Next-generation technologies for multiomics approaches including interactome sequencing

Next-generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review

This critical review summarizes the current state of omics-based biomarkers in the alcohol research field. We first provide definitions and background information on alcohol and alcohol use disorder (AUD), biomarkers, and "omic" technologies. We next summarize using (1) genetic information as risk/prognostic biomarkers for the onset of alcohol-related problems and the progression from regular drinking to problematic drinking (including AUD), (2) epigenetic information as diagnostic biomarkers for AUD and risk biomarkers for alcohol consumption, (3) transcriptomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and (4) metabolomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and predictive biomarkers for response to acamprosate in subjects with AUD. In the final section, the clinical implications of the findings are discussed, and recommendations are made for future research.

ATP-binding cassette (ABC) transporters: structures and roles in bacterial pathogenesis

Adenosine triphosphate (ATP)-binding cassette (ABC) transporter systems are divided into importers and exporters that facilitate the movement of diverse substrate molecules across the lipid bilayer, against the concentration gradient. These transporters comprise two highly conserved nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs). Unlike ABC exporters, prokaryotic ABC importers require an additional substrate-binding protein (SBP) as a recognition site for specific substrate translocation. The discovery of a large number of ABC systems in bacterial pathogens revealed that these transporters are crucial for the establishment of bacterial infections. The existing literature has highlighted the roles of ABC transporters in bacterial growth, pathogenesis, and virulence. These roles include importing essential nutrients required for a variety of cellular processes and exporting outer membrane-associated virulence factors and antimicrobial substances. This review outlines the general structures and classification of ABC systems to provide a comprehensive view of the activities and roles of ABC transporters associated with bacterial virulence and pathogenesis during infection.

Multi-component Chinese medicine formulas for drug discovery: State of the art and future perspectives

For hundreds of years, the drug discovery and development industry has aimed at identifying single components with a clear mechanism of action as desirable candidates for potential drugs. However, this conventional strategy of drug discovery and development has faced challenges including a low success rate and high development costs. Herein, we critically review state-of-the-art drug discovery and development based on multi-component Chinese medicine formulas. We review the policies and application status of new drugs based on multi-component Chinese medicines in the US, China, and the European Union. Moreover, we illustrate several excellent cases of ongoing applications. Biomedical technologies that may facilitate drug discovery and development based on multi-component Chinese medicine formulas are discussed, including network pharmacology, integrative omics, CRISPR gene editing, and chemometrics. Finally, we discuss potential problems and solutions in pre-clinical and clinical research in drug discovery and development based on multi-component Chinese medicine formulas. We hope that this review will promote discussion of the roles of multi-component Chinese medicine formulas in the discovery and development of new drugs for the treatment of human diseases.

Multi-omics analysis reveals GABAergic dysfunction after traumatic brainstem injury in rats

Background

Traumatic brainstem injury (TBSI) is one of the forms of brain injury and has a very high mortality rate. Understanding the molecular mechanism of injury can provide additional information for clinical treatment.

Materials and methods

In this study, we detected transcriptome, proteomics, and metabolome expression changes in the brainstem of TBSI rats, and comprehensively analyzed the underlying mechanisms of TBSI.

Results

After TBSI, there was significant diffuse axonal injury (DAI) in the brainstem of rats. A total of 579 genes, 70 proteins, and 183 metabolites showed significant changes in brainstem tissue. Through molecular function and pathway analysis, the differentially expressed genes, proteins, and metabolites of TBSI were mainly attributed to neural signal regulation, inflammation, neuroprotection, and immune system. In addition, a comprehensive analysis of transcripts, proteins, and metabolites showed that the genes, proteins, and metabolic pathways regulated in the brainstem after TBSI were involved in neuroactive ligand-receptor interaction. A variety of GCPR-regulated pathways were affected, especially GAGA's corresponding receptors GABA_A, GABA_B, GABA_C, and transporter GAT that were inhibited to varying degrees.

Conclusion

This study provides insights into the development of a rapid diagnostic kit and making treatment strategies for TBSI.

A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Background

Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies.

Objective

The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now.

Results

The proposed study, based on previous studies and manufacturers' descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position.

Conclusion

The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

Proteomic profile analysis of pulmonary artery in a rat model under hypoxic pulmonary hypertensionc

Aim:

Proteomic profile analysis of pulmonary artery in a rat model under hypoxic pulmonary hypertension

Background:

Background: Hypoxic pulmonary hypertension (HPH) is a pathological condition exemplified by a constant rise in pulmonary artery pressure in high-altitudes.

Objective:

Objective: To investigated the proteome profile and response mechanisms of SD rats under hypoxia over a period of four-weeks.

Method:

Method: Proteomic profile analysis of pulmonary artery in a rat model under hypoxic pulmonary hypertension.

Results:

Results: With 3,204 proteins identified, 49 were up-regulated while 46 were down-regulated. Upregulated genes included Prolargin, Protein S100-A6 and Transgelin-2, whereas Nascent polypeptide-associated complex and Elongator complex protein 1 were down-regulated. KEGG enriched pathways had purine metabolism, cancer and lipolysis regulation as significantly enriched in hypoxic group.

Conclusion:

Conclusion: In conclusion, our findings submit basis for downstream studies on tissue hypoxia mechanisms alongside the associated physiological conditions. Hypoxic pulmonary hypertension (HPH) is a pathological condition exemplified by a constant rise in pulmonary artery pressure in high altitudes. Herein, we investigated the proteome profile and response mechanisms of Sprague-Dawley (SD) rats under hypoxia over a period of four weeks. Unbiased iTRAQ-based quantitative proteomics was utilized in proteome profile analysis of a rat model exposed to HPH. With 3,204 proteins identified, 49 were upregulated while 46 were downregulated. Upregulated genes included Prolargin, Protein, S100-A6 and Transgelin-2, whereas Nascent polypeptide-associated complex and Elongator complex protein 1 were downregulated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched pathways had purine metabolism, cancer, and lipolysis regulation as significantly enriched in hypoxic group. In conclusion, the findings from this study submit a basis for downstream studies on tissue hypoxia mechanisms alongside the associated physiological conditions.

GYNOCARE Update: Modern Strategies to Improve Diagnosis and Treatment of Rare Gynecologic Tumors—Current Challenges and Future Directions

Simple Summary

More than 50% of all the tumors affecting the female genital tract can be classified as rare and usually have a poor prognosis owing to delayed diagnosis and treatment. Currently, gynecologic cancer research, due to distinct scientific and technological challenges, is lagging behind. Moreover, the overall efforts for addressing these challenges are fragmented across different countries. The European Network for Gynecological Rare Cancer Research: GYNOCARE aims to address these challenges by creating a unique network between key stakeholders covering distinct domains from basic research to cure. GYNOCARE is part of a European Collaboration in Science and Technology (COST) with the aim to focus on the development of new approaches to improve the diagnosis and treatment of rare gynecological tumors. Here, we provide a brief overview describing the goals of this COST Action and its future challenges with the aim to continue fighting against this rare cancer.

Abstract

More than 50% of all gynecologic tumors can be classified as rare (defined as an incidence of ≤6 per 100,000 women) and usually have a poor prognosis owing to delayed diagnosis and treatment. In contrast to almost all other common solid tumors, the treatment of rare gynecologic tumors (RGT) is often based on expert opinion, retrospective studies, or extrapolation from other tumor sites with similar histology, leading to difficulty in developing guidelines for clinical practice. Currently, gynecologic cancer research, due to distinct scientific and technological challenges, is lagging behind. Moreover, the overall efforts for addressing these challenges are fragmented across different European countries and indeed, worldwide. The GYNOCARE, COST Action CA18117 (European Network for Gynecological Rare Cancer Research) programme aims to address these challenges through the creation of a unique network between key stakeholders covering distinct domains from concept to cure: basic research on RGT, biobanking, bridging with industry, and setting up the legal and regulatory requirements for international innovative clinical trials. On this basis, members of this COST Action, (Working Group 1, “Basic and Translational Research on Rare Gynecological Cancer”) have decided to focus their future efforts on the development of new approaches to improve the diagnosis and treatment of RGT. Here, we provide a brief overview of the current state-of-the-art and describe the goals of this COST Action and its future challenges with the aim to stimulate discussion and promote synergy across scientists engaged in the fight against this rare cancer worldwide.

