AutoPhy: Automated phylogenetic identification of novel protein subfamilies.

Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. However, automation of functional clustering based on phylogenetic trees has been challenging and most of it is done manually. Clustering phylogenetic trees usually requires the delineation of tree-based thresholds (e.g., distances), leading to an ad hoc problem. We propose a new phylogenetic clustering approach that identifies clusters without using ad hoc distances or other pre-defined values. Our workflow combines uniform manifold approximation and projection (UMAP) with Gaussian mixture models as a k-means like procedure to automatically group sequences into clusters. We then apply a "second pass" clade identification algorithm to resolve non-monophyletic groups. We tested our approach with several well-curated protein families (outer membrane porins, acyltransferase, and nuclear receptors) and showed our automated methods recapitulated known subfamilies. We also applied our methods to a broad range of different protein families from multiple databases, including Pfam, PANTHER, and UniProt, and to alignments of RNA viral genomes. Our results showed that AutoPhy rapidly generated monophyletic clusters (subfamilies) within phylogenetic trees evolving at very different rates both within and among phylogenies. The phylogenetic clusters generated by AutoPhy resolved misannotations and identified new protein functional groups and novel viral strains.

Data-driven mathematical and visualization approaches for removing rare features for Compositional Data Analysis (CoDA).

Sparse feature tables, in which many features are present in very few samples, are common in big biological data (e.g. metagenomics). Ignoring issues of zero-laden datasets can result in biased statistical estimates and decreased power in downstream analyses. Zeros are also a particular issue for compositional data analysis using log-ratios since the log of zero is undefined. Researchers typically deal with this issue by removing low frequency features, but the thresholds for removal differ markedly between studies with little or no justification. Here, we present CurvCut, an unsupervised data-driven approach with human confirmation for rare-feature removal. CurvCut implements two distinct approaches for determining natural breaks in the feature distributions: a method based on curvature analysis borrowed from thermodynamics and the Fisher-Jenks statistical method. Our results show that CurvCut rapidly identifies data-specific breaks in these distributions that can be used as cutoff points for low-frequency feature removal that maximizes feature retention. We show that CurvCut works across different biological data types and rapidly generates clear visual results that allow researchers to confirm and apply feature removal cutoffs to individual datasets.

Trends in the Clinical Presentation of Primary Rhegmatogenous Retinal Detachments During the First Year of the COVID-19 Pandemic.

PURPOSE: To evaluate the effect of 1 full year of the coronavirus disease 2019 (COVID-19) pandemic on clinical presentation of acute, primary rhegmatogenous retinal detachment (RRD). DESIGN: Single-center, retrospective observational cohort study. METHODS: Patients were divided into 2 cohorts: consecutive patients treated for primary RRD during the COVID-19 pandemic (March 9, 2020, to March 7, 2021; pandemic cohort) and patients treated during the corresponding time in previous year (March 11, 2019, to March 8, 2020; control cohort). MAIN OUTCOME MEASURES: Proportion of patients presenting with macula-involving (mac-off) or macula-sparring (mac-on) RRD. RESULTS: A total of 952 patients in the pandemic cohort and 872 patients in the control cohort were included. Demographic factors were similar. Compared with the control cohort, a significantly greater number of pandemic cohort patients presented with mac-off RRDs ([60.92%] pandemic, [48.17%] control, P = .0001) and primary proliferative vitreoretinopathy ([15.53%] pandemic, [6.9%] control, P = .0001). Pandemic cohort patients (10.81%) had significantly higher rates of lost to follow-up compared with the control cohort (4.43%; P = .0001). Patients new to our clinic demonstrated a significant increase in mac-off RRDs in the pandemic cohort (65.35%) compared with the control cohort (50.40%; P = .0001). Pandemic cohort patients showed worse median final best-corrected visual acuity (0.30 logarithm of the minimum angle of resolution) compared with the control cohort (0.18 logarithm of the minimum angle of resolution; P = .0001). CONCLUSIONS: Patients with primary RRD during the first year of the COVID-19 pandemic were more likely to have mac-off disease, present with primary proliferative vitreoretinopathy, be lost to follow-up, and have worse final best-corrected visual acuity outcomes.

Anaerobic Fungal Mevalonate Pathway Genomic Biases Lead to Heterologous Toxicity Underpredicted by Codon Adaptation Indices.

Anaerobic fungi are emerging biotechnology platforms with genomes rich in biosynthetic potential. Yet, the heterologous expression of their biosynthetic pathways has had limited success in model hosts like E. coli. We find one reason for this is that the genome composition of anaerobic fungi like P. indianae are extremely AT-biased with a particular preference for rare and semi-rare AT-rich tRNAs in E coli, which are not explicitly predicted by standard codon adaptation indices (CAI). Native P. indianae genes with these extreme biases create drastic growth defects in E. coli (up to 69% reduction in growth), which is not seen in genes from other organisms with similar CAIs. However, codon optimization rescues growth, allowing for gene evaluation. In this manner, we demonstrate that anaerobic fungal homologs such as PI.atoB are more active than S. cerevisiae homologs in a hybrid pathway, increasing the production of mevalonate up to 2.5 g/L (more than two-fold) and reducing waste carbon to acetate by ~90% under the conditions tested. This work demonstrates the bioproduction potential of anaerobic fungal enzyme homologs and how the analysis of codon utilization enables the study of otherwise difficult to express genes that have applications in biocatalysis and natural product discovery.

Compositional Data Analysis of Periodontal Disease Microbial Communities.

Periodontal disease (PD) is a chronic, progressive polymicrobial disease that induces a strong host immune response. Culture-independent methods, such as next-generation sequencing (NGS) of bacteria 16S amplicon and shotgun metagenomic libraries, have greatly expanded our understanding of PD biodiversity, identified novel PD microbial associations, and shown that PD biodiversity increases with pocket depth. NGS studies have also found PD communities to be highly host-specific in terms of both biodiversity and the response of microbial communities to periodontal treatment. As with most microbiome work, the majority of PD microbiome studies use standard data normalization procedures that do not account for the compositional nature of NGS microbiome data. Here, we apply recently developed compositional data analysis (CoDA) approaches and software tools to reanalyze multiomics (16S, metagenomics, and metabolomics) data generated from previously published periodontal disease studies. CoDA methods, such as centered log-ratio (clr) transformation, compensate for the compositional nature of these data, which can not only remove spurious correlations but also allows for the identification of novel associations between microbial features and disease conditions. We validated many of the studies' original findings, but also identified new features associated with periodontal disease, including the genera Schwartzia and Aerococcus and the cytokine C-reactive protein (CRP). Furthermore, our network analysis revealed a lower connectivity among taxa in deeper periodontal pockets, potentially indicative of a more "random" microbiome. Our findings illustrate the utility of CoDA techniques in multiomics compositional data analysis of the oral microbiome.

Hydrolysis of untreated lignocellulosic feedstock is independent of S-lignin composition in newly classified anaerobic fungal isolate, Piromyces sp. UH3-1.

BACKGROUND: Plant biomass is an abundant but underused feedstock for bioenergy production due to its complex and variable composition, which resists breakdown into fermentable sugars. These feedstocks, however, are routinely degraded by many uncommercialized microbes such as anaerobic gut fungi. These gut fungi express a broad range of carbohydrate active enzymes and are native to the digestive tracts of ruminants and hindgut fermenters. In this study, we examine gut fungal performance on these substrates as a function of composition, and the ability of this isolate to degrade inhibitory high syringyl lignin-containing forestry residues. RESULTS: We isolated a novel fungal specimen from a donkey in Independence, Indiana, United States. Phylogenetic analysis of the Internal Transcribed Spacer 1 sequence classified the isolate as a member of the genus Piromyces within the phylum Neocallimastigomycota (Piromyces sp. UH3-1, strain UH3-1). The isolate penetrates the substrate with an extensive rhizomycelial network and secretes many cellulose-binding enzymes, which are active on various components of lignocellulose. These activities enable the fungus to hydrolyze at least 58% of the glucan and 28% of the available xylan in untreated corn stover within 168 h and support growth on crude agricultural residues, food waste, and energy crops. Importantly, UH3-1 hydrolyzes high syringyl lignin-containing poplar that is inhibitory to many fungi with efficiencies equal to that of low syringyl lignin-containing poplar with no reduction in fungal growth. This behavior is correlated with slight remodeling of the fungal secretome whose composition adapts with substrate to express an enzyme cocktail optimized to degrade the available biomass. CONCLUSIONS: Piromyces sp. UH3-1, a newly isolated anaerobic gut fungus, grows on diverse untreated substrates through production of a broad range of carbohydrate active enzymes that are robust to variations in substrate composition. Additionally, UH3-1 and potentially other anaerobic fungi are resistant to inhibitory lignin composition possibly due to changes in enzyme secretion with substrate. Thus, anaerobic fungi are an attractive platform for the production of enzymes that efficiently use mixed feedstocks of variable composition for second generation biofuels. More importantly, our work suggests that the study of anaerobic fungi may reveal naturally evolved strategies to circumvent common hydrolytic inhibitors that hinder biomass usage.

