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We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis—essential in biomedicine—evaluates patients‘ survival rates and the onset of medically relevant events, given their clinical and genetic profiles and genetic predispositions. Existing approaches often require programming expertise or rely on inflexible analysis pipelines, limiting their usability among biomedical researchers. The lack of advanced, user-friendly tools hinders problem solving, limits accessibility for biomedical researchers, and restricts interactive data exploration. Our methodology emphasizes functionality-driven design and modularity, akin to combining LEGO bricks to build tailored visual workflows. We (1) define a minimal set of reusable visualization and modeling components that support common survival analysis tasks, (2) implement interactive visualizations for discovering survival cohorts and their characteristic features, and (3) demonstrate integration within an existing visual analytics platform. We implemented the methodology as an open-source add-on to Orange Data Mining and validated it through use cases ranging from Kaplan–Meier estimation to biomarker discovery. While the framework is generally applicable, we illustrate its value through case studies in cancer research, where survival analysis is of critical importance. The resulting framework illustrates how methodological design can drive intuitive, transparent, and effective survival analysis.

Metagenomics is a powerful tool that allows identifying enzymes with novel properties from the unculturable component of microbiomes. However, thus far only a limited number of laccase or laccase -like enzymes identified through metagenomics has been subsequently biochemically characterized. This work describes the successful bio-mining of bacterial laccase-like enzymes in an acidic bog soil metagenome and the characterization of the first acidobacterial laccase-like multicopper oxidase (LMCO). LMCOs have hitherto been mostly studied in fungi and some have already found applications in diverse industries. However, improved LMCOs are in high demand. Using molecular screening of a small metagenomic library (13,500 clones), a gene encoding a three-domain LMCO (LacM) was detected, showing the highest similarity to putative copper oxidases of Candidatus Solibacter (Acidobacteria). The encoded protein was expressed in Escherichia coli, purified by affinity chromatography and biochemically characterized. LacM oxidized a variety of phenolic substrates, including two standard laccase substrates (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), k cat / k M  = 8.45 s −1  mM −1 ; 2,6-dimethoxyphenol (2,6-DMP), k cat / k M  = 6.42 s −1  mM −1 ), next to L-3,4-dihydroxyphenylalanine (L-DOPA), vanillic acid, syringaldazine, pyrogallol, and pyrocatechol. With respect to the latter two lignin building blocks, LacM showed the highest catalytic activity ( k cat / k M  = 173.6 s −1  mM −1 ) for pyrogallol, with ca. 20% activity preserved even at pH 8.0. The enzyme was thermostable and heat-activated in the interval 40–60 °C, with an optimal activity on ABTS at 50 °C. It was rather stable at high salt concentration (e.g., 34% activity preserved at 500 mM NaCl) and in the presence of organic solvents. Remarkably, LacM decolored azo and triphenylmethane dyes, also in the absence of redox mediators.

Fungal laccases have been used in various fields ranging from processes in wood and paper industries to environmental applications. Although a few bacterial laccases have been characterized in recent years, prokaryotes have largely been neglected as a source of novel enzymes, in part due to the lack of knowledge about the diversity and distribution of laccases within Bacteria. In this work genes for laccase-like enzymes were searched for in over 2,200 complete and draft bacterial genomes and four metagenomic datasets, using the custom profile Hidden Markov Models for two- and three-domain laccases. More than 1,200 putative genes for laccase-like enzymes were retrieved from chromosomes and plasmids of diverse bacteria. In 76% of the genes, signal peptides were predicted, indicating that these bacterial laccases may be exported from the cytoplasm, which contrasts with the current belief. Moreover, several examples of putatively horizontally transferred bacterial laccase genes were described. Many metagenomic sequences encoding fragments of laccase-like enzymes could not be phylogenetically assigned, indicating considerable novelty. Laccase-like genes were also found in anaerobic bacteria, autotrophs and alkaliphiles, thus opening new hypotheses regarding their ecological functions. Bacteria identified as carrying laccase genes represent potential sources for future biotechnological applications.

