LeishDB: a database of coding gene annotation and non-coding RNAs in Leishmania braziliensis

LeishMANIAdb: a comparative resource for Leishmania proteins

Leishmaniasis is a detrimental disease causing serious changes in quality of life and some forms can lead to death. The disease is spread by the parasite Leishmania transmitted by sandfly vectors and their primary hosts are vertebrates including humans. The pathogen penetrates host cells and secretes proteins (the secretome) to repurpose cells for pathogen growth and to alter cell signaling via host-pathogen protein-protein interactions). Here, we present LeishMANIAdb, a database specifically designed to investigate how Leishmania virulence factors may interfere with host proteins. Since the secretomes of different Leishmania species are only partially characterized, we collated various experimental evidence and used computational predictions to identify Leishmania secreted proteins to generate a user-friendly unified web resource allowing users to access all information available on experimental and predicted secretomes. In addition, we manually annotated host-pathogen interactions of 211 proteins and the localization/function of 3764 transmembrane (TM) proteins of different Leishmania species. We also enriched all proteins with automatic structural and functional predictions that can provide new insights in the molecular mechanisms of infection. Our database may provide novel insights into Leishmania host-pathogen interactions and help to identify new therapeutic targets for this neglected disease. Database URL  https://leishmaniadb.ttk.hu/.

Recent Advances in Chemotherapeutics for Leishmaniasis: Importance of the Cellular Biochemistry of the Parasite and Its Molecular Interaction with the Host

Leishmaniasis, a category 1 neglected protozoan disease caused by a kinetoplastid pathogen called Leishmania, is transmitted through dipteran insect vectors (phlebotomine, sand flies) in three main clinical forms: fatal visceral leishmaniasis, self-healing cutaneous leishmaniasis, and mucocutaneous leishmaniasis. Generic pentavalent antimonials have long been the drug of choice against leishmaniasis; however, their success is plagued with limitations such as drug resistance and severe side effects, which makes them redundant as frontline therapy for endemic visceral leishmaniasis. Alternative therapeutic regimens based on amphotericin B, miltefosine, and paromomycin have also been approved. Due to the unavailability of human vaccines, first-line chemotherapies such as pentavalent antimonials, pentamidine, and amphotericin B are the only options to treat infected individuals. The higher toxicity, adverse effects, and perceived cost of these pharmaceutics, coupled with the emergence of parasite resistance and disease relapse, makes it urgent to identify new, rationalized drug targets for the improvement in disease management and palliative care for patients. This has become an emergent need and more relevant due to the lack of information on validated molecular resistance markers for the monitoring and surveillance of changes in drug sensitivity and resistance. The present study reviewed the recent advances in chemotherapeutic regimens by targeting novel drugs using several strategies including bioinformatics to gain new insight into leishmaniasis. Leishmania has unique enzymes and biochemical pathways that are distinct from those of its mammalian hosts. In light of the limited number of available antileishmanial drugs, the identification of novel drug targets and studying the molecular and cellular aspects of these drugs in the parasite and its host is critical to design specific inhibitors targeting and controlling the parasite. The biochemical characterization of unique Leishmania-specific enzymes can be used as tools to read through possible drug targets. In this review, we discuss relevant metabolic pathways and novel drugs that are unique, essential, and linked to the survival of the parasite based on bioinformatics and cellular and biochemical analyses.

Finding Correlations Between mRNA and Protein Levels in Leishmania Development: Is There a Discrepancy?

Multiple genes and proteins have been identified as differentially expressed in the stages of the Leishmania life cycle. The differentiation processes are implicated in specific transcriptional and proteomic adjustments driven by gene expression regulation mechanisms. Leishmania parasites lack gene-specific transcriptional control, and gene expression regulation mostly depends on posttranscriptional mechanisms. Due to the lack of transcriptional regulation, criticism regarding the relevance of transcript quantification as a possible and efficient prediction of protein levels is recurrent in studies that use transcriptomic information. The advent of high-throughput technologies has improved the analysis of genomes, transcriptomes and proteomes for different organisms under several conditions. Nevertheless, defining the correlation between transcriptional and proteomic profiles requires arduous and expensive work and remains a challenge in Leishmania. In this review, we analyze transcriptomic and proteomic data for several Leishmania species in two different stages of the parasite life cycle: metacyclogenesis and amastigogenesis (amastigote differentiation). We found a correlation between mRNA and protein levels of 60.9% and 69.8% for metacyclogenesis and amastigogenesis, respectively; showing that majority mRNA and protein levels increase or decrease concomitantly. Among the analyzed genes that did not present correlation indicate that transcriptomic data should be carefully interpreted as protein expression. We also discuss possible explanations and mechanisms involved for this lack of correlation.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.

