Three-dimensional genome architecture persists in a 52,000-year-old woolly mammoth skin sample.

Analyses of ancient DNA typically involve sequencing the surviving short oligonucleotides and aligning to genome assemblies from related, modern species. Here, we report that skin from a female woolly mammoth (†Mammuthus primigenius) that died 52,000 years ago retained its ancient genome architecture. We use PaleoHi-C to map chromatin contacts and assemble its genome, yielding 28 chromosome-length scaffolds. Chromosome territories, compartments, loops, Barr bodies, and inactive X chromosome (Xi) superdomains persist. The active and inactive genome compartments in mammoth skin more closely resemble Asian elephant skin than other elephant tissues. Our analyses uncover new biology. Differences in compartmentalization reveal genes whose transcription was potentially altered in mammoths vs. elephants. Mammoth Xi has a tetradic architecture, not bipartite like human and mouse. We hypothesize that, shortly after this mammoth's death, the sample spontaneously freeze-dried in the Siberian cold, leading to a glass transition that preserved subfossils of ancient chromosomes at nanometer scale.

Lack of p38 activation in T cells increases IL-35 and protects against obesity by promoting thermogenesis.

Obesity is characterized by low-grade inflammation, energy imbalance and impaired thermogenesis. The role of regulatory T cells (Treg) in inflammation-mediated maladaptive thermogenesis is not well established. Here, we find that the p38 pathway is a key regulator of T cell-mediated adipose tissue (AT) inflammation and browning. Mice with T cells specifically lacking the p38 activators MKK3/6 are protected against diet-induced obesity, leading to an improved metabolic profile, increased browning, and enhanced thermogenesis. We identify IL-35 as a driver of adipocyte thermogenic program through the ATF2/UCP1/FGF21 pathway. IL-35 limits CD8+ T cell infiltration and inflammation in AT. Interestingly, we find that IL-35 levels are reduced in visceral fat from obese patients. Mechanistically, we demonstrate that p38 controls the expression of IL-35 in human and mouse Treg cells through mTOR pathway activation. Our findings highlight p38 signaling as a molecular orchestrator of AT T cell accumulation and function.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.

Expression Atlas update: insights from sequencing data at both bulk and single cell level.

Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.

The research landscape concerning environmental factors in neurodevelopmental disorders: Endocrine disrupters and pesticides-A review.

In recent years, environmental epidemiology and toxicology have seen a growing interest in the environmental factors that contribute to the increased prevalence of neurodevelopmental disorders, with the purpose of establishing appropriate prevention strategies. A literature review was performed, and 192 articles covering the topic of endocrine disruptors and neurodevelopmental disorders were found, focusing on polychlorinated biphenyls, polybrominated diphenyl ethers, bisphenol A, and pesticides. This study contributes to analyzing their effect on the molecular mechanism in maternal and infant thyroid function, essential for infant neurodevelopment, and whose alteration has been associated with various neurodevelopmental disorders. The results provide scientific evidence of the association that exists between the environmental neurotoxins and various neurodevelopmental disorders. In addition, other possible molecular mechanisms by which pesticides and endocrine disruptors may be associated with neurodevelopmental disorders are being discussed.

Further decompensation in cirrhosis: Results of a large multicenter cohort study supporting Baveno VII statements.

BACKGROUND AND AIMS: The prognostic weight of further decompensation in cirrhosis is still unclear. We investigated the incidence of further decompensation and its effect on mortality in patients with cirrhosis. APPROACH AND RESULTS: Multicenter cohort study. The cumulative incidence of further decompensation (development of a second event or complication of a decompensating event) was assessed using competing risks analysis in 2028 patients. A 4-state model was built: first decompensation, further decompensation, liver transplant, and death. A cause-specific Cox model was used to assess the adjusted effect of further decompensation on mortality. Sensitivity analyses were performed for patients included before or after 1999. In a mean follow-up of 43 months, 1192 patients developed further decompensation and 649 died. Corresponding 5-year cumulative incidences were 52% and 35%, respectively. The cumulative incidences of death and liver transplant after further decompensation were 55% and 9.7%, respectively. The most common further decompensating event was ascites/complications of ascites. Five-year probabilities of state occupation were 24% alive with first decompensation, 21% alive with further decompensation, 7% alive with a liver transplant, 16% dead after first decompensation without further decompensation, 31% dead after further decompensation, and <1% dead after liver transplant. The HR for death after further decompensation, adjusted for known prognostic indicators, was 1.46 (95% CI: 1.23-1.71) ( p <0.001). The significant impact of further decompensation on survival was confirmed in patients included before or after 1999. CONCLUSIONS: In cirrhosis, further decompensation occurs in ~60% of patients, significantly increases mortality, and should be considered a more advanced stage of decompensated cirrhosis.

Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation.

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

p38γ is essential for cell cycle progression and liver tumorigenesis.

The cell cycle is a tightly regulated process that is controlled by the conserved cyclin-dependent kinase (CDK)-cyclin protein complex1. However, control of the G0-to-G1 transition is not completely understood. Here we demonstrate that p38 MAPK gamma (p38γ) acts as a CDK-like kinase and thus cooperates with CDKs, regulating entry into the cell cycle. p38γ shares high sequence homology, inhibition sensitivity and substrate specificity with CDK family members. In mouse hepatocytes, p38γ induces proliferation after partial hepatectomy by promoting the phosphorylation of retinoblastoma tumour suppressor protein at known CDK target residues. Lack of p38γ or treatment with the p38γ inhibitor pirfenidone protects against the chemically induced formation of liver tumours. Furthermore, biopsies of human hepatocellular carcinoma show high expression of p38γ, suggesting that p38γ could be a therapeutic target in the treatment of this disease.

