Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

Enhancing Colorectal Cancer Immunotherapy: The Pivotal Role of Ferroptosis in Modulating the Tumor Microenvironment

Colorectal cancer (CRC) represents a significant challenge in oncology, with increasing incidence and mortality rates worldwide, particularly among younger adults. Despite advancements in treatment modalities, the urgent need for more effective therapies persists. Immunotherapy has emerged as a beacon of hope, offering the potential for improved outcomes and quality of life. This review delves into the critical interplay between ferroptosis, an iron-dependent form of regulated cell death, and immunotherapy within the CRC context. Ferroptosis's influence extends beyond tumor cell fate, reshaping the tumor microenvironment (TME) to enhance immunotherapy's efficacy. Investigations into Ferroptosis-related Genes (OFRGs) reveal their pivotal role in modulating immune cell infiltration and TME composition, closely correlating with tumor responsiveness to immunotherapy. The integration of ferroptosis inducers with immunotherapeutic strategies, particularly through novel approaches like ferrotherapy and targeted co-delivery systems, showcases promising avenues for augmenting treatment efficacy. Furthermore, the expression patterns of OFRGs offer novel prognostic tools, potentially guiding personalized and precision therapy in CRC. This review underscores the emerging paradigm of leveraging ferroptosis to bolster immunotherapy's impact, highlighting the need for further research to translate these insights into clinical advancements. Through a deeper understanding of the ferroptosis-immunotherapy nexus, new therapeutic strategies can be developed, promising enhanced efficacy and broader applicability in CRC treatment, ultimately improving patient outcomes and quality of life in the face of this formidable disease.

Gene regulatory network inference based on a nonhomogeneous dynamic Bayesian network model with an improved Markov Monte Carlo sampling

A nonhomogeneous dynamic Bayesian network model, which combines the dynamic Bayesian network and the multi-change point process, solves the limitations of the dynamic Bayesian network in modeling non-stationary gene expression data to a certain extent. However, certain problems persist, such as the low network reconstruction accuracy and poor model convergence. Therefore, we propose an MD-birth move based on the Manhattan distance of the data points to increase the rationality of the multi-change point process. The underlying concept of the MD-birth move is that the direction of movement of the change point is assumed to have a larger Manhattan distance between the variance and the mean of its left and right data points. Considering the data instability characteristics, we propose a Markov chain Monte Carlo sampling method based on node-dependent particle filtering in addition to the multi-change point process. The candidate parent nodes to be sampled, which are close to the real state, are pushed to the high probability area through the particle filter, and the candidate parent node set to be sampled that is far from the real state is pushed to the low probability area and then sampled. In terms of reconstructing the gene regulatory network, the model proposed in this paper (FC-DBN) has better network reconstruction accuracy and model convergence speed than other corresponding models on the Saccharomyces cerevisiae data and RAF data.

Multi-omics protein-coding units as massively parallel Bayesian networks: empirical validation of causality structure

In this article we use high-throughput epigenomics, transcriptomics, and proteomics data to construct fine-graded models of the “protein-coding units” gathering all transcript isoforms and chromatin accessibility peaks associated with more than 4000 genes in humans. Each protein-coding unit has the structure of a directed acyclic graph (DAG) and can be represented as a Bayesian network. The factorization of the joint probability distribution induced by the DAGs imposes a number of conditional independence relationships among the variables forming a protein-coding unit, corresponding to the missing edges in the DAGs. We show that a large fraction of these conditional independencies are indeed verified by the data. Factors driving this verification appear to be the structural and functional annotation of the transcript isoforms, as well as a notion of structural balance (or frustration-free) of the corresponding sample correlation graph, which naturally leads to reduction of correlation (and hence to independence) upon conditioning.

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Protein coding unit: DAG associated with epigenetic and gene information of a protein

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DAGs correspond to Bayesian networks

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Edge absence on a DAG corresponds to conditional independence

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Multi-omics data (ATAC-seq, RNA-seq and mass-spec) are used for DAG validation

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.

Dexamethasone Induces Changes in Osteogenic Differentiation of Human Mesenchymal Stromal Cells via SOX9 and PPARG, but Not RUNX2

Despite the huge body of research on osteogenic differentiation and bone tissue engineering, the translation potential of in vitro results still does not match the effort employed. One reason might be that the protocols used for in vitro research have inherent pitfalls. The synthetic glucocorticoid dexamethasone is commonly used in protocols for trilineage differentiation of human bone marrow mesenchymal stromal cells (hBMSCs). However, in the case of osteogenic commitment, dexamethasone has the main pitfall of inhibiting terminal osteoblast differentiation, and its pro-adipogenic effect is well known. In this work, we aimed to clarify the role of dexamethasone in the osteogenesis of hBMSCs, with a particular focus on off-target differentiation. The results showed that dexamethasone does induce osteogenic differentiation by inhibiting SOX9 expression, but not directly through RUNX2 upregulation as it is commonly thought. Rather, PPARG is concomitantly and strongly upregulated, leading to the formation of adipocyte-like cells within osteogenic cultures. Limiting the exposure to dexamethasone to the first week of differentiation did not affect the mineralization potential. Gene expression levels of RUNX2, SOX9, and PPARG were simulated using approximate Bayesian computation based on a simplified theoretical model, which was able to reproduce the observed experimental trends but with a different range of responses, indicating that other factors should be integrated to fully understand how dexamethasone influences cell fate. In summary, this work provides evidence that current in vitro differentiation protocols based on dexamethasone do not represent a good model, and further research is warranted in this field.