Integration of quantitative proteomics and metabolomics reveals tissue hypoxia mechanisms in an ischemic-hypoxic rat model

Tissues hypoxia caused by hemorrhage is a common complication in many clinical diseases. However, its pathological mechanism remains largely unknown. To partly address this issue, an ischemic-hypoxic rat model was established and the plasma proteomic and metabolic profiles were quantified and analyzed using TMT-based quantitative proteomics and metabolomics. The analysis revealed a total of 177 differentially expressed proteins and 32 metabolites that were uniquely altered in the hypoxic rat plasma, compared to the control. Bioinformatics analysis showed that these altered proteins and metabolites were involved in a wide range of biological processes. Twelve of the 177 differentially expressed proteins were involved in PI3K-Akt signaling, a pathway that has been reported to be strongly associated with tissue hypoxia. Other signaling pathways such as complement and coagulation cascades, GnRH signaling, relaxin signaling, protein processing in endoplasmic reticulum, as well as AGE-RAGE signaling were markedly altered in the ischemic-hypoxic response, implying their potential roles in tissue hypoxia. A joint analysis of proteome and metabolome showed that the significantly altered metabolites such as guanine, tryptamine, dopamine, hexadecenoic, l-methionine, and fumarate may have participated in the pathogenesis of tissue hypoxia. Further, we found that changes in the levels of metabolites matched the changes in protein abundance within the same pathway. Overall, this study presents an overview of the molecular networks in ischemic-hypoxic pathology and offers biochemical basis for further study on the mechanism of tissue hypoxia. SIGNIFICANCE: We employed an integrated metabonomic-proteomic method to systematically analyze the profiles of metabolites and proteins in an ischemic-hypoxic rat model. Bioinformatics and enrichment analysis showed that the differentially expressed proteins were mainly involved in complement and coagulation cascades, PI3K-Akt signaling, GnRH signaling, relaxin signaling, protein processing in endoplasmic reticulum, and AGE-RAGE signaling. Moreover, a panel of 12 candidate proteins involved in PI3K-Akt signaling (i.e., Vtn, Hsp90b1, Ywhae, Tnc, Ywhaz, Thbs4, Lamc1, Col1a1, Il2rg, Egfr, Newgene 621,351, and Tfrc) may serve as the potential biomarkers to predict tissue hypoxia.

Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.

Metabolomics Analysis of Mesenchymal Stem Cells

Various mesenchymal stem cells as easily accessible and multipotent cells can share different essential signaling pathways related to their stemness ability. Understanding the mechanism of stemness ability can be useful for controlling the stem cells for regenerative medicine targets. In this context, OMICs studies can analyze the mechanism of different stem cell properties or stemness ability via a broad range of current high-throughput techniques. This field is fundamentally directed toward the analysis of whole genome (genomics), mRNAs (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in biological samples. According to several studies, metabolomics is more effective than other OMICs ّfor various system biology concerns. Metabolomics can elucidate the biological mechanisms of various mesenchymal stem cell function by measuring their metabolites such as their secretome components. Analyzing the metabolic alteration of mesenchymal stem cells can be useful to promote their regenerative medicine application.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

RNA-Seq analysis revealed genes associated with drought stress response in kabuli chickpea (Cicer arietinum L.)

Drought is the most important constraint that effects chickpea production globally. RNA-Seq has great potential to dissect the molecular mechanisms of tolerance to environmental stresses. Transcriptome profiles in roots and shoots of two contrasting Iranian kabuli chickpea genotypes (Bivanij and Hashem) were investigated under water-limited conditions at early flowering stage using RNA-Seq approach. A total of 4,572 differentially expressed genes (DEGs) were identified. Of these, 261 and 169 drought stress responsive genes were identified in the shoots and the roots, respectively, and 17 genes were common in the shoots and the roots. Gene Ontology (GO) analysis revealed several sub-categories related to the stress, including response to stress, defense response and response to stimulus in the tolerant genotype Bivanij as compared to the sensitive genotype Hashem under drought stress. In addition, several Transcription factors (TFs) were identified in major metabolic pathways such as, ABA, proline and flavonoid biosynthesis. Furthermore, a number of the DEGs were observed in "QTL-hotspot" regions which were reported earlier in chickpea. Drought tolerance dissection in the genotypes revealed that the genes and the pathways involved in shoots of Bivanij were the most important factor to make a difference between the genotypes for drought tolerance. The identified TFs in the experiment, particularly those which were up-regulated in shoots of Bivanij during drought stress, were potential candidates for enhancing tolerance to drought.