Inhibition of adenylyl cyclase 8 prevents the upregulation of Orai1 channel, which improves cardiac function after myocardial infarction.

The upregulation of Orai1 and subsequent store-operated Ca2+ entry (SOCE) has been associated with adverse cardiac remodeling and heart failure (HF). However, the mechanism underlying Orai1 upregulation and its role in myocardial infarction remains unclear. Our study investigated the role of Orai1 in activating adenylyl cyclase 8 (AC8) and cyclic AMP (cAMP) response element-binding protein (CREB), as well as its contribution to cardiac dysfunction induced by ischemia and reperfusion (I/R). We found that I/R evoked an increase in the expression of Orai1 and AC8 in rats' hearts, resulting in a substantial rise in diastolic Ca2+ concentration ([Ca2+]i), and reduced ventricular contractions. The expression of Orai1 and AC8 was also increased in ventricular biopsies of post-ischemic HF patients. Mechanistically, we demonstrate that I/R activation of Orai1 stimulated AC8, which produced cAMP and phosphorylated CREB. Subsequently, p-CREB activated the ORAI1 promoter, resulting in Orai1 upregulation and SOCE exacerbation. Intramyocardial administration of AAV9 carrying AC8 short hairpin RNA decreased the expression of AC8, Orai1 and CREB, which restored diastolic [Ca2+]i and improved cardiac contraction. Therefore, our data suggests that the axis composed by Orai1/AC8/CREB plays a critical role in I/R-induced cardiac dysfunction, representing a potential new therapeutic target to limit the progression of the disease toward HF.

The ProteomeXchange consortium at 10 years: 2023 update.

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.

Open-source large language models in action: A bioinformatics chatbot for PRIDE database.

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo-ranking system-based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM-based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open-source (https://github.com/PRIDE-Archive/pride-chatbot).

mzIdentML 1.3.0 - Essential progress on the support of crosslinking and other identifications based on multiple spectra.

The mzIdentML data format, originally developed by the Proteomics Standards Initiative in 2011, is the open XML data standard for peptide and protein identification results coming from mass spectrometry. We present mzIdentML version 1.3.0, which introduces new functionality and support for additional use cases. First of all, a new mechanism for encoding identifications based on multiple spectra has been introduced. Furthermore, the main mzIdentML specification document can now be supplemented by extension documents which provide further guidance for encoding specific use cases for different proteomics subfields. One extension document has been added, covering additional use cases for the encoding of crosslinked peptide identifications. The ability to add extension documents facilitates keeping the mzIdentML standard up to date with advances in the proteomics field, without having to change the main specification document. The crosslinking extension document provides further explanation of the crosslinking use cases already supported in mzIdentML version 1.2.0, and provides support for encoding additional scenarios that are critical to reflect developments in the crosslinking field and facilitate its integration in structural biology. These are: (i) support for cleavable crosslinkers, (ii) support for internally linked peptides, (iii) support for noncovalently associated peptides, and (iv) improved support for encoding scores and the corresponding thresholds.

The lipidomics reporting checklist a framework for transparency of lipidomic experiments and repurposing resource data.

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

Integrated Proteomics Analysis of Baseline Protein Expression in Pig Tissues.

The availability of an increasingly large amount of public proteomics data sets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, a domestic pig, is a model organism relevant for food production and for human biomedical research. Here, we reanalyzed 14 public proteomics data sets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances were compared across different data sets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualization of the protein expression data individually or alongside gene expression data.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows.

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Microalgae and phytase dietary supplementation improved growth and gut microbiota in juvenile European seabass (Dicentrarchus labrax).

Fishmeal and fish oil have been the main sources of protein and fatty acid for aquaculture fish. However, their increasing price and low sustainability have led the aquafeed industry to seek sustainable alternative feedstuffs to meet the nutritional requirements of fish and improve their health and performance. Plant proteins have been successfully used to replace fishery derivatives in aquafeeds, but the presence of anti-nutritional substances is a potential drawback of this approach. Thus, it has been reported that phytate breakdown can be caused by feed supplementation with exogenous phytase. The inclusion of microalgae has been proposed to improve gut functionality in fish fed diets with a high vegetable protein content. The aim of this study was to evaluate the effect on the growth and gut microbiota of European seabass (Dicentrarchus labrax) juveniles of a diet containing a blend of microalgae (Arthrospira platensis and Nannochloropsis gaditana) and different concentrations of phytase. An 83-day feeding trial was conducted, comprising four experimental diets with 2.5% microalgae and 500, 1,000, 2,000, or 10,000 phytase units (FTU)/kg feed and a microalgae- and phytase-free control diet. At the end of the trial, a significantly increased body weight was observed in fish fed the diet with the highest phytase concentration (10,000 FTU/kg) versus controls, although the gut bacterial composition did not differ from controls in alpha or beta diversity with either majority (Weighted UniFrac) or minority bacterial strains (Unweighted UniFrac). In comparison to the control group, the groups fed diets with 1,000 or 2,000 FTU/kg diets had a lower alpha diversity (Shannon's diversity index), while those fed diets with 500 FTU/kg or 1,000 FTU/kg showed distinct clusters in beta diversity (involving minority ASVs). According to these findings, the diet containing the 2.5% microalgae blend with 10,000 FTU/kg may be useful to increase the aquafeed quality and sustain the growth performance of juvenile European seabass.