Target-switch SELEX: Screening with alternating targets to generate aptamers to conserved terminal dipeptides.

Systematic evolution of ligands by exponential enrichment (SELEX) encompasses a wide variety of high-throughput screening techniques for producing nucleic acid binders to molecular targets through directed evolution. We describe here the design and selection steps for discovery of DNA aptamers with specificity for the two consecutive N-terminal amino acids (AAs) of a small peptide (8-10 amino acids). This bead-based method may be adapted for applications requiring binders which recognize a specific portion of the desired target. For complete details on the use and execution of this protocol, please refer to Hong et al. (2022).

ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes.

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid-identifying barcodes on the chip. The end goal is that, over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over six consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing.

Machine learning guided aptamer refinement and discovery.

Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks.

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.

Batch equalization with a generative adversarial network.

MOTIVATION: Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-batch variation (i.e. batch effect) due to uncontrollable experimental noise (e.g. varying stain intensity or cell density). Previous approaches to minimize the batch effect have commonly focused on normalizing the low-dimensional image measurements such as an embedding generated by a neural network. However, normalization of the embedding could suffer from over-correction and alter true biological features (e.g. cell size) due to our limited ability to interpret the effect of the normalization on the embedding space. Although techniques like flat-field correction can be applied to normalize the image values directly, they are limited transformations that handle only simple artifacts due to batch effect. RESULTS: We present a neural network-based batch equalization method that can transfer images from one batch to another while preserving the biological phenotype. The equalization method is trained as a generative adversarial network (GAN), using the StarGAN architecture that has shown considerable ability in style transfer. After incorporating new objectives that disentangle batch effect from biological features, we show that the equalized images have less batch information and preserve the biological information. We also demonstrate that the same model training parameters can generalize to two dramatically different types of cells, indicating this approach could be broadly applicable. AVAILABILITY AND IMPLEMENTATION: https://github.com/tensorflow/gan/tree/master/tensorflow_gan/examples/stargan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens.

Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.

The lincRNA MIRAT binds to IQGAP1 and modulates the MAPK pathway in NRAS mutant melanoma.

Despite major advances in targeted melanoma therapies, drug resistance limits their efficacy. Long noncoding RNAs (lncRNAs) are transcriptome elements that do not encode proteins but are important regulatory molecules. LncRNAs have been implicated in cancer development and response to different therapeutics and are thus potential treatment targets; however, the majority of their functions and molecular interactions remain unexplored. In this study, we identify a novel cytoplasmic intergenic lincRNA (MIRAT), which is upregulated following prolonged MAPK inhibition in NRAS mutant melanoma and modulates MAPK signaling by binding to the MEK scaffold protein IQGAP1. Collectively, our results present MIRAT's direct modulatory effect on the MAPK pathway and highlight the relevance of cytoplasmic lncRNAs as potential targets in drug resistant cancer.

Landscape of Long Noncoding RNAs in Psoriatic and Healthy Skin.

We used RNA sequencing to study and characterize the long noncoding RNA (lncRNA) transcriptome in lesional skin from psoriasis patients before (PP) and after treatment (PT) with adalimumab and in normal skin from healthy individuals (NN). To this end, we sequenced total RNA from 18 psoriasis patients and 16 healthy controls. We merged three lncRNA reference datasets to create a single combined reference of 67,157 lncRNA transcripts with no overlaps. We identified differential expression of 971 lncRNAs between PP and NN, 157 between PP and PT, and 377 between PT and NN. Using differentially expressed lncRNAs between PP and NN, we identified a molecular lncRNA signature that distinguishes psoriatic skin from healthy skin. Furthermore, we performed an unsupervised hierarchical analysis that revealed distinct clustering of PP samples from NN. A coding noncoding network analysis revealed a large network of highly correlated lncRNA and protein coding transcripts that provided insight into the potential functions of unannotated lncRNAs. To the best of our knowledge, this description of both polyadenylated as well as nonpolyadenylated lncRNA transcripts in psoriasis has not been previously reported. Our findings highlight the potential importance of lncRNAs in the biology of psoriasis and response to treatment.

