A critical review of machine-learning for "multi-omics" marine metabolite datasets

Generative adversarial network integrated with metabolomics identifies potential biomarkers related to quality changes of atemoya (Annona cherimola × Annona squamosa) stored at 10 and 25 °C

Atemoya fruit deteriorates rapidly during post-harvest storage. A complete understanding of the metabolic mechanisms underlying this process is crucial for developing effective preservation strategies. Metabolomic approaches combined with machine learning offer new opportunities to identify quality-related biomarkers. This study compared atemoya quality stored at 25 °C and 10 °C using untargeted metabolomics integrated with generative adversarial network (GAN) and random forest (RF) analysis. It was found that GAN successfully amplified the metabolomic dataset 10-fold, enabling robust RF-based identification of 20 quality change-related biomarkers. These biomarkers were primarily involved in energy metabolism, reactive oxygen species regulation and primary metabolic pathways including amino acids, lipids and carbohydrates. Low-temperature storage inhibited respiration, preserved cell structure and altered specific glycerophospholipid metabolic pathways. These findings provide molecular insights into low temperature preservation mechanisms and establish a novel framework for metabolomic data analysis in postharvest research.

Progress in the discovery and development of anticancer agents from marine cyanobacteria

Covering 2010-April 2024There have been tremendous new discoveries and developments since 2010 in anticancer research based on marine cyanobacteria. Marine cyanobacteria are prolific sources of anticancer natural products, including the tubulin agents dolastatins 10 and 15 which were originally isolated from a mollusk that feeds on cyanobacteria. Decades of research have culminated in the approval of six antibody-drug conjugates (ADCs) and many ongoing clinical trials. Antibody conjugation has been enabling for several natural products, particularly cyanobacterial cytotoxins. Targeting tubulin dynamics has been a major strategy, leading to the discovery of the gatorbulin scaffold, acting on a new pharmacological site. Cyanobacterial compounds with different mechanisms of action (MOA), targeting novel or validated targets in a range of organelles, also show promise as anticancer agents. Important advances include the development of compounds with novel MOA, including apratoxin and coibamide A analogues, modulating cotranslational translocation at the level of Sec61 in the endoplasmic reticulum, largazole and santacruzamate A targeting class I histone deacetylases, and proteasome inhibitors based on carmaphycins, resembling the approved drug carfilzomib. The pipeline extends with SERCA inhibitors, mitochondrial cytotoxins and membrane-targeting agents, which have not yet advanced clinically since the biology is less understood and selectivity concerns remain to be addressed. In addition, efforts have also focused on the identification of chemosensitizing and antimetastatic agents. The review covers the state of current knowledge of marine cyanobacteria as anticancer agents with a focus on the mechanism, target identification and potential for drug development. We highlight the importance of solving the supply problem through chemical synthesis as well as illuminating the biological activity and in-depth mechanistic studies to increase the value of cyanobacterial natural products to catalyze their development.

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Multi-omics in exploring the pathophysiology of diabetic retinopathy

Diabetic retinopathy (DR) is a leading global cause of vision impairment, with its prevalence increasing alongside the rising rates of diabetes mellitus (DM). Despite the retina's complex structure, the underlying pathology of DR remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) and recent advancements in multi-omics analyses have revolutionized molecular profiling, enabling high-throughput analysis and comprehensive characterization of complex biological systems. This review highlights the significant contributions of scRNA-seq, in conjunction with other multi-omics technologies, to DR research. Integrated scRNA-seq and transcriptomic analyses have revealed novel insights into DR pathogenesis, including alternative transcription start site events, fluctuations in cell populations, altered gene expression profiles, and critical signaling pathways within retinal cells. Furthermore, by integrating scRNA-seq with genetic association studies and multi-omics analyses, researchers have identified novel biomarkers, susceptibility genes, and potential therapeutic targets for DR, emphasizing the importance of specific retinal cell types in disease progression. The integration of scRNA-seq with metabolomics has also been instrumental in identifying specific metabolites and dysregulated pathways associated with DR. It is highly conceivable that the continued synergy between scRNA-seq and other multi-omics approaches will accelerate the discovery of underlying mechanisms and the development of novel therapeutic interventions for DR.

Discovery of novel CDK4/6 inhibitors from fungal secondary metabolites

The development of targeted therapies for breast cancer, particularly those focusing on cyclin-dependent kinases 4/6 (CDK4/6), has significantly improved patient outcomes. However, the currently approved CDK4/6 inhibitors are associated with various side effects, underscoring the need for novel compounds with enhanced efficacy and safety profiles. This study aimed to identify potential CDK4/6 inhibitors from MeFSAT, a database of fungal secondary metabolites using an in-silico screening approach. The virtual screening process incorporated drug-likeness filters, ADME and toxicity predictions, consensus molecular docking, and 200 ns molecular dynamics simulations. Out of 411 initial compounds, two molecules demonstrated favorable binding interactions and stability with the CDK4/6 protein complex. The MTT assay showed that MSID000025 had dose-dependent cytotoxicity against MCF7 breast cancer cells. This suggests that MSID000025 could be a good candidate CDK4/6 inhibitor for treating breast cancer. Our study highlights the potential of fungal secondary metabolites as a source of novel compounds for drug discovery. It provides a framework for identifying CDK4/6 inhibitors with improved therapeutic properties.

