps between contigs and to estimate whether linking contigs into scaffolds would lead to an increase in the likelihood of the assembly. We then link contigs if they can be unambiguously joined or if the corresponding increase in likelihood is substantially greater than that of other possible joins of those contigs. The method is implemented in a tool called Swalo with approximations to make it efficient and applicable to large datasets. Analysis on real and simulated datasets reveals that it consistently makes more or similar number of correct joins as other scaffolders while linking very few contigs incorrectly, thus outperforming other scaffolders and demonstrating that substantial improvement in genome assembly may be achieved through the use of statistical models. Swalo is freely available for download at https://atifrahman.github.io/SWALO/.     

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Since the read lengths of high throughput sequencing (HTS) technologies are short, de novo assembly which plays significant roles in many applications remains a great challenge. Most of the state-of-the-art approaches base on de Bruijn graph strategy and overlap-layout strategy. However, these approaches which depend on k-mers or read overlaps do not fully utilize information of paired-end and single-end reads when resolving branches. Since they treat all single-end reads with overlapped length larger than a fix threshold equally, they fail to use the more confident long overlapped reads for assembling and mix up with the relative short overlapped reads. Moreover, these approaches have not been special designed for handling tandem repeats (repeats occur adjacently in the genome) and they usually break down the contigs near the tandem repeats. We present PERGA (Paired-End Reads Guided Assembler), a novel sequence-reads-guided de novo assembly approach, which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds using paired-end reads and different read overlap size ranging from O _max to O _min to resolve the gaps and branches. By constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. When the correct extension cannot be determined, PERGA will try to extend the contig by all feasible extensions and determine the correct extension by using look-ahead approach. Many difficult-resolved branches are due to tandem repeats which are close in the genome. PERGA detects such different copies of the repeats to resolve the branches to make the extension much longer and more accurate. We evaluated PERGA on both Illumina real and simulated datasets ranging from small bacterial genomes to large human chromosome, and it constructed longer and more accurate contigs and scaffolds than other state-of-the-art assemblers. PERGA can be freely downloaded at https://github.com/hitbio/PERGA.     

HeurAA: Accurate and Fast Detection of Genetic Variations with a Novel Heuristic Amplicon Aligner Program for Next Generation Sequencing

Next generation sequencing (NGS) of PCR amplicons is a standard approach to detect genetic variations in personalized medicine such as cancer diagnostics. Computer programs used in the NGS community often miss insertions and deletions (indels) that constitute a large part of known human mutations. We have developed HeurAA, an open source, heuristic amplicon aligner program. We tested the program on simulated datasets as well as experimental data from multiplex sequencing of 40 amplicons in 12 oncogenes collected on a 454 Genome Sequencer from lung cancer cell lines. We found that HeurAA can accurately detect all indels, and is more than an order of magnitude faster than previous programs. HeurAA can compare reads and reference sequences up to several thousand base pairs in length, and it can evaluate data from complex mixtures containing reads of different gene-segments from different samples. HeurAA is written in C and Perl for Linux operating systems, the code and the documentation are available for research applications at http://sourceforge.net/projects/heuraa/     

Accelerating the Original Profile Kernel

One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.     

A Fungal Sarcolemmal Membrane-Associated Protein (SLMAP) Homolog Plays a Fundamental Role in Development and Localizes to the Nuclear Envelope,Endoplasmic Reticulum,and Mitochondria

Sarcolemmal membrane-associated protein (SLMAP) is a tail-anchored protein involved in fundamental cellular processes, such as myoblast fusion, cell cycle progression, and chromosomal inheritance. Further, SLMAP misexpression is associated with endothelial dysfunctions in diabetes and cancer. SLMAP is part of the conserved striatin-interacting phosphatase and kinase (STRIPAK) complex required for specific signaling pathways in yeasts, filamentous fungi, insects, and mammals. In filamentous fungi, STRIPAK was initially discovered in Sordaria macrospora, a model system for fungal differentiation. Here, we functionally characterize the STRIPAK subunit PRO45, a homolog of human SLMAP. We show that PRO45 is required for sexual propagation and cell-to-cell fusion and that its forkhead-associated (FHA) domain is essential for these processes. Protein-protein interaction studies revealed that PRO45 binds to STRIPAK subunits PRO11 and SmMOB3, which are also required for sexual propagation. Superresolution structured-illumination microscopy (SIM) further established that PRO45 localizes to the nuclear envelope, endoplasmic reticulum, and mitochondria. SIM also showed that localization to the nuclear envelope requires STRIPAK subunits PRO11 and PRO22, whereas for mitochondria it does not. Taken together, our study provides important insights into fundamental roles of the fungal SLMAP homolog PRO45 and suggests STRIPAK-related and STRIPAK-unrelated functions.     

BEACON: automated tool for Bacterial GEnome Annotation ComparisON

Background

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).

Results

The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.

Conclusions

We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users.     

Clinical Profiling of BCL-2 Family Members in the Setting of BRAF Inhibition Offers a Rationale for Targeting De Novo Resistance Using BH3 Mimetics

While response rates to BRAF inhibitiors (BRAFi) are high, disease progression emerges quickly. One strategy to delay the onset of resistance is to target anti-apoptotic proteins such as BCL-2, known to be associated with a poor prognosis. We analyzed BCL-2 family member expression levels of 34 samples from 17 patients collected before and 10 to 14 days after treatment initiation with either vemurafenib or dabrafenib/trametinib combination. The observed changes in mRNA and protein levels with BRAFi treatment led us to hypothesize that combining BRAFi with a BCL-2 inhibitor (the BH3-mimetic navitoclax) would improve outcome. We tested this hypothesis in cell lines and in mice. Pretreatment mRNA levels of BCL-2 negatively correlated with maximal tumor regression. Early increases in mRNA levels were seen in BIM, BCL-XL, BID and BCL2-W, as were decreases in MCL-1 and BCL2A. No significant changes were observed with BCL-2. Using reverse phase protein array (RPPA), significant increases in protein levels were found in BIM and BID. No changes in mRNA or protein correlated with response. Concurrent BRAF (PLX4720) and BCL2 (navitoclax) inhibition synergistically reduced viability in BRAF mutant cell lines and correlated with down-modulation of MCL-1 and BIM induction after PLX4720 treatment. In xenograft models, navitoclax enhanced the efficacy of PLX4720. The combination of a selective BRAF inhibitor with a BH3-mimetic promises to be an important therapeutic strategy capable of enhancing the clinical efficacy of BRAF inhibition in many patients that might otherwise succumb quickly to de novo resistance. Trial Registrations: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01006980","term_id":"NCT01006980"}}NCT01006980;ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01107418","term_id":"NCT01107418"}}NCT01107418; ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01264380","term_id":"NCT01264380"}}NCT01264380; ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01248936","term_id":"NCT01248936"}}NCT01248936; ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00949702","term_id":"NCT00949702"}}NCT00949702; ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01072175","term_id":"NCT01072175"}}NCT01072175     

FSH: fast spaced seed hashing exploiting adjacent hashes

Background

Patterns with wildcards in specified positions, namely spaced seeds, are increasingly used instead of k-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. While the hashing of k-mers can be rapidly computed by exploiting the large overlap between consecutive k-mers, spaced seeds hashing is usually computed from scratch for each position in the input sequence, thus resulting in slower processing.

Results

The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6\(\times\) to 5.3\(\times\), depending on the structure of the spaced seed.

Conclusions

Spaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient.

Availability

The software FSH is freely available for academic use at: https://bitbucket.org/samu661/fsh/overview.