Database resources of the National Center for Biotechnology Information

CAIM: coverage-based analysis for identification of microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Sufficient water intake maintains the gut microbiota and immune homeostasis and promotes pathogen elimination

Water is the most abundant substance in the human body and plays a pivotal role in various bodily functions. While underhydration is associated with the incidence of certain diseases, the specific role of water in gut function remains largely unexplored. Here, we show that water restriction disrupts gut homeostasis, which is accompanied by a bloom of gut microbes and decreased numbers of immune cells, especially Th17 cells, within the colon. These microbial and immunological changes in the gut are associated with an impaired ability to eliminate the enteric pathogen Citrobacter rodentium. Moreover, aquaporin 3, a water channel protein, is required for the maintenance of Th17 cell function and differentiation. Taken together, adequate water intake is critical for maintaining bacterial and immunological homeostasis in the gut, thereby enhancing host defenses against enteric pathogens.

CAIM: Coverage-based Analysis for Identification of Microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Farnesoid X receptor prevents neutrophil extracellular traps via reduced sphingosine-1-phosphate in chronic kidney disease

Farnesoid X receptor (FXR) activation reduces renal inflammation, but the underlying mechanisms remain elusive. Neutrophil extracellular traps (NETs) are webs of DNA formed when neutrophils undergo specialized programmed cell death (NETosis). The signaling lipid sphingosine-1-phosphate (S1P) stimulates NETosis via its receptor on neutrophils. Here, we identify FXR as a negative regulator of NETosis via repressing S1P signaling. We determined the effects of the FXR agonist obeticholic acid (OCA) in mouse models of adenosine phosphoribosyltransferase (APRT) deficiency and Alport syndrome, both genetic disorders that cause chronic kidney disease. Renal FXR activity is greatly reduced in both models, and FXR agonism reduces disease severity. Renal NETosis and sphingosine kinase 1 (Sphk1) expression are increased in diseased mice, and they are reduced by OCA in both models. Genetic deletion of FXR increases Sphk1 expression, and Sphk1 expression correlates with NETosis. Importantly, kidney S1P levels in Alport mice are two-fold higher than controls, and FXR agonism restores them back to baseline. Short-term inhibition of sphingosine synthesis in Alport mice with severe kidney disease reverses NETosis, establishing a causal relationship between S1P signaling and renal NETosis. Finally, extensive NETosis is present in human Alport kidney biopsies (six male, nine female), and NETosis severity correlates with clinical markers of kidney disease. This suggests the potential clinical relevance of the newly identified FXR-S1P-NETosis pathway. In summary, FXR agonism represses kidney Sphk1 expression. This inhibits renal S1P signaling, thereby reducing neutrophilic inflammation and NETosis.NEW & NOTEWORTHY Many preclinical studies have shown that the farnesoid X receptor (FXR) reduces renal inflammation, but the mechanism is poorly understood. This report identifies FXR as a novel regulator of neutrophilic inflammation and NETosis via the inhibition of sphingosine-1-phosphate signaling. Additionally, NETosis severity in human Alport kidney biopsies correlates with clinical markers of kidney disease. A better understanding of this signaling axis may lead to novel treatments that prevent renal inflammation and chronic kidney disease.

Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach

Introduction

NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues.

Methods

In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function.

Results

Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients.

Discussion

This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

Challenges and opportunities of big data analytics in healthcare

Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.

