Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach.

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. The aggregation pathway of α-synuclein typically involves a defined sequence of nucleation, elongation, and secondary nucleation, exhibiting prion-like spreading. This study employed Raman spectroscopy and machine learning analysis, alongside complementary techniques, to characterize the biomolecular changes during the fibrillation of purified recombinant wild-type α-synuclein protein.

Microbiome alterations are associated with apolipoprotein E mutation in and humans with Alzheimer's disease.

Gut microbiome dysbiosis is linked to many neurological disorders including Alzheimer's disease (AD). A major risk factor for AD is polymorphism in the apolipoprotein E () gene, which affects gut microbiome composition. To explore the gut-brain axis in AD, long-lived animal models of naturally developing AD-like pathologies are needed.

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information.

Ablation of the dystrophin Dp71f alternative C-terminal variant increases sarcoma tumour cell aggressiveness.

Alterations in Dp71 expression, the most ubiquitous dystrophin isoform, have been associated with patient survival across tumours. Intriguingly, in certain malignancies, Dp71 acts as a tumour suppressor, while manifesting oncogenic properties in others. This diversity could be explained by the expression of two Dp71 splice variants encoding proteins with distinct C-termini, each with specific properties.

Emerging methods for genome-scale metabolic modeling of microbial communities.

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities.

Uncovering potential diagnostic and pathophysiological roles of α-synuclein and DJ-1 in melanoma.

Background: Melanoma, the most lethal skin cancer type, occurs more frequently in Parkinson's disease (PD), and PD is more frequent in melanoma patients, suggesting disease mechanisms overlap. α-synuclein, a protein that accumulates in PD brain, and the oncogene DJ-1, which is associated with PD autosomal recessive forms, are both elevated in melanoma cells. Whether this indicates melanoma progression or constitutes a protective response remains unclear.

Mechanism-aware and multimodal AI: beyond model-agnostic interpretation.

Artificial intelligence (AI) is widely used for exploiting multimodal biomedical data, with increasingly accurate predictions and model-agnostic interpretations, which are however also agnostic to biological mechanisms. Combining metabolic modelling, 'omics, and imaging data via multimodal AI can generate predictions that can be interpreted mechanistically and transparently, therefore with significantly higher therapeutic potential.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study.

Integration of epigenetic regulatory mechanisms in heart failure.

The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches.

Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.

Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes.

Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy.

Background: Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression.

Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions.

Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions.

The diagnostic and prognostic potential of the EGFR/MUC4/MMP9 axis in glioma patients.

Glioblastoma is the most aggressive form of brain cancer, presenting poor prognosis despite current advances in treatment. There is therefore an urgent need for novel biomarkers and therapeutic targets. Interactions between mucin 4 (MUC4) and the epidermal growth factor receptor (EGFR) are involved in carcinogenesis, and may lead to matrix metalloproteinase-9 (MMP9) overexpression, exacerbating cancer cell invasiveness.

Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods.

Background: Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries.

Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use.

Salt tolerant organisms are increasingly being used for the industrial production of high-value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole-genome sequencing allow in-depth insight into the potential of organisms while at the frontiers of systems biology.

Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer.

Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes.

Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts.

Duchenne muscular dystrophy (DMD) affects myofibers and muscle stem cells, causing progressive muscle degeneration and repair defects. It was unknown whether dystrophic myoblasts-the effector cells of muscle growth and regeneration-are affected. Using transcriptomic, genome-scale metabolic modelling and functional analyses, we demonstrate, for the first time, convergent abnormalities in primary mouse and human dystrophic myoblasts.

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models.

Computational profiling of natural compounds as promising inhibitors against the spike proteins of SARS-CoV-2 wild-type and the variants of concern, viral cell-entry process, and cytokine storm in COVID-19.

The continuous spread and evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the rapid surge in infection cases in the coronavirus disease 2019 (COVID-19) evoke a dire need for effective therapeutics. In this study, we explored the inhibitory potential of a library of 605 phytocompounds, selected from Indian medicinal plants with reported antiviral and anti-inflammatory activities, against the receptor-binding domain of spike proteins of the SARS-CoV-2 wild-type and the variants of concern, including variants B.1.

Using machine learning as a surrogate model for agent-based simulations.

In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Surrogate modelling, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods.

Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium sp. PCC 7002.

Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to sp.

Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction.

Motivation: Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge.