A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens.

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

Gene regulatory networks in disease and ageing.

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics.

Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data.

Spatially resolved multiomics on the neuronal effects induced by spaceflight in mice.

Impairment of the central nervous system (CNS) poses a significant health risk for astronauts during long-duration space missions. In this study, we employed an innovative approach by integrating single-cell multiomics (transcriptomics and chromatin accessibility) with spatial transcriptomics to elucidate the impact of spaceflight on the mouse brain in female mice. Our comparative analysis between ground control and spaceflight-exposed animals revealed significant alterations in essential brain processes including neurogenesis, synaptogenesis and synaptic transmission, particularly affecting the cortex, hippocampus, striatum and neuroendocrine structures. Additionally, we observed astrocyte activation and signs of immune dysfunction. At the pathway level, some spaceflight-induced changes in the brain exhibit similarities with neurodegenerative disorders, marked by oxidative stress and protein misfolding. Our integrated spatial multiomics approach serves as a stepping stone towards understanding spaceflight-induced CNS impairments at the level of individual brain regions and cell types, and provides a basis for comparison in future spaceflight studies. For broader scientific impact, all datasets from this study are available through an interactive data portal, as well as the National Aeronautics and Space Administration (NASA) Open Science Data Repository (OSDR).

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results.

Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments. To address this, we introduce FLOP, a comprehensive nextflow-based workflow combining methods to perform end-to-end analyses of transcriptomics data. We illustrate FLOP on datasets ranging from end-stage heart failure patients to cancer cell lines. We discovered effects not noticeable at the gene-level, and observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space. Moreover, we performed three benchmarks to evaluate the 12 pipelines included in FLOP, and confirmed that filtering is essential in scenarios of expected moderate-to-low biological signal. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

Molecular causality in the advent of foundation models.

Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.

Long-term outcomes of endoscopic ultrasound-guided pancreatic duct interventions: a single tertiary center experience.

BACKGROUND AND AIMS: Endoscopic ultrasound-guided pancreatic duct intervention (EUS-PDI) is one of the most technically challenging procedures. There remains a knowledge gap due to its rarity. The aim is to report the accumulated EUS-PDI experience in a tertiary center. METHODS: Single-tertiary center, retrospective cohort study of prospectively collected data during the study period, from Jan 2013-June 2021. RESULTS: In total, 14 patients (85% male; mean age, 61years, range:37-81) and 25 EUS-PDI procedures for unsuccessful endoscopic retrograde pancreatography (ERP) were included. Principal etiology was chronic pancreatitis with pancreatic duct obstruction (78%). EUS-guided assisted (colorant and/or guidewire, rendezvous) ERP was performed in 14/25(56%); and transmural drainage in 11procedures, including pancreaticogastrosmy in 9/25(36%) and pancreaticoduodenostomy in 2/25(8%). Overall technical and clinical success was 78.5%(11/14). Three (21%) patients required a second procedure with success in all cases. Two failed cases required surgery. Three (21%) adverse events(AEs) were noted (fever, n=1; perforation n=1; pancreatitis n=1). Patients underwent a median of 58months (range 24-108) follow-up procedures for re-stenting. Spontaneous stent migration was detected in 50% of cases. CONCLUSIONS: EUS-PDI is an effective salvage therapy for unsuccessful ERP, although 21% of patients may still experience AEs. In case of EUS-guided rendezvous failure, it can cross over to a transmural drainage.

Asthma Is Associated With Increased Sickness Absence Among Young Adults.

