Biology subjects

Greco, C.

Publications and source records attributed to Greco, C..

8 recordsLinked to original sources

bioRxiv2026

A histidine-mediated, pendulum-like proton transport mechanism is required for the high catalytic activity of -hydrogenases

[FeFe]-hydrogenases are molecular hydrogen (H2) converting enzymes that employ a hexanuclear iron-complex, the H-cluster, as catalytic cofactor, and a proton transfer pathway (PTP) that allows efficient proton coupled electron transfer (PCET). Recent phylogenetic analyses revealed different groups of [FeFe]-hydrogenases. Although only very few members from other groups have been characterized, current knowledge suggests high catalytic activity is predominantly associated with group A members. Here, we show that metal ions inhibit group A [FeFe]-hydrogenases by binding to three specific residues at the entrance of the PTP. Exchanging residue H565 of Clostridium pasteurianum CpI results in a metal-insensitive protein variant with wildtype like activity. In contrast, exchanging S320 and H569 in CpI, and their counterparts in additional group A [FeFe]-hydrogenases results in enzymes with strongly decreased activities and large overpotential requirements. These features are consistent with important roles of both residues in catalytic proton transfer. CpI structures reveal that H569, locally anchored by E278, can be present in two conformations so that we propose a histidine-dependent pendulum mechanism for exchanging protons between bulk solvent and the PTP, which is well supported by theoretical calculations. By that, our study widens information on how the mobility of histidine, governed by residues in the vicinity, contributes to the fundamental concept of PCET.

biochemistry↗

bioRxiv2026

Direct Binding of Cysteine-367 Thiolate to the Active Site of the -Hydrogenase from Clostridium beijerinckii in the O2-stable State

[FeFe]-hydrogenases are very active biocatalysts for H2 conversion. However, their active site is vulnerable to irreversible degradation initiated by O2 binding at the catalytic iron ion (Fed) of the active center. CbA5H, the [FeFe]-hydrogenases from Clostridium beijerinckii exhibits stability towards oxygen (O2) due to its ability to reversibly enter an inactive state termed Hinact upon contact with O2. We previously proposed that the close distance of approximately 3.1 [A] between the thiol of a nearby cysteine (C367) and the Fed, based on a 2.9 [A] crystal structure of CbA5H in the Hinact state, enables their binding to each other. This binding therefore was suggested to shield the Fed from O2 damage. However, there is currently a lack of evidence to support this hypothesis. Furthermore, density functional theory (DFT) calculations based on a homologous model favored hydroxide as the binding ligand of the Fed over the thiol of C367. In this study, we present the crystal structure of CbA5H in the Hinact state at an improved resolution of 2.15 [A]. The structure reveals a direct binding between the thiol of C367 and the Fed with a distance of approximated 2.77 [A] which is well supported by our DFT calculations based on the new crystallographic data. It is noteworthy that the 2.77 [A] bond distance is strikingly long when compared with other iron-sulfur bonds. This finding may provide a crucial foundation for understanding the rapid reversibility of the Hinact state.

biochemistry↗

bioRxiv2026

Environmental filtering drives cryptic diversity and shifting interaction networks across lake, ephemeral pond, and desiccated microbial mat communities in the Untersee Oasis, East Antarctica

