Comprehensive expression-based isoform biomarkers predictive of drug responses based on isoform co-expression networks and clinical data

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

Evolution of isoform-level gene expression patterns across tissues during lotus species divergence

Both gene duplication and alternative splicing (AS) drive the functional diversity of gene products in plants, yet the relative contributions of the two key mechanisms to the evolution of gene function are largely unclear. Here, we studied AS in two closely related lotus plants, Nelumbo lutea and Nelumbo nucifera, and the outgroup Arabidopsis thaliana, for both single-copy and duplicated genes. We show that most splicing events evolved rapidly between orthologs and that the origin of lineage-specific splice variants or isoforms contributed to gene functional changes during species divergence within Nelumbo. Single-copy genes contain more isoforms, have more AS events conserved across species, and show more complex tissue-dependent expression patterns than their duplicated counterparts. This suggests that expression divergence through isoforms is a mechanism to extend the expression breadth of genes with low copy numbers. As compared to isoforms of local, small-scale duplicates, isoforms of whole-genome duplicates are less conserved and display a less conserved tissue bias, pointing towards their contribution to subfunctionalization. Through comparative analysis of isoform expression networks, we identified orthologous genes of which the expression of at least some of their isoforms displays a conserved tissue bias across species, indicating a strong selection pressure for maintaining a stable expression pattern of these isoforms. Overall, our study shows that both AS and gene duplication contributed to the diversity of gene function during the evolution of lotus.

Comparative transcriptomic analysis of two Cucumis melo var. saccharinus germplasms differing in fruit physical and chemical characteristics

BACKGROUND

Hami melon (Cucumis melo var. saccharinus) is a popular fruit in China because of its excellent taste, which is largely determined by its physicochemical characteristics, including flesh texture, sugar content, aroma, and nutrient composition. However, the mechanisms by which these characteristics are regulated have not yet been determined. In this study, we monitored changes in the fruits of two germplasms that differed in physicochemical characteristics throughout the fruit development period.

RESULTS

Ripe fruit of the bred variety 'Guimi' had significantly higher soluble sugar contents than the fruit of the common variety 'Yaolong.' Additionally, differences in fruit shape and color between these two germplasms were observed during development. Comparative transcriptome analysis, conducted to identify regulators and pathways underlying the observed differences at corresponding stages of development, revealed a higher number of differentially expressed genes (DEGs) in Guimi than in Yaolong. Moreover, most DEGs detected during early fruit development in Guimi were associated with cell wall biogenesis. Temporal analysis of the identified DEGs revealed similar trends in the enrichment of downregulated genes in both germplasms, although there were differences in the enrichment trends of upregulated genes. Further analyses revealed trends in differential changes in multiple genes involved in cell wall biogenesis and sugar metabolism during fruit ripening.

CONCLUSIONS

We identified several genes associated with the ripening of Hami melons, which will provide novel insights into the molecular mechanisms underlying the development of fruit characteristics in these melons.

acorde unravels functionally interpretable networks of isoform co-usage from single cell data

Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may be relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we develop and validate percentile correlations, an innovative approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs, and domains potentially controlled by the coordination of post-transcriptional regulation. The code for acorde is available at https://github.com/ConesaLab/acorde .

BZD9L1 sirtuin inhibitor: Identification of key molecular targets and their biological functions in HCT 116 colorectal cancer cells

BZD9L1 was previously described as a SIRT1/2 inhibitor with anti-cancer activities in colorectal cancer (CRC), either as a standalone chemotherapy or in combination with 5-fluorouracil. BZD9L1 was reported to induce apoptosis in CRC cells; however, the network of intracellular pathways and crosstalk between molecular players mediated by BZD9L1 is not fully understood. This study aimed to uncover the mechanisms involved in BZD9L1-mediated cytotoxicity based on previous and new findings for the prediction and identification of related pathways and key molecular players. BZD9L1-regulated candidate targets (RCTs) were identified using a range of molecular, cell-based and biochemical techniques on the HCT 116 cell line. BZD9L1 regulated major cancer pathways including Notch, p53, cell cycle, NFκB, Myc/MAX, and MAPK/ERK signalling pathways. BZD9L1 also induced reactive oxygen species (ROS), regulated apoptosis-related proteins, and altered cell polarity and adhesion profiles. In silico analyses revealed that most RCTs were interconnected, and were involved in the modulation of catalytic activity, metabolism and transcription regulation, response to cytokines, and apoptosis signalling pathways. These RCTs were implicated in p53-dependent apoptosis pathway. This study provides the first assessment of possible associations of molecular players underlying the cytotoxic activity of BZD9L1, and establishes the links between RCTs and apoptosis through the p53 pathway.

World competitive contest-based artificial neural network: A new class-specific method for classification of clinical and biological datasets

Many data mining methods have been proposed to generate computer-aided diagnostic systems, which may determine diseases in their early stages by categorizing the data into some proper classes. Considering the importance of the existence of a suitable classifier, the present study aims to introduce an efficient approach based on the World Competitive Contests (WCC) algorithm as well as a multi-layer perceptron artificial neural network (ANN). Unlike the previously introduced methods, which each has developed a universal model for all different kinds of data classes, our proposed approach generates a single specific model for each individual class of data. The experimental results show that the proposed method (ANNWCC), which can be applied to both the balanced and unbalanced datasets, yields more than 76% (without applying feature selection methods) and 90% (with applying feature selection methods) of the average five-fold cross-validation accuracy on the 13 clinical and biological datasets. The findings also indicate that under different conditions, our proposed method can produce better results in comparison to some state-of-art meta-heuristic algorithms and methods in terms of various statistical and classification measurements. To classify the clinical and biological data, a multi-layer ANN and the WCC algorithm were combined. It was shown that developing a specific model for each individual class of data may yield better results compared with creating a universal model for all of the existing data classes. Besides, some efficient algorithms proved to be essential to generate acceptable biological results, and the methods' performance was found to be enhanced by fuzzifying or normalizing the biological data.

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We combined multi-layer artificial neural networks and world competitive contests algorithms to classify biological datasets

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The proposed method has been investigated on 13 clinical datasets with different properties

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Efficient models may yield better classification models and health diagnostic systems

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Feature selection methods can improve the performance of a model in separating case and control samples

In silico analysis of alternative splicing on drug-target gene interactions

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.

Network-based method for drug target discovery at the isoform level

Identification of primary targets associated with phenotypes can facilitate exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited effort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path’ drug target prioritization method. We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defined by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. Lastly, we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking. Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.