BACKGROUND: There is limited knowledge about how asthma affects sickness absence in young adulthood. OBJECTIVE: To examine how asthma and different asthma phenotypes affect sickness absence among young adults and potential modifying factors. A secondary aim was to estimate productivity losses related to sickness absence for asthma. METHODS: The study included 2391 participants from the Swedish population-based cohort BAMSE (Barn/Child, Allergy, Milieu, Stockholm, Epidemiology). Information on asthma, asthma phenotypes, and lifestyle factors was collected from questionnaires and clinical examinations at age approximately 24 years (2016-2019). Information on sickness absence for longer than 14 days was obtained from a national register for the years 2020 and 2021. Associations between asthma, asthma phenotypes, and sickness absence were analyzed with logistic regression models adjusted for sex, birth year, education, and overweight status. RESULTS: At age 24 years, 272 (11.4%) fulfilled the definition of asthma. Sickness absence was more common among those with asthma than among those without (15.1% vs 8.7%; P = .001; adjusted odds ratio 1.73; 95% CI, 1.19-2.51). Analyses of asthma phenotypes showed that the association tended to be stronger for persistent asthma, uncontrolled asthma, and asthma in combination with rhinitis; no consistent differences were observed across phenotypes related to allergic sensitization or inflammation. The association tended to be stronger among those with overweight than among those with normal weight. Asthma, especially uncontrolled asthma, was associated with higher productivity losses from sickness absence. CONCLUSIONS: Asthma may be associated with higher sickness absence and productivity losses. Achieving better asthma control and reducing allergic symptoms may reduce sickness absence among individuals with asthma.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Cervical cancer prevention by vaccination: review.

Abstract: Routine use of human papillomavirus (HPV) vaccines is recommended in adolescents under 15 years of age worldwide. Still, effective programs remain suboptimal for several factors, making the WHO strategy to eradicate cervical cancer public health with an uncertain future. Objective: To review the literature on the effectiveness, long-term protection, and safety of HPV vaccination programs and vaccination as adjuvant management. This review aims to describe the current state of vaccination programs and demonstrate the long-term protection and safety of vaccines implemented worldwide targeting adolescent girls, with the most recent published evidence of the three prophylactic HPV vaccines - bivalent (bHPV), quadrivalent (qHPV), and nonavalent (nHPV)-. We mainly focus on publications evaluating efficacy, dosing schemes, and HPV vaccination, as well as studies contributing to the mounting evidence for the real-life effectiveness of prophylactic HPV vaccines from several countries. Findings: Human Papillomavirus vaccination programs have made remarkable strides in preventing HPV-related diseases; countries with robust vaccination efforts have witnessed substantial reductions in HPV-related diseases with a decline in high-grade cervical abnormalities and genital warts (54%-83%). However, global coverage remains uneven, with disparities between high-income (HICs) and low-income countries (LMICs). The long-term efficacy of the available human papillomavirus (HPV) goes up to 9.4 years and continues to be immunogenic and well tolerated with an excellent safety profile. Conclusions and relevance: As these are crucial topics in HPV vaccination, it is essential to establish systems for continued monitoring of vaccine immunogenicity, efficacy, and safety over time.

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

Precarious employment in young adulthood and later alcohol-related morbidity: a register-based cohort study.

OBJECTIVES: The prevalence of precarious employment is increasing, particularly among young adults where less is known about the long-term health consequences. The present study aims to test if being precariously employed in young adulthood is associated with an increased risk of alcohol-related morbidity later in life. METHODS: A register-based cohort study was conducted in Sweden. The Swedish Work, Illness, and Labor-market Participation (SWIP) cohort was used to identify individuals who were aged 27 years between 2000 and 2003 (n=339 403). Information on labour market position (precarious employment, long-term unemployment, substandard employment and standard employment relations) was collected for young people 3 years after graduation from school using nationwide registers. Details about alcohol-related morbidity during a 28-year follow-up period were collected from the National Hospital Discharge Register. Data on sex, age, country of birth, education and previous poor health were also obtained from the registers. RESULTS: Young adults in precarious employment had an increased risk of alcohol-related morbidity compared with individuals of the same age in standard employment (HR 1.43, 95% CI 1.32 to 1.55), after adjusting for several important covariates. A stronger association was found among young men who were precariously employed compared with young women. CONCLUSION: This nationwide register-based study conducted in Sweden with a long-term follow-up suggests that being precariously employed in young adulthood is associated with an increased risk of alcohol-related morbidity later in life.