Antarctic lakes and surrounding habitats are natural laboratories for studying microbial ecology in extreme environments, yet assembly and dispersal across these systems remain poorly understood. We analyzed bacterial (16S rRNA) and eukaryotic (18S rRNA) communities across benthic lake mats, pond mats, and desiccated mats around Lake Untersee, East Antarctica. Both domains showed significant environmental differentiation with stochastic assembly dominating, but eukaryotes exhibited stronger habitat-specific structuring. Core microbiomes revealed contrasting strategies: bacteria showed broad colonization with few dominant taxa, while eukaryotes maintained smaller cores anchored by Adineta vaga. Cyanobacterial indicators demonstrated complete strain-level turnover despite uniform phylum dominance. Dispersal patterns diverged strongly: prokaryotes exhibited substantial cross-habitat exchange (28-61%) while eukaryotes showed minimal dispersal (2-16%). Distance-decay relationships revealed significant spatial structuring (bacterial r=0.316-0.70; eukaryotic r=0.43-0.84). Cross-domain networks shifted from facilitative in ponds (65% positive) to competitive under desiccation (54% negative), suggesting resource limitation overrides facilitation at extremes. Functional predictions highlighted habitat specialization: lakes enriched in biogeochemical cycling, desiccated mats in stress tolerance, ponds in CRISPR genes. These findings reveal divergent strategies shaped by environmental filtering, dispersal limitation, and viral interactions, with implications for ecosystem connectivity and resilience. ImportanceAntarctic lake ecosystems serve as natural laboratories for understanding microbial community assembly under extreme conditions, yet connectivity between aquatic and terrestrial habitats remains poorly characterized. This study reveals fundamentally divergent ecological strategies between bacterial and eukaryotic communities across Lake Untersees contrasting environments. While bacteria maintain broad dispersal networks (28-61% cross-habitat exchange) with strain-level habitat specialization, eukaryotes exhibit strong dispersal limitation (2-16% exchange) despite occupying adjacent habitats. Most strikingly, cross-domain interaction networks shift from predominantly facilitative in ephemeral ponds (65% positive associations) to competitive under desiccation stress (54% negative associations), directly challenging the stress gradient hypothesis that predicts cooperation dominates in extreme environments. Combined with habitat-specific functional specialization--from biogeochemical cycling in lakes to stress tolerance in desiccated mats--these findings provide a mechanistic framework for predicting ecosystem connectivity, resilience, and responses to climate-driven habitat transitions in polar regions.

microbiology↗

bioRxiv2026

Designing AI-programmable therapeutics with the EDEN family of foundation models

The ability to interpret, modify, and design DNA has driven many of the most significant advances in modern medicine, from diagnostics, biologics, and vaccines to cell and gene therapies. However, the inherent complexity of biological systems means that most modern medicines are still engineered using bespoke, labor-intensive processes. To address the need for a generalisable and programmable approach to therapeutic design, we introduce the EDEN (environmentally-derived evolutionary network) family of metagenomic foundation models, including a 28 billion parameter model trained on 9.7 trillion nucleotide tokens from BaseData1. This dataset, at the time of training, contained more than 10 billion novel genes from over 1 million new species, and is intentionally enriched for environmental and host-associated metagenomes, phage sequences, and mobile genetic elements, enabling the model to learn from diverse and novel cross-species evolutionary mechanisms and apply them to key challenges in human health. EDEN achieves state-of-the-art performance across a series of predictive and generative genomic and protein benchmarks. To demonstrate the models broad applicability across biology, we evaluate EDENs capacity for programmable therapeutic design by challenging a single architecture to design biological novelty across three distinct therapeutic modalities, disease areas and biological scales: (i) large gene insertion, (ii) antibiotic peptide design, and (iii) microbiome design. First, we demonstrate AI-programmable Gene Insertion (aiPGI), in which EDEN designs de novo large serine recombinases (LSRs) capable of inserting large pieces of DNA at desired target sites in the human genome when prompted only on 30 nucleotides of DNA sequence from the desired target site. In low-N experimental validation, EDEN generated multiple active recombinases for all tested disease-associated genomic loci (ATM, DMD, F9, FANCC, GALC, IDS, P4HA1, PHEX, RYR2, USH2A) and 4 potential safe harbor sites in the human genome. EDEN achieves an overall functional hit rate of 63.2% across diverse DNA prompts when prompted on only 30bp of DNA from outside the training data. 50% of EDEN-generated LSRs were active in human cells, achieving therapeutically relevant levels of CAR insertion in primary human T cells. We also show that EDEN can generate active bridge recombinases when prompted on the associated guide RNA alone, with sequence identities to training and public data as low as 65%. These results pave the way for a new generation of cell and gene therapies by opening the door to rapid, programmable and site-specific integration of large genetic payloads without double-strand breaks. This offers an alternative to the safety, efficiency and payload limitations inherent in viral or nuclease-based editing at thousands of currently intractable human therapeutic targets. Second, we use the same model to generate a focused low-N library of novel antimicrobial peptides where 97% showed activity, with top candidates achieving single-digit micromolar potency against critical-priority multidrug-resistant pathogens. Third, to demonstrate that EDEN captures inter-genomic features, we design a gigabase-scale microbiome with over 94,000 synthetic metagenomic assemblies, including prophage genomes and correct cross-species metabolic pathway completions. The EDEN-generated synthetic microbiome covers 9,067 species with a biome-specific taxonomic accuracy of 99%. Over 1,500 of the generated species were outside the fine-tuning dataset while retaining the correct microecological properties and biome association, thus significantly expanding genetic and taxonomic diversity. Together, these results establish a new strategic direction for AI-programmable therapeutics, in which a single foundation model architecture designs candidate therapeutics across diverse modalities and disease areas. This suggests that the combination of billions of years of evolutionary data with specific therapeutic records offers a clear, scaling-driven path to making therapeutic design a predictable engineering discipline. O_FIG O_LINKSMALLFIG WIDTH=141 HEIGHT=200 SRC="FIGDIR/small/699009v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@68c20borg.highwire.dtl.DTLVardef@19b8e8corg.highwire.dtl.DTLVardef@1ab9362org.highwire.dtl.DTLVardef@1592cb7_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Breaking Through Biology's Data Wall: Expanding the Known Tree of Life by Over 10x using a Global Biodiscovery Pipeline

Advancements in the life sciences have always been built upon our collective understanding of life on Earth. Now, the rise of generative biology - the use of AI foundation models to design, generate, and annotate proteins, pathways and therapeutics - is creating unprecedented demand for large, diverse biological sequence datasets. While a limited subset of such data can be generated in clinical or laboratory settings, the vast majority of the training data for unsupervised models must be sourced from the natural world - the product of nearly four billion years of evolutionary history. However, the public databases that currently supply this data, while foundational to research, were established to aggregate results from academic experiments, not as training datasets for machine learning. Their human-centric data structure limits model performance due to redundancy, taxonomic and geographic bias, limited biological context, and inconsistent provenance. With 68% of all sequence data in the SRA database coming from just 5 species, this is one of the most severe class imbalance problems ever encountered in AI. Legal and infrastructural constraints further exacerbate this bottleneck. To address these limitations and support scalable model training, we introduce BaseData: the largest and fastest-growing biological sequence database ever built, and the first purpose-built for training foundation models. As of late 2024, BaseData contained 9.8 billion novel genes, representing more than a 10-fold expansion in known protein diversity after accounting for redundancy. BaseData also contains more than 1 million species not represented in other genomic databases. Its partnership-driven data supply chain across 26 countries and autonomous regions enables growth of up to 2 billion novel genes per month, far exceeding public repositories. All data is collected under benefit sharing agreements using standardized protocols and structured using graph-based, ontology-rich metadata that preserves evolutionary context. BaseData represents a new, ethically-grounded infrastructure for training biological foundation models, complementing public efforts and enabling the next era of generative biology. Short AbstractProgress of AI in biology is now being limited by the availability of high-quality biological sequence data from nature. To get past this data wall, we introduce BaseData, a new biological database built on top of a global, scalable biodiscovery pipeline. As of 2024, BaseData had already expanded the known protein universe by over 10x.

genomics↗

bioRxiv2022

Interrogating Metabolic Interactions Between Skeletal Muscle and Liver Circadian Clocks In Vivo

Expressed throughout the body, the circadian clock system achieves daily metabolic homeostasis at every level of physiology, with clock disruption associated with metabolic disease (1, 2). Molecular clocks present in the brain, liver, adipose, pancreas and skeletal muscle each contribute to glucose homeostasis (3). However, it is unclear; 1) which organ clocks provide the most essential contributions, and 2) if these contributions depend on inter-organ communication. We recently showed that the liver clock alone is insufficient for most aspects of daily liver glucose handling and requires connections with other clocks (4). Considering the pathways that link glucose metabolism between liver and skeletal muscle, we sought to test whether a clock connection along this axis is important. Using our previous published methodology for tissue-specific rescue of Bmal1 in vivo (4, 5), we now show that in the absence of feeding-fasting cycles, liver and muscle clocks are not sufficient for systemic glucose metabolism, nor do they form a functional connection influencing local glucose handling or daily transcriptional rhythms in each tissue. However, the introduction of a daily feeding-fasting rhythm enables a synergistic state between liver and muscle clocks that leads to restoration of systemic glucose tolerance. These findings reveal limited autonomous capabilities of liver and muscle clocks and highlight the need for inter-organ clock communication for glucose homeostasis which involves at least two peripheral metabolic organs.

cell biology↗

bioRxiv2021

Absence of severe COVID-19 in patients with clonal mast cells activation disorders: effective anti-SARS-CoV-2 immune response.