RNAmining: A machine learning stand-alone and web server tool for RNA coding potential prediction

Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied seven machine learning algorithms (Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Neural Networks and Deep Learning) through model organisms from different evolutionary branches to create a stand-alone and web server tool (RNAmining) to distinguish coding and non-coding sequences. Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their trinucleotides count analysed (64 features) and we performed a normalization by the sequence length, resulting in total of 180 models. The machine learning algorithms validations were performed using 10-fold cross-validation and we selected the algorithm with the best results (eXtreme Gradient Boosting) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and TransDecoder) and our results outperformed them. Both stand-alone and web server versions of RNAmining are freely available at https://rnamining.integrativebioinformatics.me/.

In-depth quantitative proteomics uncovers specie-specific metabolic programs in Leishmania (Viannia) species

Leishmania species are responsible for a broad spectrum of diseases, denominated Leishmaniasis, affecting over 12 million people worldwide. During the last decade, there have been impressive efforts for sequencing the genome of most of the pathogenic Leishmania spp. as well as hundreds of strains, but large-scale proteomics analyses did not follow these achievements and the Leishmania proteome remained mostly uncharacterized. Here, we report a comprehensive comparative study of the proteomes of strains representing L. braziliensis, L. panamensis and L. guyanensis species. Proteins extracted by SDS-mediated lysis were processed following the multi-enzyme digestion-filter aided sample preparation (FASP) procedure and analysed by high accuracy mass spectrometry. "Total Protein Approach" and "Proteomic Ruler" were applied for absolute quantification of proteins. Principal component analysis demonstrated very high reproducibility among biological replicates and a very clear differentiation of the three species. Our dataset comprises near 7000 proteins, representing the most complete Leishmania proteome yet known, and provides a comprehensive quantitative picture of the proteomes of the three species in terms of protein concentration and copy numbers. Analysis of the abundance of proteins from the major energy metabolic processes allow us to highlight remarkably differences among the species and suggest that these parasites depend on distinct energy substrates to obtain ATP. Whereas L. braziliensis relies the more on glycolysis, L. panamensis and L. guyanensis seem to depend mainly on mitochondrial respiration. These results were confirmed by biochemical assays showing opposite profiles for glucose uptake and O2 consumption in these species. In addition, we provide quantitative data about different membrane proteins, transporters, and lipids, all of which contribute for significant species-specific differences and provide rich substrate for explore new molecules for diagnosing purposes. Data are available via ProteomeXchange with identifier PXD017696.

Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential

The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.

Leishmania-Host Interactions-An Epigenetic Paradigm

Leishmaniasis is one of the major neglected tropical diseases, for which no vaccines exist. Chemotherapy is hampered by limited efficacy coupled with development of resistance and other side effects. Leishmania parasites elude the host defensive mechanisms by modulating their surface proteins as well as dampening the host's immune responses. The parasites use the conventional RNA polymerases peculiarly under different environmental cues or pressures such as the host's milieu or the drugs. The mechanisms that restructure post-translational modifications are poorly understood but altered epigenetic histone modifications are believed to be instrumental in influencing the chromatin remodeling in the parasite. Interestingly, the parasite also modulates gene expression of the hosts, thereby hijacking or dampening the host immune response. Epigenetic factor such as DNA methylation of cytosine residues has been incriminated in silencing of macrophage-specific genes responsible for defense against these parasites. Although there is dearth of information regarding the epigenetic alterations-mediated pathogenesis in these parasites and the host, the unique epigenetic marks may represent targets for potential anti-leishmanial drug candidates. This review circumscribes the epigenetic changes during Leishmania infection, and the epigenetic modifications they enforce upon the host cells to ensure a safe haven. The non-coding micro RNAs as post-transcriptional regulators and correlates of wound healing and toll-like receptor signaling, as well as prognostic biomarkers of therapeutic failure and healing time are also explored. Finally, we highlight the recent advances on how the epigenetic perturbations may impact leishmaniasis vaccine development as biomarkers of safety and immunogenicity.