An Efficient Method for Identifying Gene Fusions by Targeted RNA Sequencing from Fresh Frozen and FFPE Samples.

Fusion genes are known to be key drivers of tumor growth in several types of cancer. Traditionally, detecting fusion genes has been a difficult task based on fluorescent in situ hybridization to detect chromosomal abnormalities. More recently, RNA sequencing has enabled an increased pace of fusion gene identification. However, RNA-Seq is inefficient for the identification of fusion genes due to the high number of sequencing reads needed to detect the small number of fusion transcripts present in cells of interest. Here we describe a method, Single Primer Enrichment Technology (SPET), for targeted RNA sequencing that is customizable to any target genes, is simple to use, and efficiently detects gene fusions. Using SPET to target 5701 exons of 401 known cancer fusion genes for sequencing, we were able to identify known and previously unreported gene fusions from both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue RNA in both normal tissue and cancer cells.

Novel CARD11 Mutations in Human Cutaneous Squamous Cell Carcinoma Lead to Aberrant NF-κB Regulation.

NF-κB signaling plays a crucial role in regulating proliferation and differentiation in the epidermis. Alterations in the NF-κB pathway can lead to skin pathologies with a significant burden to human health such as psoriasis and cutaneous squamous cell carcinoma (cSCC). Caspase recruitment domain (CARD)-containing scaffold proteins are key regulators of NF-κB signaling by providing a link between membrane receptors and NF-κB transcriptional subunits. Mutations in the CARD family member, CARD14, have been identified in patients with the inflammatory skin diseases psoriasis and pityriasis rubra pilaris. Here, we describe that the gene coding for another CARD scaffold protein, CARD11, is mutated in more than 38% of 111 cSCCs, and show that novel variants outside of the coiled-coil domain lead to constitutively activated NF-κB signaling. CARD11 protein expression was detectable in normal skin and increased in all cSCCs tested. CARD11 mRNA levels were comparable with CARD14 in normal skin and CARD11 mRNA was increased in cSCC. In addition, we identified CARD11 mutations in peritumoral and sun-exposed skin, suggesting that CARD11-mediated alterations in NF-κB signaling may be an early event in the development of cSCC.

High Rhodotorula sequences in skin transcriptome of patients with diffuse systemic sclerosis.

Previous studies have suggested a role for pathogens as a trigger of systemic sclerosis (SSc), although neither a pathogen nor a mechanism of pathogenesis is known. Here we show enrichment of Rhodotorula sequences in the skin of patients with early, diffuse SSc compared with that in normal controls. RNA-seq was performed on four SSc patients and four controls, to a depth of 200 million reads per patient. Data were analyzed to quantify the nonhuman sequence reads in each sample. We found little difference between bacterial microbiome and viral read counts, but found a significant difference between the read counts for a mycobiome component, R. glutinis. Normal samples contained almost no detected R. glutinis or other Rhodotorula sequence reads (mean score 0.021 for R. glutinis, 0.024 for all Rhodotorula). In contrast, SSc samples had a mean score of 5.039 for R. glutinis (5.232 for Rhodotorula). We were able to assemble the D1-D2 hypervariable region of the 28S ribosomal RNA (rRNA) of R. glutinis from each of the SSc samples. Taken together, these results suggest that R. glutinis may be present in the skin of early SSc patients at higher levels than in normal skin, raising the possibility that it may be triggering the inflammatory response found in SSc.

NOTCH1 mutations occur early during cutaneous squamous cell carcinogenesis.

Cutaneous SCC (cSCC) is the most frequently occuring skin cancer with metastatic potential and can manifest rapidly as a common side effect in patients receiving systemic kinase inhibitors. Here, we use massively parallel exome and targeted level sequencing of 132 sporadic cSCCs and of 39 squamoproliferative lesions and cSCCs arising in patients receiving the BRAF inhibitor vemurafenib, as well as 10 normal skin samples, to identify NOTCH1 mutation as an early event in squamous cell carcinogenesis. Bisected vemurafenib-induced lesions revealed surprising heterogeneity with different activating HRAS and NOTCH1 mutations identified in two halves of the same cSCC, suggesting polyclonal origin. Immunohistochemical analysis using an antibody specific to nuclear NOTCH1 correlates with mutation status in sporadic cSCCs, and regions of NOTCH1 loss or downregulation are frequently observed in normal-looking skin. Our data indicate that NOTCH1 acts as a gatekeeper in human cSCC.