Machine learning-based prediction models unleash the enhanced production of fucoxanthin in Isochrysis galbana

Introduction

The marine microalga Isochrysis galbana is prolific producer of fucoxanthin, which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. This study presented a novel integrated experimental approach coupled with machine learning (ML) models to predict the fucoxanthin content in I. galbana by altering the type and concentration of phytohormone supplementation, thus overcoming the multiple methodological limitations of conventional fucoxanthin quantification.

Methods

A novel integrated experimental approach was developed, analyzing the effect of varying phytohormone types and concentrations on fucoxanthin production in I. galbana. Morphological analysis was conducted to assess changes in microalgal structure, while growth rate and fucoxanthin yield correlations were explored using statistical analysis and machine learning models. Several ML models were employed to predict fucoxanthin content, with and without hormone descriptors as variables.

Results

The findings revealed that the Random Forest (RF) model was highly significant with a highR 2 of 0.809 and R M S E of 0.776 when hormone descriptors were excluded, and the inclusion of hormone descriptors further improved prediction accuracy toR 2 of 0.839, making it a useful tool for predicting the fucoxanthin yield. The model that fitted the experimental data indicated methyl jasmonate (0.2 mg/L) as an effective phytohormone. The combined experimental and ML approach demonstrated rapid, reliable, and cost-efficient prediction of fucoxanthin yield.

Discussion

This study highlights the potential of machine learning models, particularly Random Forest, to optimize parameters influencing microalgal growth and fucoxanthin production. This approach offers a more efficient alternative to conventional methods, providing valuable insights into improving fucoxanthin production in microalgal cultivation. The findings suggest that leveraging diverse ML models can enhance the predictability and efficiency of fucoxanthin production, making it a promising tool for industrial applications.

Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8+ T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity

BACKGROUND

α-1,3-mannosyltransferase (ALG3) holds significance as a key member within the mannosyltransferase family. Nevertheless, the exact function of ALG3 in cancer remains ambiguous. Consequently, the current research aimed to examine the function and potential mechanisms of ALG3 in various types of cancer.

METHODS

Deep pan-cancer analyses were conducted to investigate the expression patterns, prognostic value, genetic variations, single-cell omics, immunology, and drug responses associated with ALG3. Subsequently, in vitro experiments were executed to ascertain the biological role of ALG3 in breast cancer. Moreover, the link between ALG3 and CD8+ T cells was verified using immunofluorescence. Lastly, the association between ALG3 and chemokines was assessed using qRT-PCR and ELISA.

RESULTS

Deep pan-cancer analysis demonstrated a heightened expression of ALG3 in the majority of tumors based on multi-omics evidence. ALG3 emerges as a diagnostic and prognostic biomarker across diverse cancer types. In addition, ALG3 participates in regulating the tumor immune microenvironment. Elevated levels of ALG3 were closely linked to the infiltration of bone marrow-derived suppressor cells (MDSC) and CD8+ T cells. According to in vitro experiments, ALG3 promotes proliferation and migration of breast cancer cells. Moreover, ALG3 inhibited CD8+ T cell infiltration by suppressing chemokine secretion. Finally, the inhibition of ALG3 enhanced the responsiveness of breast cancer cells to 5-fluorouracil treatment.

CONCLUSION

ALG3 shows potential as both a prognostic indicator and immune infiltration biomarker across various types of cancer. Inhibition of ALG3 may represent a promising therapeutic strategy for tumor treatment.

PHE-SICH-CT-IDS: A benchmark CT image dataset for evaluation semantic segmentation, object detection and radiomic feature extraction of perihematomal edema in spontaneous intracerebral hemorrhage

BACKGROUND AND OBJECTIVE

Intracerebral hemorrhage is one of the diseases with the highest mortality and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH) typically presents acutely, prompt and expedited radiological examination is crucial for diagnosis, localization, and quantification of the hemorrhage. Early detection and accurate segmentation of perihematomal edema (PHE) play a critical role in guiding appropriate clinical intervention and enhancing patient prognosis. However, the progress and assessment of computer-aided diagnostic methods for PHE segmentation and detection face challenges due to the scarcity of publicly accessible brain CT image datasets.

METHODS

This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage. The dataset comprises 120 brain CT scans and 7,022 CT images, along with corresponding medical information of the patients. To demonstrate its effectiveness, classical algorithms for semantic segmentation, object detection, and radiomic feature extraction are evaluated. The experimental results confirm the suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation, detection and radiomic feature extraction methods.

RESULTS

This study conducts numerous experiments using classical machine learning and deep learning methods, demonstrating the differences in various segmentation and detection methods on the PHE-SICH-CT-IDS. The highest precision achieved in semantic segmentation is 76.31%, while object detection attains a maximum precision of 97.62%. The experimental results on radiomic feature extraction and analysis prove the suitability of PHE-SICH-CT-IDS for evaluating image features and highlight the predictive value of these features for the prognosis of SICH patients.

CONCLUSION

To the best of our knowledge, this is the first publicly available dataset for PHE in SICH, comprising various data formats suitable for applications across diverse medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to explore novel algorithms, providing valuable support for clinicians and patients in the clinical setting. PHE-SICH-CT-IDS is freely published for non-commercial purpose at https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.