High-throughput Approach Research Articles

Insights into freshwater ciliate diversity through high throughput DNA metabarcoding.

The freshwater bodies of India are highly biodiverse but still understudied, especially concerning ciliates. Ciliates constitute a significant portion of eukaryotic diversity and play crucial roles in microbial loops, nutrient recycling, and ecosystem maintenance. The present study aimed to elucidate ciliate diversity in three freshwater sites in the Delhi region of India: Okhla Bird Sanctuary (OBS), Sanjay Lake (SL), and Raj Ghat pond (RJ). This study represents the first investigation into the taxonomic diversity and richness of freshwater ciliates in India using a high-throughput DNA metabarcoding approach. For the analysis, total environmental DNA was extracted from the three freshwater samples, followed by sequencing of the 18S V4 barcode region and subsequent phylogenetic analyses. Operational taxonomic units (OTU) analyses revealed maximum species diversity in OBS (106), followed by SL (104) and RJ (99) sites. Ciliates from the classes Oligohymenophorea, Prostomatea, and Spirotrichea were dominant in the three sites. The study discusses the ability of the metabarcoding approach to uncover unknown and rare species. The study highlights the need for refined reference databases and cautious interpretation of the high-throughput sequencing-generated data while emphasizing the complementary nature of molecular and morphological approaches in studying ciliate diversity.

Mn Additive Improves Zr Grain Boundary Diffusion for Sintering of a Y-Doped BaZrO3 Proton Conductor.

Yttrium-doped barium zirconate (BZY) has garnered attention as a protonic conductor in intermediate-temperature electrolysis and fuel cells due to its high bulk proton conductivity and excellent chemical stability. However, the performance of BZY can be further enhanced by reducing the concentration and resistance of grain boundaries. In this study, we investigate the impact of manganese (Mn) additives on the sinterability and proton conductivity of Y-doped BaZrO3 (BZY). By employing a combinatorial pulsed laser deposition (PLD) technique, we synthesized BZY thin films with varying Mn concentrations and sintering temperatures. Our results revealed a significant enhancement in sinterability as Mn concentrations increased, leading to larger grain sizes and lower grain boundary concentrations. These improvements can be attributed to the elevated grain boundary diffusion of zirconium (Zr) cations, which enhances material densification. We also observed a reduction in Goldschmidt's tolerance factor with increased Mn substitution, which can improve proton transport. The high proton conduction of BZY with Mn additives in low-temperature and wet hydrogen environments makes it a promising candidate for protonic ceramic electrolysis cells and fuel cells. Our findings not only advance the understanding of Mn additives in BZY materials but also demonstrate a high-throughput combinatorial thin film approach to select additives for other perovskite materials with importance in mass and charge transport applications.

Proteomic study on nintedanib in gastric cancer cells.

Gastric cancer is a very common gastrointestinal tumor with a high mortality rate. Nintedanib has been shown to significantly reduce tumor cell proliferation and increase apoptosis in gastric cancer cells in vitro. However, its systemic action mechanism on gastric cancer cells remains unclear. A high-throughput proteomic approach should help identify the potential mechanisms and targets of nintedanib on gastric cancer cells. The effects of nintedanib on the biological behavior of gastric cancer cells were evaluated. A cytotoxic proliferation assay was performed to estimate the half maximal inhibitory concentration (IC50). AGS cells were divided into control, and nintedanib-treated groups (5 µM, 48 h), and differential protein expression was investigated using tandem mass tags (TMT) proteomics. The molecular mechanisms of these differentially expressed proteins and their network interactions were then analyzed using bioinformatics, and potential nintedanib targets were identified. This study identified 845 differentially expressed proteins in the nintedanib-treated group (compared to the control group), comprising 526 up-regulated and 319 down-regulated proteins. Bioinformatics analysis revealed that the differentially expressed proteins were primarily enriched in biological pathways for branched-chain amino acid metabolism, steroid biosynthesis, propionate metabolism, fatty acid metabolism, lysosome, peroxisome, and ferroptosis. Key driver analysis revealed that proteins, such as enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH), isocitrate dehydrogenase 1 (IDH1), acyl-CoA oxidase 1 (ACOX1), acyl-CoA oxidase 2 (ACOX2), acyl-CoA oxidase 3 (ACOX3), and acetyl-CoA acyltransferase 1 (ACAA1) could be linked with nintedanib action. Nintedanib inhibits the proliferation, invasion, and metastasis of gastric cancer cells. The crossover pathways and protein networks predicted by proteomics should provide more detailed molecular information enabling the use of nintedanib against gastric cancer.