BackgroundMicrosatellite instability (MSI) has emerged as an important biomarker for guiding treatment decisions in immuno-oncology. The FDA recently approved the use of pembrolizumab in patients with metastatic MSI-high or mismatch repair-deficient (dMMR) solid tumors. Approved assays for MSI, such as Foundation One, typically rely on measuring variants by targeted DNA sequencing. While DNA-based assays are the state of the art today, the precision oncology field is beginning to see new types of biomarkers based on complex signatures like those measured by RNA sequencing, which aim to increase predictive accuracy by better describing disease subtypes. If MSI status could be determined from RNA-seq data, this would enable combining MSI with other biomarker signatures to provide a more comprehensive portrait of the disease state, all from a single sequencing assay. In this study, we developed a RNA-seq variant calling pipeline and used it to characterize MSI in different cancer indications from tumor samples without the need for matched normal samples.MethodsPublicly available data sets that included MSI status were selected for a range of cancer types—colorectal, ovarian, endometrial, and gastric cancer. A bioinformatics pipeline was developed following GATK best practices for calling and quantifying single nucleotide variants (SNVs) and insertion-deletion mutations (INDELs) from RNA-seq data. This pipeline was used to characterize an inventory of frequently altered MSI hotspots in the human transcriptome by filtering for microsatellites from MSI-high patients with high frequency of INDELs across multiple cancers. The RNA-seq variant calling pipeline is available under Apache 3.0 open source license.ResultsThe RNA-seq pipeline was validated by comparing its outputs to the 1000 Genomes Project. Next, the RNA-seq workflow was used to predict MSI status in hundreds of tumor samples representing four cancer types based on tumor INDEL alteration at the cataloged hotspots. We observed >90% deletions than insertions in the MSI-high hotspots, consistent with previously published observations. This method showed comparable performance to established commercially available tests.ConclusionsThis study demonstrated reliable prediction of MSI status using genomic variants called from RNA-seq data. Measuring variation at hundreds of hotspot loci present in different tissues and demographically distinct human cohorts may contribute to more robust and generalizable performance. Further, this method does not require a normal control to estimate the mutational load. Ongoing work aims to evaluate the potential as a pan-cancer diagnostic that can be combined readily with other gene signature biomarkers to maximize clinically actionable insights.

Research background. In recent decades, laccases (p-diphenol-dioxygen oxidoreductases; EC 1.10.3.2) have attracted the attention of researchers due to their wide range of biotechnological and industrial applications. Laccases can oxidize a variety of organic and inorganic compounds, making them suitable as biocatalysts in biotechnological processes. Even though the most traditionally used laccases in the industry are of fungal origin, bacterial laccases have shown an enormous potential given their ability to act on several substrates and in multiple conditions. The present study aims to characterize a plasmid-encoded laccase-like multicopper oxidase (LMCO) from Ochrobactrum sp. BF15, a bacterial strain previously isolated from polluted soil. Experimental approach. We used in silico profile hidden Markov models to identify novel laccase-like genes in Ochrobactrum sp. BF15. For laccase characterization, we performed heterologous expression in Escherichia coli, purification and activity measurement on typical laccase substrates. Results and conclusions. Profile hidden Markov models allowed us to identify a novel LMCO, named Lac80. In silico analysis of Lac80 revealed the presence of three conserved copper oxidase domains characteristic of three-domain laccases. We successfully expressed Lac80 heterologously in E. coli, allowing us to purify the protein for further activity evaluation. Of thirteen typical laccase substrates tested, Lac80 showed lower activity on 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), pyrocatechol, pyrogallol and vanillic acid, and higher activity on 2,6-dimethoxyphenol. Novelty and scientific contribution. Our results show Lac80 as a promising laccase for use in industrial applications. The present work shows the relevance of bacterial laccases and highlights the importance of environmental plasmids as valuable sources of new genes encoding enzymes with potential use in biotechnological processes.