An In Silico Bioremediation Study to Identify Essential Residues of Metallothionein Enhancing the Bioaccumulation of Heavy Metals in Pseudomonas aeruginosa

Microorganisms are ubiquitously present in the environment and exert significant influence on numerous natural phenomena. The soil and groundwater systems, precipitation, and effluent outfalls from factories, refineries, and waste treatment facilities are all sources of heavy metal contamination. For example, Madinah, Saudi Arabia, has alarmingly high levels of lead and cadmium. The non-essential minerals cadmium (Cd) and lead (Pb) have been linked to damage to vital organs. Bioremediation is an essential component in the process of cleaning up polluted soil and water where biological agents such as bacteria are used to remove the contaminants. It is demonstrated that Pseudomonas aeruginosa (P. aeruginosa) isolated from activated sludge was able to remove Cd and Pb from water. The protein sequence of metallothionein from P. aeruginosa was retrieved to explore it for physicoparameters, orthologs, domain, family, motifs, and conserved residues. The homology structure was generated, and models were validated. Docking of the best model with the heavy metals was carried out to inspect the intramolecular interactions. The target protein was found to belong to the "metallothionein_pro" family, containing six motifs, and showed a close orthologous relationship with other heavy metal-resistant bacteria. The best model was generated by Phyre2. In this study, three key residues of metallothionein were identified that participate in heavy metal (Pb and Cd) binding, viz., Ala33, Ser34, and Glu59. In addition, the study provides an essential basis to explore protein engineering for the optimum use of metallothionein protein to reduce/remove heavy metals from the environment.

CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 - 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.

Dietary Supplementation of Caulerpa racemosa Ameliorates Cardiometabolic Syndrome via Regulation of PRMT-1/DDAH/ADMA Pathway and Gut Microbiome in Mice

This study evaluated the effects of an aqueous extract of Caulerpa racemosa (AEC) on cardiometabolic syndrome markers, and the modulation of the gut microbiome in mice administered a cholesterol- and fat-enriched diet (CFED). Four groups of mice received different treatments: normal diet, CFED, and CFED added with AEC extract at 65 and 130 mg/kg body weight (BW). The effective concentration (EC₅₀) values of AEC for 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and lipase inhibition were lower than those of the controls in vitro. In the mice model, the administration of high-dose AEC showed improved lipid and blood glucose profiles and a reduction in endothelial dysfunction markers (PRMT-1 and ADMA). Furthermore, a correlation between specific gut microbiomes and biomarkers associated with cardiometabolic diseases was also observed. In vitro studies highlighted the antioxidant properties of AEC, while in vivo data demonstrated that AEC plays a role in the management of cardiometabolic syndrome via regulation of oxidative stress, inflammation, endothelial function (PRMT-1/DDAH/ADMA pathway), and gut microbiota.

Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach

The dramatic increase in the number of microbe descriptions in databases, reports, and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis. Recent text mining methods offer powerful ways to extract textual information and generate ontology-based representation. This paper describes the design of the Omnicrobe application that gathers comprehensive information on habitats, phenotypes, and usages of microbes from scientific sources of high interest to the microbiology community. The Omnicrobe database contains around 1 million descriptions of microbe properties. These descriptions are created by analyzing and combining six information sources of various kinds, i.e. biological resource catalogs, sequence databases and scientific literature. The microbe properties are indexed by the Ontobiotope ontology and their taxa are indexed by an extended version of the taxonomy maintained by the National Center for Biotechnology Information. The Omnicrobe application covers all domains of microbiology. With simple or rich ontology-based queries, it provides easy-to-use support in the resolution of scientific questions related to the habitats, phenotypes, and uses of microbes. We illustrate the potential of Omnicrobe with a use case from the food innovation domain.

Genome Mining as an Alternative Way for Screening the Marine Organisms for Their Potential to Produce UV-Absorbing Mycosporine-like Amino Acid