Exploring Andean High-Altitude Lake Extremophiles through Advanced Proteotyping.

Quickly identifying and characterizing isolates from extreme environments is currently challenging while very important to explore the Earth's biodiversity. As these isolates may, in principle, be distantly related to known species, techniques are needed to reliably identify the branch of life to which they belong. Proteotyping these environmental isolates by tandem mass spectrometry offers a rapid and cost-effective option for their identification using their peptide profiles. In this study, we document the first high-throughput proteotyping approach for environmental extremophilic and halophilic isolates. Microorganisms were isolated from samples originating from high-altitude Andean lakes (3700-4300 m a.s.l.) in the Chilean Altiplano, which represent environments on Earth that resemble conditions on other planets. A total of 66 microorganisms were cultivated and identified by proteotyping and 16S rRNA gene amplicon sequencing. Both the approaches revealed the same genus identification for all isolates except for three isolates possibly representing not yet taxonomically characterized organisms based on their peptidomes. Proteotyping was able to indicate the presence of two potentially new genera from the families of Paracoccaceae and Chromatiaceae/Alteromonadaceae, which have been overlooked by 16S rRNA amplicon sequencing approach only. The paper highlights that proteotyping has the potential to discover undescribed microorganisms from extreme environments.

What’s in Your Fruit Juice?—Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy

Fruit juices (FJ) have gained widespread global consumption, driven by their perceived health benefits. The accuracy of nutrition information is essential for consumers assessing FJ quality, especially with increasing concerns about added sugars and obesity risk. Conversely, ascorbic acid (Vitamin C), found in nature in many fruits and vegetables, is often lost due to its susceptibility to light, air, and heat, and it undergoes fortification during FJ production. Current analytical methods for determining FJ components are time-consuming and labor-intensive, prompting the need for rapid analytical tools. This study employed a field-deployable portable FT-IR device, requiring no sample preparation, to simultaneously predict multiple quality traits in 68 FJ samples from US markets. Using partial least square regression (PLSR) models, a strong correlation (RCV ≥ 0.93) between FT-IR predictions and reference values was obtained, with a low standard error of prediction. Remarkably, 21% and 37% of FJs deviated from nutrition label values for sugars and ascorbic acid, respectively. Portable FT-IR devices offer non-destructive, simultaneous, simple, and high-throughput approaches for chemical profiling and real-time prediction of sugars and acid levels in FJs. Their handiness and ruggedness can provide food processors with a valuable “out-of-the-laboratory” analytical tool.

Analysis of Culturable Bacterial Diversity of Pangong Tso Lake via a 16S rRNA Tag Sequencing Approach.

The Pangong Tso lake is a high-altitude freshwater habitat wherein the resident microbes experience unique selective pressures, i.e., high radiation, low nutrient content, desiccation, and temperature extremes. Our study attempts to analyze the diversity of culturable bacteria by applying a high-throughput amplicon sequencing approach based on long read technology to determine the spectrum of bacterial diversity supported by axenic media. The phyla Pseudomonadota, Bacteriodetes, and Actinomycetota were retrieved as the predominant taxa in both water and sediment samples. The genera Hydrogenophaga and Rheinheimera, Pseudomonas, Loktanella, Marinomonas, and Flavobacterium were abundantly present in the sediment and water samples, respectively. Low nutrient conditions supported the growth of taxa within the phyla Bacteriodetes, Actinomycetota, and Cyanobacteria and were biased towards the selection of Pseudomonas, Hydrogenophaga, Bacillus, and Enterococcus spp. Our study recommends that media formulations can be finalized after analyzing culturable diversity through a high-throughput sequencing effort to retrieve maximum species diversity targeting novel/relevant taxa.