BackgroundIn advanced colorectal cancer (CRC), analysis of the tumor microenvironment (TME) may be useful as a predictive biomarker, particularly supporting the use of immunotherapies and anti-angiogenic therapies.1 The XernaTM TME Panel utilizes RNA sequencing data and machine learning to analyze the angiogenic and immunogenic biology of the TME to classify tumors into four TME subtypes.2 In this study, we investigated the distribution of Xerna TME subtypes and associated genomic alterations in CRC for their potential use in therapy selection.MethodsA total of 336 CRC patient samples underwent testing with the OncoExTraTM assay. This assay utilizes whole-exome, whole-transcriptome sequencing to identify actionable alterations, defined as those with FDA-approved matched therapies in any cancer, with matched clinical trials, or with evidence in cancer guidelines or the literature for possible matched therapies. The whole-transcriptome expression data were analyzed using the Xerna TME Panel to assign each sample to one of four subtypes: Immune Active (IA), Immune Suppressed (IS), Immune Desert (ID) and Angiogenic (A). Biomarker associations were explored.ResultsApproximately half (49.4%) of the patient samples had high (IA+IS) versus low (ID+A) immune subtypes, and 247 (73.5%) harbored targetable alterations associated with an FDA-approved therapy. Several biomarkers were significantly associated (p<0.05) with Xerna subtypes, most of which were over-represented in high immune subtypes (19 of 21), with 13 indicative of defective DNA repair (table 1). Microsatellite instability (MSI-high) and high tumor mutational burden (TMB-high) were detected in 30 (8.9%) and 37 (11.0%) patient samples, with 28 (16.9%) and 33 (19.9%) occurring within high immune subtypes (IA+IS), respectively. Some MSI-high and TMB-high samples occurred in low immune subtypes (ID+A), perhaps indicating a lower propensity for response to ICI therapy. Of note, 138 of 306 (45.1%) MSI-low and 133 of 299 (44.5%) TMB-low samples were in the high immune subtypes, suggestive of possible sensitivity to ICI therapy. Actionable KRAS/NRAS, and BRAF alterations were detected in 162 (48.2%) and 23 (6.8%) patients respectively, though none were significantly associated with TME subtypes.ConclusionsThe Xerna TME Panel classified 49.4% of CRC patients to IA or IS subtypes who may benefit from ICI therapy, including many lacking biomarkers currently used for this therapy decision. Most (73.5%) patients harbored alterations associated with FDA-approved therapies, providing the potential for novel combination therapies.3 These findings warrant further study and clinical validation in CRC patients treated with ICI therapy.ReferencesHuyghe N, Benidovskaya E, Stevens P, Van den Eynde M. Biomarkers of Response and Resistance to Immunotherapy in Microsatellite Stable Colorectal Cancer: Toward a New Personalized Medicine. Cancers (Basel). 2022 Apr 29;14(9):2241. doi: 10.3390/cancers14092241. PMID: 35565369; PMCID: PMC9105843.Uhlik M, Pointing D, Iyer S, Ausec L, Štajdohar M, Cvitkovič R, Žganec M, Culm K, Santos VC, Pytowski B, Malafa M, Liu H, Krieg AM, Lee J, Rosengarten R, Benjamin L. Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers. Front Oncol. 2023 May 12;13:1158345. doi: 10.3389/fonc.2023.1158345. PMID: 37251949; PMCID: PMC10213262.Yang Z, Wu G, Zhang X, Gao J, Meng C, Liu Y, Wei Q, Sun L, Wei P, Bai Z, Yao H, Zhang Z. Current progress and future perspectives of neoadjuvant anti-PD-1/PD-L1 therapy for colorectal cancer. Front Immunol. 2022 Sep 9;13:1001444. doi: 10.3389/fimmu.2022.1001444. PMID: 36159842; PMCID: PMC9501688.Ethics ApprovalThe study was approved by WCG IRB Ethics Board, approval number 20181863.Abstract 219 Table 1Frequency of actionable biomarkers that exhibited a significant association across the Xerna Panel immune subtypes (IA+IS vs A+ID; Fisher’s Exact Test). No correction for multiple comparisons was employed.