Long Non-Coding RNAs in the Regulation of Gene Expression: Physiology and Disease

The identification of RNAs that are not translated into proteins was an important breakthrough, defining the diversity of molecules involved in eukaryotic regulation of gene expression. These non-coding RNAs can be divided into two main classes according to their length: short non-coding RNAs, such as microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). The lncRNAs in association with other molecules can coordinate several physiological processes and their dysfunction may impact in several pathologies, including cancer and infectious diseases. They can control the flux of genetic information, such as chromosome structure modulation, transcription, splicing, messenger RNA (mRNA) stability, mRNA availability, and post-translational modifications. Long non-coding RNAs present interaction domains for DNA, mRNAs, miRNAs, and proteins, depending on both sequence and secondary structure. The advent of new generation sequencing has provided evidences of putative lncRNAs existence; however, the analysis of transcriptomes for their functional characterization remains a challenge. Here, we review some important aspects of lncRNA biology, focusing on their role as regulatory elements in gene expression modulation during physiological and disease processes, with implications in host and pathogens physiology, and their role in immune response modulation.

LeishInDB: A web-accessible resource for small molecule inhibitors against Leishmania sp

Despite the availability of drugs to treat Leishmaniasis, various other factors including drug resistance and adverse side effects encourage the researchers to search for new strategies and alternatives for treating Leishmaniasis. Repurposing and devising combination therapy with the existing small molecules would serve as an alternative strategy to address the issue, especially the drug resistance. Hence, here we report LeishInDB, a web-accessible resource of small molecule inhibitors having a varying degree of activity towards Leishmania sp. The database includes searchable information of >7000 small molecules collected from >600 literature. The comprehensive information of inhibitors mainly include the activity details (IC50, EC50, Ki, binding energy etc., if any); information on species and form of Leishmania the inhibitor is active against; and the details about their protein target (actively linked to TriTrypDB). In addition, chemical properties including the log P-value, number of rotatable bonds, number of hydrogen bond donors and acceptors, molecular weight, 2D/3D structural information etc., were also included. Toxicity prediction for each molecule was performed using admetSAR and their corresponding results were available to perform the filtered search. In addition, facility to perform sub-structure search, facility to perform the dynamic search on various fields, and facility to download all the structure of molecules that match the search criteria were also included. We believe that the scope of LeishInDB allows the researchers to utilize the available information for repurposing the inhibitors as well as for the investigation of new therapeutics. Database URL:http://leishindb.biomedinformri.com/.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Predicting RNA Families in Nucleotide Sequences Using StructRNAfinder

Noncoding RNA (ncRNA) research is already a routine in every genomics or transcriptomics initiatives. According to their functions, ncRNAs can be grouped into several different RNA families, which can be represented by conserved primary sequences, secondary structures, or covariance models (CMs). CMs are very sensitive in predicting RNA families in nucleotide sequences and have been widely used in characterizing the repertoire of ncRNAs in organisms from all domains of life. However, the large-scale prediction and annotation of ncRNAs require multiple tools along the process, imposing a great obstacle for researchers with lesser computational or bioinformatics background. StructRNAfinder emerged as an automated tool to avoid these bottlenecks, by performing the automatic identification and complete annotation of regulatory RNA families derived directly from nucleotide sequences. In this chapter, we provide a complete tutorial for both stand-alone and web server versions of StructRNAfinder. This will help users to install the tool and to perform predictions of RNA families in any genome or transcriptome sequences dataset.

StructRNAfinder: an automated pipeline and web server for RNA families prediction

Background

The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills.

Results

We present an automated pipeline named StructRNAfinder that predicts and annotates RNA families in transcript or genome sequences. This single tool not only displays the sequence/structural consensus alignments for each RNA family, according to Rfam database but also provides a taxonomic overview for each assigned functional RNA. Moreover, we implemented a user-friendly web service that allows researchers to upload their own nucleotide sequences in order to perform the whole analysis. Finally, we provided a stand-alone version of StructRNAfinder to be used in large-scale projects. The tool was developed under GNU General Public License (GPLv3) and is freely available at http://structrnafinder.integrativebioinformatics.me.

Conclusions

The main advantage of StructRNAfinder relies on the large-scale processing and integrating the data obtained by each tool and database employed along the workflow, of which several files are generated and displayed in user-friendly reports, useful for downstream analyses and data exploration.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2052-2) contains supplementary material, which is available to authorized users.