Mycosporine-like amino acids (MAAs) are small molecules with robust ultraviolet (UV)-absorbing capacities and a huge potential to be used as an environmentally friendly natural sunscreen. MAAs, temperature, and light-stable compounds demonstrate powerful photoprotective capacities and the ability to capture light in the UV-A and UV-B ranges without the production of damaging free radicals. The biotechnological uses of these secondary metabolites have been often limited by the small quantities restored from natural resources, variation in MAA expression profiles, and limited success in heterologous expression systems. Overcoming these obstacles requires a better understanding of MAA biosynthesis and its regulatory processes. MAAs are produced to a certain extent via a four-enzyme pathway, including genes encoding enzymes dehydroquinate synthase, enzyme O-methyltransferase, adenosine triphosphate grasp, and a nonribosomal peptide synthetase. However, there are substantial genetic discrepancies in the MAA genetic pathway in different species, suggesting further complexity of this pathway that is yet to be fully explored. In recent years, the application of genome-mining approaches allowed the identification of biosynthetic gene clusters (BGCs) that resulted in the discovery of many new compounds from unconventional sources. This review explores the use of novel genomics tools for linking BGCs and secondary metabolites based on the available omics data, including MAAs, and evaluates the potential of using novel genome-mining tools to reveal a cryptic potential for new bioproduct screening approaches and unrevealing new MAA producers.

Ectopic Colonization and Immune Landscapes of Periodontitis Microbiota in Germ-Free Mice With Streptozotocin-Induced Type 1 Diabetes Mellitus

A two-way relationship between diabetes and periodontitis has been discussed recently. Periodontitis microbiota might affect the immune homeostasis of diabetes, but the molecular mechanism of their interactions is still not clear. The aims of this study were to clarify the possible immune regulatory effects of periodontitis microbiota on diabetes and the correlation between immunomodulation and ectopic colonization. A model of germ-free mice with streptozotocin-induced type 1 diabetes mellitus (T1D), which was orally inoculated with mixed saliva samples for 2 weeks, was used in this study. Those mice were randomly divided into two groups, namely, SP (where the T1D mice were orally inoculated with mixed saliva samples from periodontitis patients) and SH (where the T1D mice were orally inoculated with mixed saliva samples from healthy subjects). Ectopic colonization of saliva microbiota was assessed using culture-dependent method and Sanger sequencing, and the composition of gut microbiota was analyzed using 16S rRNA gene sequencing. Changes in 15 types of immune cells and six cytokines either from the small intestine or spleen were detected by multicolor flow cytometry. The correlation between gut microbiota and immune cells was evaluated by redundancy analysis. Although periodontitis microbiota minorly colonized the lungs, spleens, and blood system, they predominantly colonized the gut, which was mainly invaded by Klebsiella. SH and SP differed in beta diversity of the gut bacterial community. Compared to SH, microbial alteration in small intestine occurred with an increase of Lacticaseibacillus, Bacillus, Agathobacter, Bacteroides, and a decrease of Raoultella in SP. More types of immune cells were disordered in the spleen than in the small intestine by periodontitis microbiota, mainly with a dramatical increase in the proportion of macrophages, plasmacytoid dendritic cells (pDCs), monocytes, group 3 innate lymphoid cells, CD4-CD8- T cells and Th17 cells, as well as a decline of αβT cells in SP. Cytokines of IFNγ, IL17, and IL22 produced by CD4 + T cells as well as IL22 produced by ILCs of small intestine rose in numbers, and the intestinal and splenic pDCs were positively regulated by gut bacterial community in SP. In conclusion, periodontitis microbiota invasion leads to ectopic colonization of the extra-oral sites and immune cells infiltration, which might cause local or systemic inflammation. Those cells are considered to act as a “bridge” between T1D and periodontitis.

Mass spectrometry, data re-analysis, and homology modelling predict posttranslational modifications of leucine-rich alpha-2-glycoprotein as a marker of myelodysplastic syndrome

BACKGROUND

Leucine-rich alpha-2-glycoprotein (LRG) has been repeatedly proposed as a potential plasma biomarker for myelodysplastic syndrome (MDS).

OBJECTIVE

The goal of our work was to establish the total LRG plasma level and LRG posttranslational modifications (PTMs) as a suitable MDS biomarker.

METHODS

The total plasma LRG concentration was determined with ELISA, whilst the LRG-specific PTMs and their locations, were established using mass spectrometry and public mass spectrometry data re-analysis. Homology modelling and sequence analysis were used to establish the potential impact of PTMs on LRG functions via their impact on the LRG structure.

