Molecular Systems Biology最新文献

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.

Gastrointestinal adenocarcinoma is a major cancer type for the digestive system, ranking as the top cause of cancer-related deaths worldwide. While there has been extensive research on mutations in protein-coding regions, the knowledge of the landscape of its non-coding regulatory elements is still insufficient. Combining the analysis of active enhancer profiles and genomic structural variation, we discovered and validated a lineage-specific super-enhancer for MYB in gastrointestinal adenocarcinoma. This super-enhancer is composed of a predominant enhancer e4 and several additional enhancers, whose transcriptional activity is regulated by the direct binding of HNF4A and MYB itself. Suppression of the super-enhancer downregulated the expression of MYB, inhibited downstream Notch signaling and prevented the development of gastrointestinal adenocarcinoma both in vitro and in vivo. Our study uncovers a mechanism driven by non-coding variations that regulate MYB expression in a lineage-specific manner, offering new insights into the carcinogenic mechanism and potential therapeutic strategies for gastrointestinal adenocarcinoma.

Solute carrier (SLC) transporters form a protein superfamily that enables transmembrane transport of diverse substrates including nutrients, ions and drugs. There are about 450 different SLCs, residing in a variety of subcellular membranes. Loss-of-function of an unusually high proportion of SLC transporters is genetically associated with a plethora of human diseases, making SLCs a rapidly emerging but challenging drug target class. Knowledge of their protein environment may elucidate the molecular basis for their functional integration with metabolic and cellular pathways and help conceive pharmacological interventions based on modulating proteostatic regulation. We aimed at obtaining a global survey of the SLC-protein interaction landscape and mapped the protein-protein interactions of 396 SLCs by interaction proteomics. We employed a functional assessment based on RNA interference of interactors in combination with measurement of protein stability and localization. As an example, we detail the role of a SLC16A6 phospho-degron and the contributions of PDZ-domain proteins LIN7C and MPP1 to the trafficking of SLC43A2. Overall, our work offers a resource for SLC-protein interactions for the scientific community.

Solute carrier (SLC) transporters govern most of the chemical exchange across cellular membranes and are integral to metabolic regulation, which in turn is linked to cellular function and identity. Despite their key role, individual functions of the SLC superfamily members were not evaluated systematically. We determined the metabolic and transcriptional profiles upon SLC overexpression in knock-out or wild-type isogenic cell backgrounds for 378 SLCs and 441 SLCs, respectively. Targeted metabolomics provided a fingerprint of 189 intracellular metabolites, while transcriptomics offered insights into cellular programs modulated by SLC expression. Beyond the metabolic profiles of 102 SLCs directly related to their known substrates, we identified putative substrates or metabolic pathway connections for 71 SLCs without previously annotated bona fide substrates, including SLC45A4 as a new polyamine transporter. By comparing the molecular profiles, we identified functionally related SLC groups, including some with distinct impacts on osmolyte balancing and glycosylation. The assessment of functionally related human genes presented here may serve as a blueprint for other systematic studies and supports future investigations into the functional roles of SLCs.

RNA and proteins can have diverse isoforms due to post-transcriptional and post-translational modifications. A fundamental question is whether these isoforms are mostly beneficial or the result of noisy molecular processes. To assess the plausibility of these explanations, we developed mathematical models depicting different regulatory architectures and investigated isoform evolution under multiple population genetic regimes. We found that factors beyond selection, such as effective population size and the number of cis-acting loci, significantly influence evolutionary outcomes. We found that sub-optimal phenotypes are more likely to evolve when populations are small and/or when the number of cis-loci is large. We also discovered that opposing selection on cis- and trans-acting loci can constrain adaptation, leading to a non-monotonic relationship between effective population size and optimization. More generally, our models provide a quantitative framework for developing statistical tests to analyze empirical data; as a demonstration of this, we analyzed A-to-I RNA editing levels in coleoids and found these to be largely consistent with non-adaptive explanations.