Perspectives for elucidating the ethylenediurea (EDU) mode of action for protection against O3 phytotoxicity

Ethylenediurea (EDU) has been widely studied for its effectiveness to protect plants against injuries caused by surface ozone (O₃), however its mode of action remains unclear. So far, there is not a unified methodological approach and thus the methodology is quite arbitrary, thereby making it more difficult to generalize findings and understand the EDU mode of action. This review examines the question of whether potential N addition to plants by EDU is a fundamental underlying mechanism in protecting against O₃ phytotoxicity. Yet, this review proposes an evidence-based hypothesis that EDU may protect plants against O₃ deleterious effects upon generation of EDU-induced hormesis, i.e. by activating plant defense at low doses. This hypothesis challenges the future research directions. Revealing a hormesis-based EDU mode of action in protecting plants against O₃ toxicity would have further implications to ecotoxicology and environmental safety. Furthermore, this review discusses the need for further studies on plant metabolism under EDU treatment through relevant experimental approach, and attempts to set the bases for approaching a unified methodology that will contribute in revealing the EDU mode of action. In this framework, focus is given to the main EDU application methods.

High-throughput system-wide engineering and screening for microbial biotechnology

Genetic engineering and screening of large number of cells or populations is a crucial bottleneck in today's systems biology and applied (micro)biology. Instead of using standard methods in bottles, flasks or 96-well plates, scientists are increasingly relying on high-throughput strategies that miniaturize their experiments to the nanoliter and picoliter scale and the single-cell level. In this review, we summarize different high-throughput system-wide genome engineering and screening strategies for microbes. More specifically, we will emphasize the use of multiplex automated genome evolution (MAGE) and CRISPR/Cas systems for high-throughput genome engineering and the application of (lab-on-chip) nanoreactors for high-throughput single-cell or population screening.

Biases in the SMART-DNA library preparation method associated with genomic poly dA/dT sequences

Avoiding biases in next generation sequencing (NGS) library preparation is crucial for obtaining reliable sequencing data. Recently, a new library preparation method has been introduced which has eliminated the need for the ligation step. This method, termed SMART (switching mechanism at the 5' end of the RNA transcript), is based on template switching reverse transcription. To date, there has been no systematic analysis of the additional biases introduced by this method. We analysed the genomic distribution of sequenced reads prepared from genomic DNA using the SMART methodology and found a strong bias toward long (≥12bp) poly dA/dT containing genomic loci. This bias is unique to the SMART-based library preparation and does not appear when libraries are prepared with conventional ligation based methods. Although this bias is obvious only when performing paired end sequencing, it affects single end sequenced samples as well. Our analysis demonstrates that sequenced reads originating from SMART-DNA libraries are heavily skewed toward genomic poly dA/dT tracts. This bias needs to be considered when deciding to use SMART based technology for library preparation.

The Use of Omic Technologies Applied to Traditional Chinese Medicine Research

Natural products represent one of the most important reservoirs of structural and chemical diversity for the generation of leads in the drug development process. A growing number of researchers have shown interest in the development of drugs based on Chinese herbs. In this review, the use and potential of omic technologies as powerful tools in the modernization of traditional Chinese medicine are discussed. The analytical combination from each omic approach is crucial for understanding the working mechanisms of cells, tissues, organs, and organisms as well as the mechanisms of disease. Gradually, omic approaches have been introduced in every stage of the drug development process to generate high-quality Chinese medicine-based drugs. Finally, the future picture of the use of omic technologies is a promising tool and arena for further improvement in the modernization of traditional Chinese medicine.

The Potentials and Pitfalls of Microarrays in Neglected Tropical Diseases: A Focus on Human Filarial Infections

Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared to conventional diagnostic methods, which primarily focus on single genes or proteins. Thus, microarray technologies offer advantages in understanding host-parasite interactions associated with filarial infections. More importantly, the use of these technologies can aid diagnostics and helps translate current genomic research into effective treatment and interventions for filarial infections. Studying immune responses via microarray following infection can yield insight into genetic pathways and networks that can have a profound influence on the development of anti-parasitic vaccines.