RESULTS

While the results showed that the total LRG plasma concentration is not a suitable MDS marker, alterations within two LRG sites correlated with MDS diagnosis (p= 0.0011). Sequence analysis and the homology model suggest the influence of PTMs within the two LRG sites on the function of this protein.

CONCLUSIONS

We report the presence of LRG proteoforms that correlate with diagnosis in the plasma of MDS patients. The combination of mass spectrometry, re-analysis of publicly available data, and homology modelling, represents an approach that can be used for any protein to predict clinically relevant protein sites for biomarker research despite the character of the PTMs being unknown.

Transmembrane Helices Are an Over-Presented and Evolutionarily Conserved Source of Major Histocompatibility Complex Class I and II Epitopes

Cytolytic T cell responses are predicted to be biased towards membrane proteins. The peptide-binding grooves of most alleles of histocompatibility complex class I (MHC-I) are relatively hydrophobic, therefore peptide fragments derived from human transmembrane helices (TMHs) are predicted to be presented more often as would be expected based on their abundance in the proteome. However, the physiological reason of why membrane proteins might be over-presented is unclear. In this study, we show that the predicted over-presentation of TMH-derived peptides is general, as it is predicted for bacteria and viruses and for both MHC-I and MHC-II, and confirmed by re-analysis of epitope databases. Moreover, we show that TMHs are evolutionarily more conserved, because single nucleotide polymorphisms (SNPs) are present relatively less frequently in TMH-coding chromosomal regions compared to regions coding for extracellular and cytoplasmic protein regions. Thus, our findings suggest that both cytolytic and helper T cells are more tuned to respond to membrane proteins, because these are evolutionary more conserved. We speculate that TMHs are less prone to mutations that enable pathogens to evade T cell responses.

Sustained software development, not number of citations or journal choice, is indicative of accurate bioinformatic software

Background

Computational biology provides software tools for testing and making inferences about biological data. In the face of increasing volumes of data, heuristic methods that trade software speed for accuracy may be employed. We have studied these trade-offs using the results of a large number of independent software benchmarks, and evaluated whether external factors, including speed, author reputation, journal impact, recency and developer efforts, are indicative of accurate software.

Results

We find that software speed, author reputation, journal impact, number of citations and age are unreliable predictors of software accuracy. This is unfortunate because these are frequently cited reasons for selecting software tools. However, GitHub-derived statistics and high version numbers show that accurate bioinformatic software tools are generally the product of many improvements over time. We also find an excess of slow and inaccurate bioinformatic software tools, and this is consistent across many sub-disciplines. There are few tools that are middle-of-road in terms of accuracy and speed trade-offs.

Conclusions

Our findings indicate that accurate bioinformatic software is primarily the product of long-term commitments to software development. In addition, we hypothesise that bioinformatics software suffers from publication bias. Software that is intermediate in terms of both speed and accuracy may be difficult to publish—possibly due to author, editor and reviewer practises. This leaves an unfortunate hole in the literature, as ideal tools may fall into this gap. High accuracy tools are not always useful if they are slow, while high speed is not useful if the results are also inaccurate.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-022-02625-x).

SPENCER: a comprehensive database for small peptides encoded by noncoding RNAs in cancer patients

