Molecular Systems Biology最新文献

Colorectal cancer molecular signatures derived from omics data can be employed to stratify CRC patients and aid decisions about therapies or evaluate prognostic outcome. However, molecular biomarkers for identification of patients at increased risk of disease relapse are currently lacking. Here, we present a comprehensive multi-omics analysis of a Danish colorectal cancer tumor cohort composed of 412 biopsies from tumors of 371 patients diagnosed at TNM stage II or III. From mass spectrometry-based patient proteome profiles, we classified the tumors into four molecular subtypes, including a mesenchymal-like subtype. As the mesenchymal-rich tumors are known to represent the most invasive and metastatic phenotype, we focused on the protein signature defining this subtype to evaluate their potential as relapse risk markers. Among signature-specific proteins, we followed-up Caveolae-Associated Protein-1 (CAVIN1) and demonstrated its role in tumor progression in a 3D in vitro model of colorectal cancer. Compared to previous omics analyses of CRC, our multi-omics classification provided deeper insights into EMT in cancer cells with stronger correlations with risk of relapse.

Buffering between genes, where one gene can compensate for the loss of another gene, is fundamental for robust cellular functions. While experimentally testing all possible gene pairs is infeasible, gene buffering can be predicted genome-wide under the assumption that a gene's buffering capacity depends on its expression level and its absence primes a severe fitness phenotype of the buffered gene. We developed BaCoN (Balanced Correlation Network), a post hoc unsupervised correction method that amplifies specific signals in expression-vs-fitness correlation networks. We quantified 147 million potential buffering relationships by associating CRISPR-Cas9-screening fitness effects with transcriptomic data across 1019 Cancer Dependency Map (DepMap) cell lines. BaCoN outperformed state-of-the-art methods, including multiple linear regression based on our compiled gene buffering prediction metrics. Combining BaCoN with batch correction or Cholesky data whitening further boosts predictive performance. We characterized 808 high-confidence buffering predictions and found that in contrast to buffering gene pairs overall, buffering paralogs were on different chromosomes. BaCoN performance increases with more screens and genes considered, making it a valuable tool for gene buffering predictions from the growing DepMap.

Enzyme activation by cellular metabolites plays a pivotal role in regulating metabolic processes. Nevertheless, our comprehension of such activation events on a global network scale remains incomplete. In this study, we conducted a comprehensive investigation into the optimization of cell-intrinsic activation interactions using Saccharomyces cerevisiae metabolic network as the basis of the analysis. To achieve this, we integrated a genome-scale metabolic model with cross-species enzyme kinetic data sourced from the BRENDA database, and to use this model as a basis to estimate the distribution of enzyme activators throughout the cellular network. Our findings indicate that the vast majority of biochemical pathways encompass enzyme activators, frequently originating from disparate pathways, thus revealing extensive regulatory crosstalk between metabolic pathways. Notably, activators have short pathway lengths, indicating they are activated quickly upon nutrient shifts, and in most instances, these activators target key enzymatic reactions to facilitate downstream metabolic processes. Interestingly, highly activated enzymes are substantially enriched with non-essential enzymes compared to their essential counterparts. This observation suggests that cells employ enzyme activators to finely regulate secondary metabolic pathways that are only required under specific conditions. Conversely, the activator metabolites themselves are more likely to be essential components, and their activation levels surpass those of non-essential activators. In summary, our study unveils the widespread importance of enzymatic activators and suggests that feed-forward activation of conditional metabolic pathways through essential metabolites mediates metabolic plasticity.

Macromolecular protein complexes carry out most cellular functions. Unfortunately, we lack the subunit composition for many human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify >15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing nearly 70% of human proteins into their physical contexts. We globally characterize our complexes using mass spectrometry based protein covariation data (ProteomeHD.2) and identify covarying complexes suggesting common functional associations. hu.MAP3.0 generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 5871 mutually exclusive proteins in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal ( https://www.ebi.ac.uk/complexportal/home ) and provide complexes through our hu.MAP3.0 web interface ( https://humap3.proteincomplexes.org/ ). We expect our resource to be highly impactful to the broader research community.

Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single-nucleotide polymorphisms (SNPs). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, and with Polycomb occupancy. We show that histone genes are expressed with allelic imbalance in mESCs, and are involved in haplotype-specific chromatin contacts marked by H3K27me3. Conditional knockouts of Polycomb enzymatic subunits, Ezh2 or Ring1, show that one-third of ASE genes, including histone genes, is regulated through Polycomb repression. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.

Solute carriers (SLCs), the largest superfamily of transporter proteins in humans with about 450 members, control the movement of molecules across membranes. A typical human cell expresses over 200 different SLCs, yet their collective influence on cell phenotypes is not well understood due to overlapping substrate specificities and expression patterns. To address this, we performed systematic pairwise gene double knockouts using CRISPR-Cas12a and -Cas9 in human colon carcinoma cells. A total of 1,088,605 guide combinations were used to interrogate 35,421 SLC-SLC and SLC-enzyme double knockout combinations across multiple growth conditions, uncovering 1236 genetic interactions with a growth phenotype. Further exploration of an interaction between the mitochondrial citrate/malate exchanger SLC25A1 and the zinc transporter SLC39A1 revealed an unexpected role for SLC39A1 in metabolic reprogramming and anti-apoptotic signaling. This full-scale genetic interaction map of human SLC transporters is the backbone for understanding the intricate functional network of SLCs in cellular systems and generates hypotheses for pharmacological target exploitation in cancer and other diseases. The results are available at https://re-solute.eu/resources/dashboards/genomics/ .

The human solute carrier (SLC) superfamily of ~460 membrane transporters remains the largest understudied protein family despite its therapeutic potential. To advance SLC research, we developed a comprehensive knowledgebase that integrates systematic multi-omics data sets with selected curated information from public sources. We annotated SLC substrates through literature curation, compiled SLC disease associations using data mining techniques, and determined the subcellular localization of SLCs by combining annotations from public databases with an immunofluorescence imaging approach. This SLC-centric knowledge is made accessible to the scientific community via a web portal featuring interactive dashboards and visualization tools. Utilizing this systematically collected and curated resource, we computationally derived an integrated functional landscape for the entire human SLC superfamily. We identified clusters with distinct properties and established functional distances between transporters. Based on all available data sets and their integration, we assigned biochemical/biological functions to each SLC, making this study one of the largest systematic annotations of human gene function and a potential blueprint for future research endeavors.

Type 2 diabetes (T2D) presents a global health concern, with evidence highlighting the role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in T2D. Glucose levels, diet, 16S sequences and metadata were collected for 1866 individuals. In addition, microbial community models, and ecological interactions were simulated for the gut microbiomes. Our findings revealed a significant decrease in metabolic fluxes provided by the host's diet to the microbiome in T2D patients, accompanied by increased within-community exchanges. Moreover, the diabetic microbiomes shift towards increased exploitative ecological interactions at the expense of collaborative interactions. The reduced microbiome-to-host butyrate flux, along with decreased fluxes of amino acids (including tryptophan), nucleotides, and B vitamins from the host's diet, further highlight the dysregulation in microbial-host interactions in diabetes. In addition, microbiomes of T2D patients exhibit enrichment in energy metabolism, indicative of increased metabolic activity and antagonism. This study sheds light on the increased microbiome autonomy and antagonism accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validation.

Genome-wide association studies have identified thousands of variants associated with disease risk but the mechanism by which such variants contribute to disease remains largely unknown. Indeed, a major challenge is that variants do not act in isolation but rather in the framework of highly complex biological networks, such as the human metabolic network, which can amplify or buffer the effect of specific risk alleles on disease susceptibility. Here we use genetically predicted reaction fluxes to perform a systematic search for metabolic fluxes acting as buffers or amplifiers of coronary artery disease (CAD) risk alleles. Our analysis identifies 30 risk locus-reaction flux pairs with significant interaction on CAD susceptibility involving 18 individual reaction fluxes and 8 independent risk loci. Notably, many of these reactions are linked to processes with putative roles in the disease such as the metabolism of inflammatory mediators. In summary, this work establishes proof of concept that biochemical reaction fluxes can have non-additive effects with risk alleles and provides novel insights into the interplay between metabolism and genetic variation on disease susceptibility.