Discovery of broadly-neutralizing antibodies against brown recluse spider and Gadim scorpion sphingomyelinases using consensus toxins as antigens.

Broadly-neutralizing monoclonal antibodies are becoming increasingly important tools for treating infectious diseases and animal envenomings. However, designing and developing broadly-neutralizing antibodies can be cumbersome using traditional low-throughput iterative protein engineering methods. Here, we present a new high-throughput approach for the standardized discovery of broadly-neutralizing monoclonal antibodies relying on phage display technology and consensus antigens representing average sequences of related proteins. We showcase the utility of this approach by applying it to toxic sphingomyelinases from the venoms of species from very distant orders of the animal kingdom, the recluse spider and Gadim scorpion. First, we designed a consensus sphingomyelinase and performed three rounds of phage display selection, followed by DELFIA-based screening and ranking, and benchmarked this to a similar campaign involving cross-panning against recombinant versions of the native toxins. Second, we identified two scFvs that not only bind the consensus toxins, but which can also neutralize sphingomyelinase activity of native whole venom in vitro. Finally, we conclude that the phage display campaign involving the use of the consensus toxin was more successful in yielding cross-neutralizing scFvs than the phage display campaign involving cross-panning.

Biosensor-assisted CRISPRi high-throughput screening to identify genetic targets in Zymomonas mobilis for high d-lactate production

Lactate is an important monomer for the synthesis of poly-lactate (PLA), which is a substitute for the petrochemical plastics. To achieve the goal of high lactate titer, rate, and yield for commercial production, efficient lactate production pathway is needed as well as genetic targets that affect high lactate production and tolerance. In this study, an LldR-based d-lactate biosensor with a broad dynamic range was first applied into Zymomonas mobilis to select mutant strains with strong GFP fluorescence, which could be the mutant strains with increased d-lactate production. Then, LldR-based d-lactate biosensor was combined with a genome-wide CRISPR interference (CRISPRi) library targeting the entire genome to generate thousands of mutants with gRNA targeting different genetic targets across the whole genome. Specifically, two mutant libraries were selected containing 105 and 104 mutants with different interference sites from two rounds of fluorescence-activated cell sorting (FACS), respectively. Two genetic targets of ZMO1323 and ZMO1530 were characterized and confirmed to be associated with the increased d-lactate production, further knockout of ZMO1323 and ZMO1530 resulted in a 15% and 21% increase of d-lactate production, respectively. This work thus not only established a high-throughput approach that combines genome-scale CRISPRi and biosensor-assisted screening to identify genetic targets associated with d-lactate production in Z. mobilis, but also provided a feasible high-throughput screening approach for rapid identification of genetic targets associated with strain performance for other industrial microorganisms.

A high-throughput target-based screening approach for the identification and assessment of Mycobacterium tuberculosis mycothione reductase inhibitors.

The growing anti-microbial resistance poses a global public health threat, impeding progress toward eradicating tuberculosis. Despite decades of active research, there is still a dire need for the discovery of drugs with novel modes of action and exploration of combination drug regimens. Within the European RespiriTB consortium, we explore Mycobacterium tuberculosis energy metabolism to identify new drug candidates that synergize with bedaquiline, with the aim of discovering more efficient combination drug regimens. In this study, we present the development of a high-throughput screening pipeline that led to the identification of M. tuberculosis mycothione reductase inhibitors.

High-throughput reprogramming of an NRPS condensation domain

Engineered biosynthetic assembly lines could revolutionize the sustainable production of bioactive natural product analogs. Although yeast display is a proven, powerful tool for altering the substrate specificity of gatekeeper adenylation domains in nonribosomal peptide synthetases (NRPSs), comparable strategies for other components of these megaenzymes have not been described. Here we report a high-throughput approach for engineering condensation (C) domains responsible for peptide elongation. We show that a 120-kDa NRPS module, displayed in functional form on yeast, can productively interact with an upstream module, provided in solution, to produce amide products tethered to the yeast surface. Using this system to screen a large C-domain library, we reprogrammed a surfactin synthetase module to accept a fatty acid donor, increasing catalytic efficiency for this noncanonical substrate >40-fold. Because C domains can function as selectivity filters in NRPSs, this methodology should facilitate the precision engineering of these molecular assembly lines.