As an increasing number of noncoding RNAs (ncRNAs) have been suggested to encode short bioactive peptides in cancer, the exploration of ncRNA-encoded small peptides (ncPEPs) is emerging as a fascinating field in cancer research. To assist in studies on the regulatory mechanisms of ncPEPs, we describe here a database called SPENCER (http://spencer.renlab.org). Currently, SPENCER has collected a total of 2806 mass spectrometry (MS) data points from 55 studies, covering 1007 tumor samples and 719 normal samples. Using an MS-based proteomics analysis pipeline, SPENCER identified 29 526 ncPEPs across 15 different cancer types. Specifically, 22 060 of these ncPEPs were experimentally validated in other studies. By comparing tumor and normal samples, the identified ncPEPs were divided into four expression groups: tumor-specific, upregulated in cancer, downregulated in cancer, and others. Additionally, since ncPEPs are potential targets for neoantigen-based cancer immunotherapy, SPENCER also predicted the immunogenicity of all the identified ncPEPs by assessing their MHC-I binding affinity, stability, and TCR recognition probability. As a result, 4497 ncPEPs curated in SPENCER were predicted to be immunogenic. Overall, SPENCER will be a useful resource for investigating cancer-associated ncPEPs and may boost further research in cancer.

A new strategy for distinguishing menstrual blood from peripheral blood by the miR-451a/miR-21-5p ratio

Distinction between menstrual blood and peripheral blood is vital for forensic casework, as it could provide strong evidence to figure out the nature of some criminal cases. However, to date no single blood-specific gene, including the most variable microRNAs (miRNAs) could work well in identification of blood source. In this study, we developed a new strategy for identification of human blood samples by using the copy number ratios of miR-451a to miR-21-5p based on 133 samples, including 56 menstrual blood and 47 peripheral blood, as well as 30 non-blood samples of saliva (10), semen (10) and vaginal secretion (10). The cut-off value and efficacy of the identification strategy were determined through receiver operating characteristic (ROC) analysis. Our results showed that when the miR-451a/miR-21-5p ratio below 0.929, the sample should be non-blood. In contrast, when the miR-451a/miR-21-5p ratio above 0.929 and below 10.201, the sample should be menstrual blood; and when this ratio above 10.201, the sample should be peripheral blood. External validation using 86 samples (62 menstrual blood and 24 peripheral blood samples) fully supported this strategy with the 100% sensitivity and 100% specificity. We confirmed that this result accuracy was not affected by various potential confounding factors of samples and different experimental platforms. We showed that 0.2 ng of total RNA from menstrual blood and peripheral blood was sufficient for qPCR quantification. In conclusion, our results provide an accurate reference to distinguish menstrual blood from peripheral blood for forensic authentication.

NR2F1 database: 112 variants and 84 patients support refining the clinical synopsis of Bosch-Boonstra-Schaaf optic atrophy syndrome

Pathogenic variants of the nuclear receptor subfamily 2 group F member 1 gene (NR2F1) are responsible for Bosch-Boonstra-Schaaf optic atrophy syndrome (BBSOAS), an autosomal dominant disorder characterized by optic atrophy associated with developmental delay and intellectual disability, but with a clinical presentation which appears to be multifaceted. We created the first public locus-specific database dedicated to NR2F1. All variants and clinical cases reported in the literature, as well as new unpublished cases, were integrated into the database using standard nomenclature to describe both molecular and phenotypic anomalies. We subsequently pursued a comprehensive approach based on computed representation and analysis suggesting a refinement of the BBSOAS clinical description with respect to neurological features and the inclusion of additional signs of hypotonia and feeding difficulties. This database is fully accessible for both clinician and molecular biologists and should prove useful in further refining the clinical synopsis of NR2F1 as new data is recorded.

Variimorphobacter saccharofermentans gen. nov., sp. nov., a new member of the family Lachnospiraceae, isolated from a maize-fed biogas fermenter