Many peatlands in Europe have been subjected to land reclamation and systematic drainage, which have substantially affected nutrient cycles in the soil. This work reviews published studies on microbial processes linked to carbon and nitrogen transformations in the soils of the Ljubljana marsh, a drained peatland positioned close to Ljubljana, the capital of Slovenia. This region is known for its dramatic diversity of animal and plant life, but below ground it hides diverse bacterial and archaeal communities that are highly responsive to environmental changes and make the Ljubljana marsh soils a good source of [N.sub.2]O and C[O.sub.2], and a sink for C[H.sub.4]. Methanogenesis is highly restricted in these soils due to competition for electron donors with iron reducers. In addition, methane is efficiently removed by methanotrophs, which are highly active, especially in the soil layers exposed to the changing water table. Denitrification is limited by electron acceptors and in deeper soil layers also by carbon, which becomes more recalcitrant with depth. Nitrification involves bacterial and archaeal ammonia oxidisers with ammonia oxidation rates being among the highest in the world. Interestingly, ammonia-oxidising Thaumarchaeota in acidic bog soils thrive only on ammonia released through mineralisation of organic matter and are incapable of oxidising added mineral ammonia. The soils of the Ljubljana marsh are rich in bacterial laccase-like genes, which may encode enzymes involved in lignin degradation and are therefore interesting for bioexploitations. Future challenges involve designing studies that will reveal specific physiological functions of phenol oxidases and other enzymes involved in peat transformations and address relations between microbial diversity, function and ecosystem responses to anthropogenic disturbances. Key words: microbial community, microbial diversity, peatland, greenhouse gas, nitrification, denitrification, methanogenesis, methanotrophy, laccase, amoA

Citation: Ausec L, Zakrzewski M, Goesmann A, Schlüter A, Mandic-Mulec I (2012) Correction: Bioinformatic Analysis Reveals High Diversity of Bacterial Genes for Laccase-Like Enzymes. No competing interests declared.

We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis-essential in biomedicine-evaluates patients' survival rates and the onset of medically relevant events, given their clinical and genetic profiles and genetic predispositions. Existing approaches often require programming expertise or rely on inflexible analysis pipelines, limiting their usability among biomedical researchers. The lack of advanced, user-friendly tools hinders problem solving, limits accessibility for biomedical researchers, and restricts interactive data exploration. Our methodology emphasizes functionality-driven design and modularity, akin to combining LEGO bricks to build tailored visual workflows. We (1) define a minimal set of reusable visualization and modeling components that support common survival analysis tasks, (2) implement interactive visualizations for discovering survival cohorts and their characteristic features, and (3) demonstrate integration within an existing visual analytics platform. We implemented the methodology as an open-source add-on to Orange Data Mining and validated it through use cases ranging from Kaplan-Meier estimation to biomarker discovery. While the framework is generally applicable, we illustrate its value through case studies in cancer research, where survival analysis is of critical importance. The resulting framework illustrates how methodological design can drive intuitive, transparent, and effective survival analysis.

Bioinformatics has revealed the presence of putative laccase genes in diverse bacteria, including extremophiles, autotrophs, and, interestingly, anaerobes. Integrity of laccase genes in anaerobes has been questioned, since laccases oxidize a variety of compounds using molecular oxygen as the electron acceptor. The genome of the anaerobe Geobacter metallireducens GS-15 contains five genes for laccase-like multicopper oxidases. In order to show whether one of the predicted genes encodes a functional laccase, the protein encoded by GMET_RS10855 was heterologously expressed in Escherichia coli cells. The His6-tagged enzyme (named GeoLacc) was purified to a large extent in the apoprotein, inactive form: incubation with CuSO4 allowed a 43-fold increase of the specific activity yielding a metallo-enzyme. The purified enzyme oxidized some of the typical laccase substrates, including 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), syringaldazine, and 2,6-dimethoxyphenol (2,6-DMP), along with pyrogallol and K4[Fe(CN)6]. Temperature optimum was 75 °C and pH optimum for ABTS and 2,6-DMP oxidation was ~ 6.0. As observed for other laccases, the enzyme was inhibited by halide anions and was sensitive to increasing concentrations of dimethyl sulfoxide and Tween-80. Notably, GeoLacc possesses a very high affinity for dioxygen: a similar activity was measured performing the reaction at air-saturated or microaerophilic conditions.