Analysis of HBV integration in mitochondrial DNA of HepAD38 cells by the high-throughput HBV integration sequencing and RNASeq approaches

Analysis of HBV integration in mitochondrial DNA of HepAD38 cells by the high-throughput HBV integration sequencing and RNASeq approaches

Analysis of immunoglobulin/T-cell receptor repertoires by high-throughput RNA sequencing reveals a continuous dynamic of positive clonal selection in follicular lymphoma.

Follicular lymphoma (FL) course is highly variable, making its clinical management challenging. In this incurable and recurring pathology, the interval between relapses tends to decrease while aggressiveness increases, sometimes resulting in the transformation to higher-grade lymphoma. These evolutions are particularly difficult to anticipate, resulting from complex clonal evolutions where multiple subclones compete and thrive due to their capacity to proliferate and resist therapies. Here, to apprehend further these processes, we used a high-throughput RNA sequencing approach to address simultaneously the B-cell immunoglobulin repertoires and T-cell immunoglobulin repertoires repertoires of lymphoma cells and their lymphoid microenvironment in a large cohort of 131 FL1/2-3A patients. Our data confirm the existence of a high degree of intra-clonal heterogeneity in this pathology, resulting from ongoing somatic hyper-mutation and class switch recombination. Through the evaluation of the Simpson ecological-diversity index, we show that the contribution of the cancerous cells increases during the course of the disease to the detriment of the reactive compartment, a phenomenon accompanied by a concomitant decrease in the diversity of the tumoral population. Clonal evolution in FL thus contrasts with many tumors, where clonal heterogeneity steadily increases over time and participates in treatment evasion. In this pathology, the selection of lymphoma subclones with proliferative advantages progressively outweighs clonal diversification, ultimately leading in extreme cases to transformation to high-grade lymphoma resulting from the rapid emergence of homogeneous subpopulations.

High-Throughput GPCRome Screen of Pollutants Reveals the Activity of Polychlorinated Biphenyls at Melatonin and Sphingosine-1-phosphate Receptors.

Exposure to environmental pollutants is linked to numerous toxic outcomes, warranting concern about the effect of pollutants on human health. To assess the threat of pollutant exposure, it is essential to understand their biological activity. Unfortunately, gaps remain for many pollutants' specific biological activity and molecular targets. A superfamily of signaling proteins, G-protein-coupled receptors (GPCRs), has been shown as potential targets for pollutant activity. However, research investigating the pollutant activity at the GPCRome is scarce. This work explores pollutant activity across a library of human GPCRs by leveraging modern high-throughput screening techniques devised for drug discovery and pharmacology. We designed and implemented a pilot screen of eight pollutants at 314 human GPCRs and discovered specific polychlorinated biphenyl (PCB) activity at sphingosine-1-phosphate and melatonin receptors. The method utilizes open-source resources available to academic and governmental institutions to enable future campaigns that screen large numbers of pollutants. Thus, we present a novel high-throughput approach to assess the biological activity and specific targets of pollutants.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry traces the geographical source of Biomphalariapfeifferi and Bulinusforskalii, involved in schistosomiasis transmission

BackgroundFreshwater snails of the genera Bulinus spp., Biomphalaria spp., and Oncomelania spp. are the main intermediate hosts of human and animal schistosomiasis. Identification of these snails has long been based on morphological and/or genomic criteria, which have their limitations. These limitations include a lack of precision for the morphological tool and cost and time for the DNA-based approach. Recently, Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight (MALDI–TOF) mass spectrometry, a new tool used which is routinely in clinical microbiology, has emerged in the field of malacology for the identification of freshwater snails. This study aimed to evaluate the ability of MALDI–TOF MS to identify Biomphalariapfeifferi and Bulinusforskalii snail populations according to their geographical origin.MethodsThis study was conducted on 101 Bi.pfeifferi and 81 Bu.forskalii snails collected in three distinct geographical areas of Senegal (the North-East, South-East and central part of the country), and supplemented with wild and laboratory strains. Specimens which had previously been morphologically described were identified by MALDI–TOF MS [identification log score values (LSV) ≥ 1.7], after an initial blind test using the pre-existing database. After DNA-based identification, new reference spectra of Bi.pfeifferi (n = 10) and Bu.forskalii (n = 5) from the geographical areas were added to the MALDI–TOF spectral database. The final blind test against this updated database was performed to assess identification at the geographic source level.ResultsMALDI–TOF MS correctly identified 92.1% of 101 Bi.pfeifferi snails and 98.8% of 81 Bu.forskalii snails. At the final blind test, 88% of 166 specimens were correctly identified according to both their species and sampling site, with LSVs ranging from 1.74 to 2.70. The geographical source was adequately identified in 90.1% of 91 Bi.pfeifferi and 85.3% of 75 Bu.forskalii samples.ConclusionsOur findings demonstrate that MALDI–TOF MS can identify and differentiate snail populations according to geographical origin. It outperforms the current DNA-based approaches in discriminating laboratory from wild strains. This inexpensive high-throughput approach is likely to further revolutionise epidemiological studies in areas which are endemic for schistosomiasis.

Towards an Improved High-Throughput Phenotyping Approach: Utilizing MLRA and Dimensionality Reduction Techniques for Transferring Hyperspectral Proximal-Based Model to Airborne Images

At present, it is critical to accurately monitor wheat crops to help decision-making processes in precision agriculture. This research aims to retrieve various wheat crop traits from hyperspectral data using machine learning regression algorithms (MLRAs) and dimensionality reduction (DR) techniques. This experiment was conducted in an agricultural field in Arborea, Oristano-Sardinia, Italy, with different factors such as cultivars, N-treatments, and soil ploughing conditions. Hyperspectral data were acquired on the ground using a full-range Spectral Evolution spectrometer (350–2500 nm). Four DR techniques, including (i) variable influence on projection (VIP), (ii) principal component analysis (PCA), (iii) vegetation indices (VIs), and (iv) spectroscopic feature (SF) calculation, were undertaken to reduce the dimension of the hyperspectral data while maintaining the information content. We used five MLRA models, including (i) partial least squares regression (PLSR), (ii) random forest (RF), (iii) support vector regression (SVR), (iv) Gaussian process regression (GPR), and (v) neural network (NN), to retrieve wheat traits at either leaf and canopy levels. The studied traits were leaf area index (LAI), leaf and canopy water content (LWC and CWC), leaf and canopy chlorophyll content (LCC and CCC), and leaf and canopy nitrogen content (LNC and CNC). MLRA models were able to accurately retrieve wheat traits at the canopy level with PLSR and NN indicating the highest modelling performance. On the contrary, MLRA models indicated less accurate retrievals of the leaf-level traits. DR techniques were found to notably improve the retrieval accuracy of crop traits. Furthermore, the generated models were re-calibrated using soil spectra and then transferred to an airborne dataset collected using a CASI-SASI hyperspectral sensor, allowing the estimation of wheat traits across the entire field. The predicted crop trait maps illustrated consistent patterns while also preserving the real-field characteristics well. Lastly, a statistical paired t-test was undertaken to conduct a proof of concept of wheat phenotyping analysis considering the different agricultural variables across the study site. N-treatment caused significant differences in wheat crop traits in many instances, whereas the observed differences were less pronounced between the cultivars. No particular impact of soil ploughing conditions on wheat crop characteristics was found. Using such combinations of MLRA and DR techniques based on hyperspectral data can help to effectively monitor crop traits throughout the cropping seasons and can also be readily applied to other agricultural settings to help both precision farming applications and the implementation of high-throughput phenotyping solutions.

Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels as well as machine learning models trained on simulated data to predict the drag coefficient and Stanton number. We show that convolutional neural networks (CNNs) can accurately predict target properties at a fraction of the computational cost of numerical simulations. We use CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data augmentation techniques are incorporated to enforce physical invariances toward shifting and flipping, contributing to precise prediction for fluid flow and heat transfer characteristics. Moreover, we approach the interpretation of the trained model to better understand relevant channel structures and their influence on heat transfer. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering.

Metatranscriptomic Sequencing of Medically Important Mosquitoes Reveals Extensive Diversity of RNA Viruses and Other Microbial Communities in Western Australia.

Mosquitoes harbor a wide diversity of microorganisms, including viruses that are human pathogens, or that are insect specific. We used metatranscriptomics, an unbiased high-throughput molecular approach, to describe the composition of viral and other microbial communities in six medically important mosquito species from across Western Australia: Aedes vigilax, Culex annulirostris, Cx. australicus, Cx. globocoxitus, Cx. pipiens biotype molestus, and Cx. quinquefasciatus. We identified 42 viral species, including 13 novel viruses, from 19 families. Culex mosquitoes exhibited a significantly higher diversity of viruses than Aedes mosquitoes, and no virus was shared between the two genera. Comparison of mosquito populations revealed a heterogenous distribution of viruses between geographical regions and between closely related species, suggesting that geography and host species may play a role in shaping virome composition. We also detected bacterial and parasitic microorganisms, among which Wolbachia bacteria were detected in three members of the Cx. pipiens complex, Cx. australicus, Cx. pipiens biotype molestus, and Cx. quinquefasciatus. In summary, our unbiased metatranscriptomics approach provides important insights into viral and other microbial diversity in Western Australian mosquitoes that vector medically important viruses.

Screening of astaxanthin-overproducing Xanthophyllomyces dendrorhous strains via iterative ARTP mutagenesis and cell sorting by flow cytometry.

The astaxanthin-producing yeast Xanthophyllomyces dendrorhous is widely used in aquaculture. Due to the production of carotenoid, this yeast shows visible color; however, high-throughput approaches for identification of astaxanthin-overproducing strains remain rare. This study verified an effective approach to identify astaxanthin-overproducing mutants of X. dendrorhous by flow cytometry (FCM) and cell sorting. First, the mutant libraries were generated by atmospheric and room-temperature plasma (ARTP) mutagenesis. Second, a highly direct correlation between the concentrations of intracellular astaxanthin and the levels of emitting fluorescence was constructed by testing a variety of astaxanthin-contained populations via FCM and cell sorting. Third, iterative cell sorting efficiently improves the identification of astaxanthin-overproducing strains. Finally, two mutants producing 4.96mg astaxanthin g-1 DCW (dry cell weight) and 5.30mg astaxanthin g-1 DCW were obtained, which were 25.3% and 33.8% higher than that of the original strain, respectively. This study demonstrated that iterative ARTP mutagenesis along with cell sorting by FCM is effective for identifying astaxanthin-overproduction strains.

Multilevel analysis between Physcomitrium patens and Mortierellaceae endophytes explores potential long-standing interaction among land plants and fungi.

The model moss species Physcomitrium patens has long been used for studying divergence of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes provide a system to represent basic plant architecture. Based on plant-fungal interactions today, it is hypothesized these kingdoms have a long-standing relationship, predating plant terrestrialization. Mortierellaceae have origins diverging from other land fungi paralleling bryophyte divergence, are related to arbuscular mycorrhizal fungi but are free-living, observed to interact with plants, and can be found in moss microbiomes globally. Due to their parallel origins, we assess here how two Mortierellaceae species, Linnemannia elongata and Benniella erionia, interact with P. patens in coculture. We also assess how Mollicute-related or Burkholderia-related endobacterial symbionts (MRE or BRE) of these fungi impact plant response. Coculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Here we present new high-throughput approaches for measuring P. patens growth, identify novel expression of over 800 genes that are not expressed on traditional agar media, identify subtle interactions between P. patens and Mortierellaceae, and observe changes to plant-fungal interactions dependent on whether MRE or BRE are present. Our study provides insights into how plants and fungal partners may have interacted based on their communications observed today as well as identifying L. elongata and B. erionia as modern fungal endophytes with P. patens.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.