From Laboratory towards Industrial Operation: Biomarkers for Acidophilic Metabolic Activity in Bioleaching Systems

In the actual mining scenario, copper bioleaching, mainly raw mined material known as run-of-mine (ROM) copper bioleaching, is the best alternative for the treatment of marginal resources that are not currently considered part of the profitable reserves because of the cost associated with leading technologies in copper extraction. It is foreseen that bioleaching will play a complementary role in either concentration-as it does in Minera Escondida Ltd. (MEL)-or chloride main leaching plants. In that way, it will be possible to maximize mines with installed solvent-extraction and electrowinning capacities that have not been operative since the depletion of their oxide ores. One of the main obstacles for widening bioleaching technology applications is the lack of knowledge about the key events and the attributes of the technology's critical events at the industrial level and mainly in ROM copper bioleaching industrial operations. It is relevant to assess the bed environment where the bacteria-mineral interaction occurs to learn about the limiting factors determining the leaching rate. Thus, due to inability to accurately determine in-situ key variables, their indirect assessment was evaluated by quantifying microbial metabolic-associated responses. Several candidate marker genes were selected to represent the predominant components of the microbial community inhabiting the industrial heap and the metabolisms involved in microbial responses to changes in the heap environment that affect the process performance. The microbial community's predominant components were Acidithiobacillus ferrooxidans, At. thiooxidans, Leptospirillum ferriphilum, and Sulfobacillus sp. Oxygen reduction, CO₂ and N₂ fixation/uptake, iron and sulfur oxidation, and response to osmotic stress were the metabolisms selected regarding research results previously reported in the system. After that, qPCR primers for each candidate gene were designed and validated. The expression profile of the selected genes vs. environmental key variables in pure cultures, column-leaching tests, and the industrial bioleaching heap was defined. We presented the results obtained from the industrial validation of the marker genes selected for assessing CO₂ and N₂ availability, osmotic stress response, as well as ferrous iron and sulfur oxidation activity in the bioleaching heap process of MEL. We demonstrated that molecular markers are useful for assessing limiting factors like nutrients and air supply, and the impact of the quality of recycled solutions. We also learned about the attributes of variables like CO₂, ammonium, and sulfate levels that affect the industrial ROM-scale operation.

Review of Artificial Intelligence Applications and Algorithms for Brain Organoid Research

The human brain organoid is a miniature three-dimensional tissue culture that can simulate the structure and function of the brain in an in vitro culture environment. Although we consider that human brain organoids could be used to understand brain development and diseases, experimental models of human brain organoids are so highly variable that we apply artificial intelligence (AI) techniques to investigate the development mechanism of the human brain. Therefore, this study briefly reviewed commonly used AI applications for human brain organoid-magnetic resonance imaging, electroencephalography, and gene editing techniques, as well as related AI algorithms. Finally, we discussed the limitations, challenges, and future study direction of AI-based technology for human brain organoids.

Systems biology of acidophile biofilms for efficient metal extraction

Society’s demand for metals is ever increasing while stocks of high-grade minerals are being depleted. Biomining, for example of chalcopyrite for copper recovery, is a more sustainable biotechnological process that exploits the capacity of acidophilic microbes to catalyze solid metal sulfide dissolution to soluble metal sulfates. A key early stage in biomining is cell attachment and biofilm formation on the mineral surface that results in elevated mineral oxidation rates. Industrial biomining of chalcopyrite is typically carried out in large scale heaps that suffer from the downsides of slow and poor metal recoveries. In an effort to mitigate these drawbacks, this study investigated planktonic and biofilm cells of acidophilic (optimal growth pH < 3) biomining bacteria. RNA and proteins were extracted, and high throughput “omics” performed from a total of 80 biomining experiments. In addition, micrographs of biofilm formation on the chalcopyrite mineral surface over time were generated from eight separate experiments. The dataset generated in this project will be of great use to microbiologists, biotechnologists, and industrial researchers.

Measurement(s)	biofilm formation • Proteome • protein expression data • RNA • transcriptome
Technology Type(s)	fluorescence microscopy • mass spectrometry • RNA sequencing
Sample Characteristic - Organism	Leptospirillum ferriphilum • Sulfobacillus thermosulfidooxidans • Acidithiobacillus caldus
Sample Characteristic - Environment	mine

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12173637