IMSA: integrated metagenomic sequence analysis for identification of exogenous reads in a host genomic background.

Metagenomics, the study of microbial genomes within diverse environments, is a rapidly developing field. The identification of microbial sequences within a host organism enables the study of human intestinal, respiratory, and skin microbiota, and has allowed the identification of novel viruses in diseases such as Merkel cell carcinoma. There are few publicly available tools for metagenomic high throughput sequence analysis. We present Integrated Metagenomic Sequence Analysis (IMSA), a flexible, fast, and robust computational analysis pipeline that is available for public use. IMSA takes input sequence from high throughput datasets and uses a user-defined host database to filter out host sequence. IMSA then aligns the filtered reads to a user-defined universal database to characterize exogenous reads within the host background. IMSA assigns a score to each node of the taxonomy based on read frequency, and can output this as a taxonomy report suitable for cluster analysis or as a taxonomy map (TaxMap). IMSA also outputs the specific sequence reads assigned to a taxon of interest for downstream analysis. We demonstrate the use of IMSA to detect pathogens and normal flora within sequence data from a primary human cervical cancer carrying HPV16, a primary human cutaneous squamous cell carcinoma carrying HPV 16, the CaSki cell line carrying HPV16, and the HeLa cell line carrying HPV18.

Combined targeting of MEK and PI3K/mTOR effector pathways is necessary to effectively inhibit NRAS mutant melanoma in vitro and in vivo.

Activating mutations in the neuroblastoma rat sarcoma viral oncogene homolog (NRAS) gene are common genetic events in malignant melanoma being found in 15-25% of cases. NRAS is thought to activate both mitogen activated protein kinase (MAPK) and PI3K signaling in melanoma cells. We studied the influence of different components on the MAP/extracellular signal-regulated (ERK) kinase (MEK) and PI3K/mammalian target of rapamycin (mTOR)-signaling cascade in NRAS mutant melanoma cells. In general, these cells were more sensitive to MEK inhibition compared with inhibition in the PI3K/mTOR cascade. Combined targeting of MEK and PI3K was superior to MEK and mTOR1,2 inhibition in all NRAS mutant melanoma cell lines tested, suggesting that PI3K signaling is more important for cell survival in NRAS mutant melanoma when MEK is inhibited. However, targeting of PI3K/mTOR1,2 in combination with MEK inhibitors is necessary to effectively abolish growth of NRAS mutant melanoma cells in vitro and regress xenografted NRAS mutant melanoma. Furthermore, we showed that MEK and PI3K/mTOR1,2 inhibition is synergistic. Expression analysis confirms that combined MEK and PI3K/mTOR1,2 inhibition predominantly influences genes in the rat sarcoma (RAS) pathway and growth factor receptor pathways, which signal through MEK/ERK and PI3K/mTOR, respectively. Our results suggest that combined targeting of the MEK/ERK and PI3K/mTOR pathways has antitumor activity and might serve as a therapeutic option in the treatment of NRAS mutant melanoma, for which there are currently no effective therapies.

ReCombine: a suite of programs for detection and analysis of meiotic recombination in whole-genome datasets.