Strain MD1^T is an anaerobic, Gram-stain-negative bacterium isolated from a lab-scale biogas fermenter fed with maize silage. It has a rod-shaped morphology with peritrichously arranged appendages and forms long chains of cells and coccoid structures. The colonies of MD1^T were white, circular, slightly convex and had a smooth rim. The isolate is mesophilic, displaying growth between 25 and 45 °C with an optimum at 40 °C. It grew at pH values of pH 6.7-8.2 (optimum, pH 7.1) and tolerated the addition of up to 1.5% (w/v) NaCl to the medium. The main cellular fatty acids of MD1^T are C_14:0 DMA and C_16:0. Strain MD1^T fermented xylose, arabinose, glucose, galactose, cellobiose, maltose, maltodextrin10, lactose starch, and xylan, producing mainly 2-propanol and acetic acid. The genome of the organism has a total length of 4163427 bp with a G+C content of 38.5 mol%. The two closest relatives to MD1^T are Mobilitalea sibirica P3M-3^T and Anaerotaenia torta FH052^T with 96.44 or 95.8 % 16S rRNA gene sequence similarity and POCP values of 46.58 and 50.58%, respectively. As MD1^T showed saccharolytic and xylanolytic properties, it may play an important role in the biogas fermentation process. Closely related variants of MD1^T were also abundant in microbial communities involved in methanogenic fermentation. Based on morphological, phylogenetic and genomic data, the isolated strain can be considered as representing a novel genus in the family Lachnospiraceae, for which the name Variimorphobacter saccharofermentans gen. nov., sp. nov. (type strain MD1^T=DSM 110715^T=JCM 39125^T) is proposed.

Chronic cerebral lipocalin 2 exposure elicits hippocampal neuronal dysfunction and cognitive impairment

Lipocalin 2 (LCN2) is a pleiotropic molecule that is induced in the central nervous system (CNS) in several acute and chronic pathologies. The acute induction of LCN2 evolved as a beneficial process, aimed at combating bacterial infection through the sequestration of iron from pathogens, while the role of LCN2 during chronic, non-infectious disease remains unclear, and recent studies suggest that LCN2 is neurotoxic. However, whether LCN2 is sufficient to induce behavioral and cognitive alterations remains unclear. In this paper, we sought to address the role of cerebral LCN2 on cognition in both acute and chronic settings. We demonstrate that LCN2 is robustly induced in the CNS during both acute and chronic inflammatory conditions, including LPS-based sepsis and cancer cachexia. In vivo, LPS challenge results in a global induction of LCN2 in the central nervous system, while cancer cachexia results in a distribution specific to the vasculature. Similar to these in vivo observations, in vitro modeling demonstrated that both glia and cerebral endothelium produce and secrete LCN2 when challenged with LPS, while only cerebral endothelium secrete LCN2 when challenged with cancer-conditioned medium. Chronic, but not short-term, cerebral LCN2 exposure resulted in reduced hippocampal neuron staining intensity, an increase in newborn neurons, microglial activation, and increased CNS immune cell infiltration, while gene set analyses suggested these effects were mediated through melanocortin-4 receptor independent mechanisms. RNA sequencing analyses of primary hippocampal neurons revealed a distinct transcriptome associated with prolonged LCN2 exposure, and ontology analysis was suggestive of altered neurite growth and abnormal spatial learning. Indeed, LCN2-treated hippocampal neurons display blunted neurite processes, and mice exposed to prolonged cerebral LCN2 levels experienced a reduction in spatial reference memory as indicated by Y-maze assessment. These findings implicate LCN2 as a pathologic mediator of cognitive decline in the setting of chronic disease.

Universal concept signature analysis: genome-wide quantification of new biological and pathological functions of genes and pathways

Identifying new gene functions and pathways underlying diseases and biological processes are major challenges in genomics research. Particularly, most methods for interpreting the pathways characteristic of an experimental gene list defined by genomic data are limited by their dependence on assessing the overlapping genes or their interactome topology, which cannot account for the variety of functional relations. This is particularly problematic for pathway discovery from single-cell genomics with low gene coverage or interpreting complex pathway changes such as during change of cell states. Here, we exploited the comprehensive sets of molecular concepts that combine ontologies, pathways, interactions and domains to help inform the functional relations. We first developed a universal concept signature (uniConSig) analysis for genome-wide quantification of new gene functions underlying biological or pathological processes based on the signature molecular concepts computed from known functional gene lists. We then further developed a novel concept signature enrichment analysis (CSEA) for deep functional assessment of the pathways enriched in an experimental gene list. This method is grounded on the framework of shared concept signatures between gene sets at multiple functional levels, thus overcoming the limitations of the current methods. Through meta-analysis of transcriptomic data sets of cancer cell line models and single hematopoietic stem cells, we demonstrate the broad applications of CSEA on pathway discovery from gene expression and single-cell transcriptomic data sets for genetic perturbations and change of cell states, which complements the current modalities. The R modules for uniConSig analysis and CSEA are available through https://github.com/wangxlab/uniConSig.

