Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors.

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery.

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

Cell-free fetal DNA for genetic evaluation in Copenhagen Pregnancy Loss Study (COPL): a prospective cohort study.

BACKGROUND: One in four pregnancies end in a pregnancy loss. Although the effect on couples is well documented, evidence-based treatments and prediction models are absent. Fetal aneuploidy is associated with a higher chance of a next successful pregnancy compared with euploid pregnancy loss in which underlying maternal conditions might be causal. Ploidy diagnostics are therefore advantageous but challenging as they require collection of the pregnancy tissue. Cell-free fetal DNA (cffDNA) from maternal blood has the potential for evaluation of fetal ploidy status, but no large-scale validation of the method has been done. METHODS: In this prospective cohort study, women with a pregnancy loss were recruited as a part of the Copenhagen Pregnancy Loss (COPL) study from three gynaecological clinics at public hospitals in Denmark. Women were eligible for inclusion if older than 18 years with a pregnancy loss before gestational age 22 weeks (ie, 154 days) and with an intrauterine pregnancy confirmed by ultrasound (including anembryonic sac), and women with pregnancies of unknown location or molar pregnancies were excluded. Maternal blood was collected while pregnancy tissue was still in situ or within 24 h after pregnancy tissue had passed and was analysed by genome-wide sequencing of cffDNA. Direct sequencing of the pregnancy tissue was done as reference. FINDINGS: We included 1000 consecutive women, at the time of a pregnancy loss diagnosis, between Nov 12, 2020, and May 1, 2022. Results from the first 333 women with a pregnancy loss (recruited between Nov 12, 2020, and Aug 14, 2021) were used to evaluate the validity of cffDNA-based testing. Results from the other 667 women were included to evaluate cffDNA performance and result distribution in a larger cohort of 1000 women in total. Gestational age of fetus ranged from 35-149 days (mean of 70·5 days [SD 16·5], or 10 weeks plus 1 day). The cffDNA-based test had a sensitivity for aneuploidy detection of 85% (95% CI 79-90) and a specificity of 93% (95% CI 88-96) compared with direct sequencing of the pregnancy tissue. Among 1000 cffDNA-based test results, 446 (45%) were euploid, 405 (41%) aneuploid, 37 (4%) had multiple aneuploidies, and 112 (11%) were inconclusive. 105 (32%) of 333 women either did not manage to collect the pregnancy tissue or collected a sample classified as unknown tissue giving a high risk of being maternal. INTERPRETATION: This validation of cffDNA-based testing in pregnancy loss shows the potential and feasibility of the method to distinguish euploid and aneuploid pregnancy loss for improved clinical management and benefit of future reproductive medicine and women's health research. FUNDING: Ole Kirks Foundation, BioInnovation Institute Foundation, and the Novo Nordisk Foundation.

Microbiome and metabolome features of the cardiometabolic disease spectrum.

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism.

OBJECTIVES: Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. DESIGN: We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. RESULTS: Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. CONCLUSION: Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity. TRIAL REGISTRATION NUMBER: NCT02059538.

Exploring Gut Microbiota in Patients with Colorectal Disease Based on 16S rRNA Gene Amplicon and Shallow Metagenomic Sequencing.

The gastrointestinal tract, the largest human microbial reservoir, is highly dynamic. The gut microbes play essential roles in causing colorectal diseases. In the present study, we explored potential keystone taxa during the development of colorectal diseases in central China. Fecal samples of some patients were collected and were allocated to the adenoma (Group A), colorectal cancer (Group C), and hemorrhoid (Group H) groups. The 16S rRNA amplicon and shallow metagenomic sequencing (SMS) strategies were used to recover the gut microbiota. Microbial diversities obtained from 16S rRNA amplicon and SMS data were similar. Group C had the highest diversity, although no significant difference in diversity was observed among the groups. The most dominant phyla in the gut microbiota of patients with colorectal diseases were Bacteroidetes, Firmicutes, and Proteobacteria, accounting for >95% of microbes in the samples. The most abundant genera in the samples were Bacteroides, Prevotella, and Escherichia/Shigella, and further species-level and network analyses identified certain potential keystone taxa in each group. Some of the dominant species, such as Prevotella copri, Bacteroides dorei, and Bacteroides vulgatus, could be responsible for causing colorectal diseases. The SMS data recovered diverse antibiotic resistance genes of tetracycline, macrolide, and beta-lactam, which could be a result of antibiotic overuse. This study explored the gut microbiota of patients with three different types of colorectal diseases, and the microbial diversity results obtained from 16S rRNA amplicon sequencing and SMS data were found to be similar. However, the findings of this study are based on a limited sample size, which warrants further large-scale studies. The recovery of gut microbiota profiles in patients with colorectal diseases could be beneficial for future diagnosis and treatment with modulation of the gut microbiota. Moreover, SMS data can provide accurate species- and gene-level information, and it is economical. It can therefore be widely applied in future clinical metagenomic studies.

Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma.

BACKGROUNDIdentifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which gut microbial features are applicable across different patient cohorts has not been extensively explored.METHODSWe performed a meta-analysis of 4 published shotgun metagenomic studies (Ntot = 130 patients) investigating differential microbiome composition and imputed metabolic function between responders and nonresponders to ICI.RESULTSOur analysis identified both known microbial features enriched in responders, such as Faecalibacterium as the prevailing taxa, as well as additional features, including overrepresentation of Barnesiella intestinihominis and the components of vitamin B metabolism. A classifier designed to predict responders based on these features identified responders in an independent cohort of 27 patients with the area under the receiver operating characteristic curve of 0.625 (95% CI: 0.348-0.899) and was predictive of prognosis (HR = 0.35, P = 0.081).CONCLUSIONThese results suggest the existence of a fecal microbiome signature inherent across responders that may be exploited for diagnostic or therapeutic purposes.FUNDINGThis work was funded by the Knut and Alice Wallenberg Foundation, BioGaia AB, and Cancerfonden.

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment.

Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics.

Logical transformation of genome-scale metabolic models for gene level applications and analysis.

MOTIVATION: In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of gene-reaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites. RESULTS: Here, we propose a framework named logic transformation of model (LTM) that is able to simplify the gene-reaction associations and enables integration with other developed methods for gene level applications. We show that the transformed GEMs have increased reaction and metabolite number as well as degree of freedom in flux balance analysis, but the gene-reaction associations and the main features of flux distributions remain constant. In addition, we develop two methods, OptGeneKnock and FastGeneSL by combining LTM with previously developed reaction-based methods. We show that the FastGeneSL outperforms exhaustive search. Finally, we demonstrate the use of the developed methods in two different case studies. We could design fast genetic intervention strategies for targeted overproduction of biochemicals and identify double and triple synthetic lethal gene sets for inhibition of hepatocellular carcinoma tumor growth through the use of OptGeneKnock and FastGeneSL, respectively. AVAILABILITY AND IMPLEMENTATION: Source code implemented in MATLAB, RAVEN toolbox and COBRA toolbox, is public available at https://sourceforge.net/projects/logictransformationofmodel.

Insights into the pathways of iron- and sulfur-oxidation, and biofilm formation from the chemolithotrophic acidophile Acidithiobacillus ferrivorans CF27.

The iron-oxidizing acidithiobacilli cluster into at least four groups, three of which (Acidithiobacillus ferrooxidans, Acidithiobacillus ferridurans and Acidithiobacillus ferrivorans) have been designated as separate species. While these have many physiological traits in common, they differ in some phenotypic characteristics including motility, and pH and temperature minima. In contrast to At. ferrooxidans and At. ferridurans, all At. ferrivorans strains analysed to date possess the iro gene (encoding an iron oxidase) and, with the exception of strain CF27, the rusB gene encoding an iso-rusticyanin whose exact function is uncertain. Strain CF27 differs from other acidithiobacilli by its marked propensity to form macroscopic biofilms in liquid media. To identify the genetic determinants responsible for the oxidation of ferrous iron and sulfur and for the formation of extracellular polymeric substances, the genome of At. ferrivorans CF27 strain was sequenced and comparative genomic studies carried out with other Acidithiobacillus spp.. Genetic disparities were detected that indicate possible differences in ferrous iron and reduced inorganic sulfur compounds oxidation pathways among iron-oxidizing acidithiobacilli. In addition, strain CF27 is the only sequenced Acidithiobacillus spp. to possess genes involved in the biosynthesis of fucose, a sugar known to confer high thickening and flocculating properties to extracellular polymeric substances.