In meiosis, the exchange of DNA between chromosomes by homologous recombination is a critical step that ensures proper chromosome segregation and increases genetic diversity. Products of recombination include reciprocal exchanges, known as crossovers, and non-reciprocal gene conversions or non-crossovers. The mechanisms underlying meiotic recombination remain elusive, largely because of the difficulty of analyzing large numbers of recombination events by traditional genetic methods. These traditional methods are increasingly being superseded by high-throughput techniques capable of surveying meiotic recombination on a genome-wide basis. Next-generation sequencing or microarray hybridization is used to genotype thousands of polymorphic markers in the progeny of hybrid yeast strains. New computational tools are needed to perform this genotyping and to find and analyze recombination events. We have developed a suite of programs, ReCombine, for using short sequence reads from next-generation sequencing experiments to genotype yeast meiotic progeny. Upon genotyping, the program CrossOver, a component of ReCombine, then detects recombination products and classifies them into categories based on the features found at each location and their distribution among the various chromatids. CrossOver is also capable of analyzing segregation data from microarray experiments or other sources. This package of programs is designed to allow even researchers without computational expertise to use high-throughput, whole-genome methods to study the molecular mechanisms of meiotic recombination.

Assemblathon 1: a competitive assessment of de novo short read assembly methods.

Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/.

RNA-seq analyses of blood-induced changes in gene expression in the mosquito vector species, Aedes aegypti.

BACKGROUND: Hematophagy is a common trait of insect vectors of disease. Extensive genome-wide transcriptional changes occur in mosquitoes after blood meals, and these are related to digestive and reproductive processes, among others. Studies of these changes are expected to reveal molecular targets for novel vector control and pathogen transmission-blocking strategies. The mosquito Aedes aegypti (Diptera, Culicidae), a vector of Dengue viruses, Yellow Fever Virus (YFV) and Chikungunya virus (CV), is the subject of this study to look at genome-wide changes in gene expression following a blood meal. RESULTS: Transcriptional changes that follow a blood meal in Ae. aegypti females were explored using RNA-seq technology. Over 30% of more than 18,000 investigated transcripts accumulate differentially in mosquitoes at five hours after a blood meal when compared to those fed only on sugar. Forty transcripts accumulate only in blood-fed mosquitoes. The list of regulated transcripts correlates with an enhancement of digestive activity and a suppression of environmental stimuli perception and innate immunity. The alignment of more than 65 million high-quality short reads to the Ae. aegypti reference genome permitted the refinement of the current annotation of transcript boundaries, as well as the discovery of novel transcripts, exons and splicing variants. Cis-regulatory elements (CRE) and cis-regulatory modules (CRM) enriched significantly at the 5'end flanking sequences of blood meal-regulated genes were identified. CONCLUSIONS: This study provides the first global view of the changes in transcript accumulation elicited by a blood meal in Ae. aegypti females. This information permitted the identification of classes of potentially co-regulated genes and a description of biochemical and physiological events that occur immediately after blood feeding. The data presented here serve as a basis for novel vector control and pathogen transmission-blocking strategies including those in which the vectors are modified genetically to express anti-pathogen effector molecules.

RNA-Seq analysis of splicing in Plasmodium falciparum uncovers new splice junctions, alternative splicing and splicing of antisense transcripts.

Over 50% of genes in Plasmodium falciparum, the deadliest human malaria parasite, contain predicted introns, yet experimental characterization of splicing in this organism remains incomplete. We present here a transcriptome-wide characterization of intraerythrocytic splicing events, as captured by RNA-Seq data from four timepoints of a single highly synchronous culture. Gene model-independent analysis of these data in conjunction with publically available RNA-Seq data with HMMSplicer, an in-house developed splice site detection algorithm, revealed a total of 977 new 5' GU-AG 3' and 5 new 5' GC-AG 3' junctions absent from gene models and ESTs (11% increase to the current annotation). In addition, 310 alternative splicing events were detected in 254 (4.5%) genes, most of which truncate open reading frames. Splicing events antisense to gene models were also detected, revealing complex transcriptional arrangements within the parasite's transcriptome. Interestingly, antisense introns overlap sense introns more than would be expected by chance, perhaps indicating a functional relationship between overlapping transcripts or an inherent organizational property of the transcriptome. Independent experimental validation confirmed over 30 new antisense and alternative junctions. Thus, this largest assemblage of new and alternative splicing events to date in Plasmodium falciparum provides a more precise, dynamic view of the parasite's transcriptome.