ACO2 clinicobiological dataset with extensive phenotype ontology annotation

Pathogenic variants of the aconitase 2 gene (ACO2) are responsible for a broad clinical spectrum involving optic nerve degeneration, ranging from isolated optic neuropathy with recessive or dominant inheritance, to complex neurodegenerative syndromes with recessive transmission. We created the first public locus-specific database (LSDB) dedicated to ACO2 within the "Global Variome shared LOVD" using exclusively the Human Phenotype Ontology (HPO), a standard vocabulary for describing phenotypic abnormalities. All the variants and clinical cases listed in the literature were incorporated into the database, from which we produced a dataset. We followed a rational and comprehensive approach based on the HPO thesaurus, demonstrating that ACO2 patients should not be classified separately between isolated and syndromic cases. Our data highlight that certain syndromic patients do not have optic neuropathy and provide support for the classification of the recurrent pathogenic variants c.220C>G and c.336C>G as likely pathogenic. Overall, our data records demonstrate that the clinical spectrum of ACO2 should be considered as a continuum of symptoms and refines the classification of some common variants.

Beneficial Gut Microbiome Remodeled During Intermittent Fasting in Humans

Intermittent fasting (IF) is the practice of restricting food intake for 12-48 hours per fasting cycle over a prolonged period of time. Previous study shows beneficial health effects such as weight loss and lower risk for cardiometabolic diseases. Although reduced calorie intake may account for some of the observed benefits of IF, exact mechanisms are still unclear. Recent evidence indicates that IF may lead to remodeling and increased taxonomic diversity in the human gut microbiome. In particular, the Lachnospiraceae family of anaerobic bacteria increased during fasting. This family, in the order Clostridiales, promotes butryogenesis in the gut, a process that is associated with healthful metabolic and prolongevity effects. IF-associated alterations to the microbiome may play a key role in the metabolic and potential healthspan-enhancing benefits of IF and dietary restriction.

Molecular Dynamic Simulations Suggest That Metabolite-Induced Post-Translational Modifications Alter the Behavior of the Fibrinogen Coiled-Coil Domain

Fibrinogen is an abundant blood plasma protein that, inter alia, participates in blood coagulation. It polymerizes to form a fibrin clot that is among the major components of the thrombus. Fibrinogen reactions with various reactive metabolites may induce post-translational modifications (PTMs) into the protein structure that affect the architecture and properties of fibrin clots. We reviewed the previous literature to find the positions of PTMs of fibrinogen. For 7 out of 307 reported PTMs, we used molecular dynamics simulations to characterize their effect on the behavior of the fibrinogen coiled-coil domain. Interactions of the γ-coil with adjacent chains give rise to π-helices in Aα and Bβ chains of even unmodified fibrinogen. The examined PTMs suppress fluctuations of the γ-coil, which may affect the fibrinolysis and stiffness of the fibrin fibers. Citrullination of AαR104 and oxidations of γP70 and γP76 to glutamic semialdehyde unfold the α-helical structure of Aα and Bβ chains. Oxidation of γM78 to methionine sulfoxide induces the formation of an α-helix in the γ-coil region. Our findings suggest that certain PTMs alter the protein secondary structure. Thus, the altered protein structure may indicate the presence of PTMs in the molecule and consequently of certain metabolites within the system.