[Brain death: attitude and knowledge of medical personnel in a third level hospital]. / Muerte encefálica: actitud y conocimientos del personal médico en un hospital de especialidades.

BACKGROUND: Brain death is defined as the irreversible loss of all functions of the brain, including the brainstem. The objective is to know the attitude and knowledge toward brain death of the medical personnel involved in the process of the organ/tissue transplantation and donation in a third level hospital of Mexico City. METHODS: 67 attending physicians were interviewed with the methodology of pen, paper and a printed questionnaire. They were distributed in two groups: group A, consisting of non-surgical physicians, and group B, which was formed by surgical physicians. It was analyzed the attitude and knowledge of the criteria established in the Ley General de Salud (General Law of Health) of Mexico. Thirty-five men and 32 women (median age 42 years) responded to the survey. RESULTS: More than 90 % of both groups would wish to participate in a brain death course, and they would accept to be potential donors or receptors of transplanted organs. A high percentage knows partially the law on brain death (Ley General de Salud) and clinical procedures. Of the interviewed population, 68 % does not know the standard complementary studies to confirm the diagnosis of brain death. Non-significant differences were observed in the attitude and knowledge of both groups (p = 0.170). CONCLUSION: Physicians must improve their knowledge on brain death.

Introducción: la muerte encefálica se define como el cese irreversible de las funciones de las estructuras neurológicas intracraneales, tanto de los hemisferios cerebrales como del troncoencéfalo. El objetivo es conocer la actitud y los conocimientos que tiene ante la muerte encefálica el personal médico relacionado con el trasplante y la donación de órganos y tejidos en un hospital de tercer nivel de la ciudad de México. Métodos: fueron encuestados 67 médicos con el método de pluma, papel y un cuestionario impreso; se distribuyeron en dos grupos: grupo A no quirúrgicos y grupo B quirúrgicos. Se exploraron la actitud y los conocimientos de los criterios establecidos en la Ley General de Salud en México. Contestaron la encuesta 35 hombres y 32 mujeres, con una mediana de edad de 42 años. Resultados: más del 90 % en ambos grupos desearía participar en un curso-taller de muerte encefálica, así como ser potenciales donadores y receptores de órganos. Un alto porcentaje conoce parcialmente la ley sobre muerte encefálica y los conceptos clínicos. El 68 % de la población encuestada no conoce los estudios complementarios establecidos para confirmar el diagnóstico de muerte encefálica. Al comparar ambos grupos no se encontró diferencia significativa (p = 0.170). Conclusión: el médico debe responsabilizarse más en el dominio de la muerte encefálica.

Validation of a prognostic score for mortality in elderly patients admitted to Intensive Care Unit.

CONTEXT: The performance of a prognostic score must be evaluated prior to being used. The aim of the present study was to evaluate the predictive ability of hospital mortality of Simplified Acute Physiology Score 3 (SAPS 3) score in elderly patients admitted to Intensive Care Units (ICUs). AIMS: The aim of the present study was to evaluate the SAPS 3 score predictive ability of hospital mortality in elderly patients admitted to ICU. SETTINGS AND DESIGN: This study was conducted as a prospective cohort, in two mixed ICUs. PATIENTS AND METHODS: Two hundred and eleven elderly patients were included. INTERVENTIONS: None. We compared the predictive accuracy of SAPS 3 measured at the first hour at ICU and Acute Physiology and Chronic Health Evaluation II (APACHE II) measured with the worst values in the first 24 h at ICU. The patients were followed until hospital discharge. STATISTICAL ANALYSIS USED: Evaluation of discrimination through area under curve receiver operating characteristic (aROC) and calibration by Hosmer-Lemeshow (HL) test. RESULTS: The median age was 68 years. The hospital mortality rate was 35.54%. The mean value of SAPS 3 was 62.54 ± 12.51 and APACHE II was 17.46 ± 6.77. The mortality predicted by APACHE II was 24.98 ± 19.96 and for standard SAPS 3 equation 41.18 ± 22.34. The discrimination for SAPS 3 model was aROC = 0.68 (0.62-0.75) and to APACHE II aROC = 0.70 (0.63-0.78). Calibration: APACHE II with HL 10.127 P = 0.26, and standard SAPS 3 equation HL 7.204 P = 0.51. CONCLUSIONS: In this study, the prognostic model of SAPS 3 was not found to be accurate in predicting mortality in geriatric patients requiring ICU admission.