Integration of molecular profiles in a longitudinal wellness profiling cohort

Evening chronotype is associated with elevated biomarkers of cardiometabolic risk in the EpiHealth cohort: a cross-sectional study

STUDY OBJECTIVES

Individuals with evening chronotype have a higher risk of cardiovascular and metabolic disorders, although the underlying mechanisms are not well understood. In a population-based cohort, we aimed to investigate the association between chronotype and 242 circulating proteins from three panels of established or candidate biomarkers of cardiometabolic processes.

METHODS

In 2,471 participants (49.7% men, mean age 61.2±8.4 SD years) from the EpiHealth cohort, circulating proteins were analyzed with a multiplex proximity extension technique. Participants self-reported their chronotype on a five-level scale from extreme morning to extreme evening chronotype. With the intermediate chronotype set as the reference, each protein was added as the dependent variable in a series of linear regression models adjusted for confounders. Next, the chronotype coefficients were jointly tested and the resulting p-values adjusted for multiple testing using false discovery rate (5%). For the associations identified, we then analyzed the marginal effect of each chronotype category.

RESULTS

We identified 17 proteins associated with chronotype. Evening chronotype was positively associated with proteins previously linked to insulin resistance and cardiovascular risk, namely retinoic acid receptor protein 2, fatty acid-binding protein adipocyte, tissue-type plasminogen activator, and plasminogen activator inhibitor 1 (PAI-1). Additionally, PAI-1 was inversely associated with the extreme morning chronotype.

CONCLUSIONS

In this population-based study, proteins previously related with cardiometabolic risk were elevated in the evening chronotypes. These results may guide future research in the relation between chronotype and cardiometabolic disorders.

LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy

Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.

Mahalanobis distance, a novel statistical proxy of homeostasis loss is longitudinally associated with risk of type 2 diabetes

Background

The potential role of individual plasma biomarkers in the pathogenesis of type 2 diabetes (T2D) has been broadly studied, but the impact of biomarkers interaction remains underexplored. Recently, the Mahalanobis distance (MD) of plasma biomarkers has been proposed as a proxy of physiological dysregulation. Here we aimed to investigate whether the MD calculated from circulating biomarkers is prospectively associated with development of T2D.

Methods

We calculated the MD of the Principal Components (PCs) integrating the information of 32 circulating biomarkers (comprising inflammation, glycemic, lipid, microbiome and one-carbon metabolism) measured in 6247 participants of the PREVEND study without T2D at baseline. Cox proportional-hazards regression analyses were performed to study the association of MD with T2D development.

Findings

After a median follow-up of 7·3 years, 312 subjects developed T2D. The overall MD (mean (SD)) was higher in subjects who developed T2D compared to those who did not: 35·65 (26·67) and 30.75 (27·57), respectively (P = 0·002). The highest hazard ratio (HR) was obtained using the MD calculated from the first 31 PCs (per 1 log-unit increment) (1·72 (95% CI 1·42,2·07), P < 0·001). Such associations remained after the adjustment for age, sex, plasma glucose, parental history of T2D, lipids, blood pressure medication, and BMI (HR_adj 1·37 (95% CI 1·11,1·70), P = 0·004).

Interpretation

Our results are in line with the premise that MD represents an estimate of homeostasis loss. This study suggests that MD is able to provide information about physiological dysregulation also in the pathogenesis of T2D.

Funding

The Dutch Kidney Foundation (Grant E.033).

Prognostic Impact of MITD1 and Associates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most malignant diseases with poor survival rate over the world. The tumor microenvironment (TME) is highly related to the oncogenesis, development, and prognosis of KIRC. Thus, making the identification of KIRC biomarkers and immune infiltrates critically important. Microtubule Interacting and Trafficking Domain containing 1(MITD1) was reported to participate in cytokinesis of cell division. In the present study, multiple bioinformatics tools and databases were applied to investigate the expression level and clinical value of MITD1 in KIRC. We found that the expression of MITD1 was significantly increased in KIRC tissues. Further, the KIRC patients with high MITD1 levels showed a worse overall survival (OS) rate and disease free survival (DFS) rate. Otherwise, we found a significant correlation MITD1 expression and the abundance of CD8+ T cells. Functional enrichment analyses revealed that immune response and cytokine-cytokine receptor are very critical signaling pathways which associated with MITD1 in KIRC. In conclusion, our findings indicated that MITD1 may be a potential biomarker and associated with immune infiltration in KIRC.

Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

iNetModels 2.0: an interactive visualization and database of multi-omics data

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

iNetModels 2.0: an interactive visualization and database of multi-omics data

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Developmental trajectory of the healthy human gut microbiota during the first 5 years of life

The gut is inhabited by a densely populated ecosystem, the gut microbiota, that is established at birth. However, the succession by which different bacteria are incorporated into the gut microbiota is still relatively unknown. Here, we analyze the microbiota from 471 Swedish children followed from birth to 5 years of age, collecting samples after 4 and 12 months and at 3 and 5 years of age as well as from their mothers at birth using 16S rRNA gene profiling. We also compare their microbiota to an adult Swedish population. Genera follow 4 different colonization patterns during establishment where Methanobrevibacter and Christensenellaceae colonize late and do not reached adult levels at 5 years. These late colonizers correlate with increased alpha diversity in both children and adults. By following the children through age-specific community types, we observe that children have individual dynamics in the gut microbiota development trajectory.

Gut Microbiome over a Lifetime and the Association with Hypertension

PURPOSE OF REVIEW

Microorganisms living within an ecosystem create microbial communities and play key roles in ecosystem functioning. During their lifespan, humans share their bodies with a variety of microorganisms. More than 10-100 trillion symbiotic microorganisms live on and within human beings, and the majority of these microorganisms populate the distal ileum and colon (referred to as the gut microbiota). Interactions between the gut microbiota and the host involve signaling via chemical neurotransmitters and metabolites, neuronal pathways, and the immune system. Hypertension is a complex and heterogeneous pathophenotype. A reductionist approach that assumes that all patients who have the same signs of a disease share a common disease mechanism and thus should be treated similarly is insufficient for optimal blood pressure management. Herein, we have highlighted the contribution of the gut microbiome to blood pressure regulation in humans.

RECENT FINDINGS

Gut dysbiosis-an imbalance in the composition and function of the gut microbiota-has been shown to be associated with hypertension. Gut dysbiosis occurs via environmental pressures, including caesarean section, antibiotic use, dietary changes, and lifestyle changes over a lifetime. This review highlights how gut dysbiosis may affect a host's blood pressure over a lifetime. The review also clarifies future challenges in studies of associations between the gut microbiome and hypertension.

Bead-Based Assays for Validating Proteomic Profiles in Body Fluids

Protein biomarkers in biological fluids represent an important resource for improving the clinical management of diseases. Current proteomics technologies are capable of performing high-throughput and multiplex profiling in different types of fluids, often leading to the shortlisting of tens of candidate biomarkers per study. However, before reaching any clinical setting, these discoveries require thorough validation and an assay that would be suitable for routine analyses. In the path from biomarker discovery to validation, the performance of the assay implemented for the intended protein quantification is extremely critical toward achieving reliable and reproducible results. Development of robust sandwich immunoassays for individual candidates is challenging and labor and resource intensive, and multiplies when evaluating a panel of interesting candidates at the same time. Here we describe a versatile pipeline that facilitates the systematic and parallel development of multiple sandwich immunoassays using a bead-based technology.

Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes

BACKGROUND

Comprehensive proteomics profiling may offer new insights into the dysregulated metabolic milieu of type 2 diabetes, and in the future, serve as a useful tool for personalized medicine. This calls for a better understanding of circulating protein patterns at the early stage of type 2 diabetes as well as the dynamics of protein patterns during changes in metabolic status.

METHODS

To elucidate the systemic alterations in early-stage diabetes and to investigate the effects on the proteome during metabolic improvement, we measured 974 circulating proteins in 52 newly diagnosed, treatment-naïve type 2 diabetes subjects at baseline and after 1 and 3 months of guideline-based diabetes treatment, while comparing their protein profiles to that of 94 subjects without diabetes.

FINDINGS

Early stage type 2 diabetes was associated with distinct protein patterns, reflecting key metabolic syndrome features including insulin resistance, adiposity, hyperglycemia and liver steatosis. The protein profiles at baseline were attenuated during guideline-based diabetes treatment and several plasma proteins associated with metformin medication independently of metabolic variables, such as circulating EPCAM.

INTERPRETATION

The results advance our knowledge about the biochemical manifestations of type 2 diabetes and suggest that comprehensive protein profiling may serve as a useful tool for metabolic phenotyping and for elucidating the biological effects of diabetes treatments.

FUNDING

This work was supported by the Swedish Heart and Lung Foundation, the Swedish Research Council, the Erling Persson Foundation, the Knut and Alice Wallenberg Foundation, and the Swedish state under the agreement between the Swedish government and the county councils (ALF-agreement).