Mast cells are key actors of innate immunity and Th2 adaptive immune response which counterbalance Th1 response, critical for anti-viral immunity. Clonal Mast Cells Activation Disorders (cMCADs) such as mastocytosis and clonal mast cells activation syndrome are characterized by an abnormal mast cells accumulation and/or activation. No data have been published on the anti-viral immune response of patients with cMCADs. The aims of the study were to collected, in a comprehensive way, outcomes of cMCADs patients who experienced a biologically-proven COVID-19 and to characterize both anti-endemic coronaviruses and specific anti-SARS-CoV-2 immune responses in these patients. Clinical follow-up and outcome data were collected prospectively for one year within the French rare disease network CEREMAST encompassing patients from all over the country. Anti-SARS-CoV-2 and anti-endemic coronaviruses specific T-cells were assessed with an enzyme-linked immunospot assay (EliSpot) and anti-SARS-CoV-2 humoral response with dosage of circulating levels of specific IgG, IgA and neutralizing antibodies. Overall, 32 cMCADs patients were identified. None of them required non-invasive or mechanical ventilation; two patients were hospitalized to receive oxygen and steroid therapy. In 21 patients, a characterization of the SARS-CoV-2-specific immune response has been performed. A majority of patients showed a high proportion of circulating SARS-CoV-2-specific interferon (IFN)-{gamma} producing T-cells and high levels of anti-Spike IgG antibodies with neutralizing activity. In addition, no defects in anti-endemic coronaviruses responses were found in patients with cMCADs compared to non-cMCADs controls. Patients with cMCADs frequently showed a spontaneous IFN-{gamma} T-cell production in absence of any stimulation that correlated with circulating basal tryptase levels, a marker of mast cells burden. These findings underscore that patients with cMCADs might be not at risk of severe COVID-19 and the spontaneous IFN-{gamma} production might explain this observation. Author SummaryMast cells are immune cells involved in many biological processes including the anti-microbial response. However, previous studies suggest that mast cells may have a detrimental role in the response against viruses such as SARS-CoV-2, responsible for COVID-19. When a mutation occurs in mast cells, it can lead to a group of diseases called clonal mast cells activation disorders (cMCADs), characterized by deregulated activation of these cells. Hence, patients with cMCADs might be more susceptible to severe COVID-19 than general population. We therefore conducted a 1-year study in France to collect data from all cMCADs patients included in the CEREMAST rare disease French network and who experienced COVID-19. Interestingly, we did not find any severe COVID-19 (i.e. requiring non-invasive or mechanical ventilation) in spite of well-known risk factors for severe COVID-19 in a part of cMCADs patients. We then have studied the immune response against SARS-CoV-2 and other endemic coronaviruses in these patients. We did not observe any abnormalities in the immune response either at the level of T and B lymphocytes. These findings underscore that these patients might not be at risk of severe COVID-19 as one might have feared.

immunology↗

bioRxiv2020

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm Based BCI Learning

Sensorimotor rhythm (SMR) based brain-computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery (MI). Despite the non-invasiveness, ease of use and low cost, this kind of BCI has limitation due to long training times and BCI inefficiency-- where a subpopulation cannot generate decodable EEG signals to perform the control task. Meditation is a mental training method to improve mindfulness and awareness, and is reported to have a positive effect on ones mental state. Here we investigate the behavioral and electrophysiological differences between experienced meditators and meditation naive subjects in 1-dimensional and 2-dimensional cursor control tasks. We found that within subjects who have room for improvement, meditators outperformed control subjects in both tasks, and there were fewer BCI insufficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups, and showed that meditators had higher SMR predictor and were better able to generate decodable EEG signals to achieve SMR BCI control.

bioengineering↗