A comprehensive view of the web-resources related to sericulture

Recent progress in the field of sequencing and analysis has led to a tremendous spike in data and the development of data science tools. One of the outcomes of this scientific progress is development of numerous databases which are gaining popularity in all disciplines of biology including sericulture. As economically important organism, silkworms are studied extensively for their numerous applications in the field of textiles, biomaterials, biomimetics, etc. Similarly, host plants, pests, pathogens, etc. are also being probed to understand the seri-resources more efficiently. These studies have led to the generation of numerous seri-related databases which are extremely helpful for the scientific community. In this article, we have reviewed all the available online resources on silkworm and its related organisms, including databases as well as informative websites. We have studied their basic features and impact on research through citation count analysis, finally discussing the role of emerging sequencing and analysis technologies in the field of seri-data science. As an outcome of this review, a web portal named SeriPort, has been created which will act as an index for the various sericulture-related databases and web resources available in cyberspace.Database URL: http://www.seriport.in/.

The in vitro selection world

Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provides the ability to isolate molecules of desired properties and function from large pools (libraries) of random molecules with as many as 10(16) distinct species. This review, in recognition of a quarter of century of scientific discoveries made through in vitro selection, starts with a brief overview of the method and its history. It further covers recent developments in in vitro selection with a focus on tools that enhance the capabilities of in vitro selection and its expansion from being purely a nucleic acids selection to that of polypeptides and proteins. In addition, we cover how next generation sequencing and modern biological computational tools are being used to complement in vitro selection experiments. On the very least, sequencing and computational tools can translate the large volume of information associated with in vitro selection experiments to manageable, analyzable, and exploitable information. Finally, in vivo selection is briefly compared and contrasted to in vitro selection to highlight the unique capabilities of each method.

Visual programming for next-generation sequencing data analytics

Background

High-throughput or next-generation sequencing (NGS) technologies have become an established and affordable experimental framework in biological and medical sciences for all basic and translational research. Processing and analyzing NGS data is challenging. NGS data are big, heterogeneous, sparse, and error prone. Although a plethora of tools for NGS data analysis has emerged in the past decade, (i) software development is still lagging behind data generation capabilities, and (ii) there is a ‘cultural’ gap between the end user and the developer.

Text

Generic software template libraries specifically developed for NGS can help in dealing with the former problem, whilst coupling template libraries with visual programming may help with the latter. Here we scrutinize the state-of-the-art low-level software libraries implemented specifically for NGS and graphical tools for NGS analytics. An ideal developing environment for NGS should be modular (with a native library interface), scalable in computational methods (i.e. serial, multithread, distributed), transparent (platform-independent), interoperable (with external software interface), and usable (via an intuitive graphical user interface). These characteristics should facilitate both the run of standardized NGS pipelines and the development of new workflows based on technological advancements or users’ needs. We discuss in detail the potential of a computational framework blending generic template programming and visual programming that addresses all of the current limitations.

Conclusion

In the long term, a proper, well-developed (although not necessarily unique) software framework will bridge the current gap between data generation and hypothesis testing. This will eventually facilitate the development of novel diagnostic tools embedded in routine healthcare.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015).

Current progress in the biology of members of the Sporothrix schenckii complex following the genomic era

Sporotrichosis has been attributed for more than a century to one single etiological agent, Sporothrix schencki. Only eight years ago, it was described that, in fact, the disease is caused by several pathogenic cryptic species. The present review will focus on recent advances to understand the biology and virulence of epidemiologically relevant pathogenic species of the S. schenckii complex. The main subjects covered are the new clinical and epidemiological aspects including diagnostic and therapeutic challenges, the development of molecular tools, the genome database and the perspectives for study of virulence of emerging Sporothrix species.

Biomarkers of An Autoimmune Skin Disease--Psoriasis

Psoriasis is one of the most prevalent autoimmune skin diseases. However, its etiology and pathogenesis are still unclear. Over the last decade, omics-based technologies have been extensively utilized for biomarker discovery. As a result, some promising markers for psoriasis have been identified at the genome, transcriptome, proteome, and metabolome level. These discoveries have provided new insights into the underlying molecular mechanisms and signaling pathways in psoriasis pathogenesis. More importantly, some of these markers may prove useful in the diagnosis of psoriasis and in the prediction of disease progression once they have been validated. In this review, we summarize the most recent findings in psoriasis biomarker discovery. In addition, we will discuss several emerging technologies and their potential for novel biomarker discovery and diagnostics for psoriasis.