Changes in the endemic-epidemic pattern of malaria in Colombia, 1978-2021.

BACKGROUND: Malaria is a major global public health issue with varying epidemiologies across countries. In Colombia, it is a priority endemic-epidemic event included in the national public health policy. However, evidence demonstrating nationwide variations in the disease behavior is limited. This study aimed to analyze changes in the levels and distribution of endemic-epidemic malaria transmission in the eco-epidemiological regions of Colombia from 1978 to 1999 and 2000 to 2021. METHODS: We conducted a comprehensive time-series study using official secondary data on malaria-associated morbidity and mortality in Colombia from 1978 to 2021. Temporal-spatial and population variables were analyzed, and the absolute and relative frequency measures of general and regional morbidity and mortality were estimated. RESULTS: We observed an 18% reduction in malaria endemic cases between the two study periods. The frequency and severity of the epidemic transmission of malaria varied less and were comparable across both periods. A shift was observed in the frequency of parasitic infections, with a tendency to match and increase infections by Plasmodium falciparum. The risk of malaria transmission varied significantly among the eco-epidemiological regions during both study periods. This study demonstrated a sustained decrease of 78% in malarial mortality. CONCLUSIONS: Although the endemic components of malaria decreased slightly between the two study periods, the epidemic pattern persisted. There were significant variations in the risk of transmission across the different eco-epidemiological regions. These findings underscore the importance of targeted public health interventions in reducing malarial morbidity and mortality rates in Colombia.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Helper T cell immunity in humans with inherited CD4 deficiency.

CD4+ T cells are vital for host defense and immune regulation. However, the fundamental role of CD4 itself remains enigmatic. We report seven patients aged 5-61 years from five families of four ancestries with autosomal recessive CD4 deficiency and a range of infections, including recalcitrant warts and Whipple's disease. All patients are homozygous for rare deleterious CD4 variants impacting expression of the canonical CD4 isoform. A shorter expressed isoform that interacts with LCK, but not HLA class II, is affected by only one variant. All patients lack CD4+ T cells and have increased numbers of TCRαß+CD4-CD8- T cells, which phenotypically and transcriptionally resemble conventional Th cells. Finally, patient CD4-CD8- αß T cells exhibit intact responses to HLA class II-restricted antigens and promote B cell differentiation in vitro. Thus, compensatory development of Th cells enables patients with inherited CD4 deficiency to acquire effective cellular and humoral immunity against an unexpectedly large range of pathogens. Nevertheless, CD4 is indispensable for protective immunity against at least human papillomaviruses and Trophyrema whipplei.

Safety and effectiveness of isavuconazole in real-life non-neutropenic patients.

OBJECTIVES: Information is scarce on clinical experiences with non-neutropenic patients with invasive fungal infection (IFI) receiving isavuconazole. We aimed to report the safety and effectiveness of this drug as a first-line treatment or rescue in real life. METHODS: A retrospective, observational multicentric study of non-neutropenic patients who received isavuconazole as an IFI treatment at 12 different university hospitals (January 2018-2022). All patients met criteria for proven, probable or possible IFI according to EORTC-MSG. RESULTS: A total of 238 IFIs were treated with isavuconazole during the study period. Combination therapy was administered in 27.7% of cases. The primary IFI was aspergillosis (217, 91.2%). Other IFIs treated with isavuconazole were candidemia (n = 10), mucormycosis (n = 8), histoplasmosis (n = 2), cryptococcosis (n = 2), and others (n = 4). Median time of isavuconazole treatment was 29 days. Only 5.9% (n = 14) of cases developed toxicity, mainly hepatic-related (10 patients, 4.2%). Nine patients (3.8%) had treatment withdrawn. Successful clinical response at 12 weeks was documented in 50.5% of patients. CONCLUSION: Isavuconazole is an adequate treatment for non-neutropenic patients with IFIs. Toxicity rates were low and its effectiveness was comparable to other antifungal therapies previously reported.