Remodeling of the gut microbiome during Ramadan-associated intermittent fasting

BACKGROUND

Intermittent fasting is a popular dietary intervention with perceived relatively easy compliance and is linked to various health benefits, including weight loss and improvement in blood glucose concentrations. The mechanistic explanations underlying the beneficial effects of intermittent fasting remain largely obscure but may involve alterations in the gut microbiota.

OBJECTIVES

We sought to establish the effects of 1 mo of intermittent fasting on the gut microbiome.

METHODS

We took advantage of intermittent fasting being voluntarily observed during the Islamic faith-associated Ramadan and sampled feces and blood, as well as collected longitudinal physiologic data in 2 cohorts, sampled in 2 different years. The fecal microbiome was determined by 16S sequencing. Results were contrasted to age- and body weight-matched controls and correlated to physiologic parameters (e.g., body mass and calorie intake).

RESULTS

We observed that Ramadan-associated intermittent fasting increased microbiome diversity and was specifically associated with upregulation of the Clostridiales order-derived Lachnospiraceae [no fasting 24.6 ± 13.67 compared with fasting 39.7 ± 15.9 in relative abundance (%); linear discriminant analysis = 4.9, P < 0.001 by linear discriminant analysis coupled with effect size measurements] and Ruminococcaceae [no fasting 13.4 ± 6.9 compared with fasting 23.2 ± 12.9 in relative abundance (%); linear discriminant analysis = 4.7, P < 0.001 by linear discriminant analysis coupled with effect size measurements] bacterial families. Microbiome composition returned to baseline upon cessation of intermittent feeding. Furthermore, changes in Lachnospiraceae concentrations mirrored intermittent fasting-provoked changes in physiologic parameters.

CONCLUSIONS

Intermittent fasting provokes substantial remodeling of the gut microbiome. The intermittent fasting-provoked upregulation of butyric acid-producing Lachnospiraceae provides an obvious possible mechanistic explanation for health effects associated with intermittent fasting.

Overview of PAX gene family: analysis of human tissue-specific variant expression and involvement in human disease

Paired-box (PAX) genes encode a family of highly conserved transcription factors found in vertebrates and invertebrates. PAX proteins are defined by the presence of a paired domain that is evolutionarily conserved across phylogenies. Inclusion of a homeodomain and/or an octapeptide linker subdivides PAX proteins into four groups. Often termed "master regulators", PAX proteins orchestrate tissue and organ development throughout cell differentiation and lineage determination, and are essential for tissue structure and function through maintenance of cell identity. Mutations in PAX genes are associated with myriad human diseases (e.g., microphthalmia, anophthalmia, coloboma, hypothyroidism, acute lymphoblastic leukemia). Transcriptional regulation by PAX proteins is, in part, modulated by expression of alternatively spliced transcripts. Herein, we provide a genomics update on the nine human PAX family members and PAX homologs in 16 additional species. We also present a comprehensive summary of human tissue-specific PAX transcript variant expression and describe potential functional significance of PAX isoforms. While the functional roles of PAX proteins in developmental diseases and cancer are well characterized, much remains to be understood regarding the functional roles of PAX isoforms in human health. We anticipate the analysis of tissue-specific PAX transcript variant expression presented herein can serve as a starting point for such research endeavors.

Virus Isoelectric Point Estimation: Theories and Methods

Much of virus fate, both in the environment and in physical/chemical treatment, is dependent on electrostatic interactions. Developing an accurate means of predicting virion isoelectric point (pI) would help to understand and anticipate virus fate and transport, especially for viruses that are not readily propagated in the lab. One simple approach to predicting pI estimates the pH at which the sum of charges from ionizable amino acids in capsid proteins approaches zero. However, predicted pIs based on capsid charges frequently deviate by several pH units from empirically measured pIs. Recently, the discrepancy between empirical and predicted pI was attributed to the electrostatic neutralization of predictable polynucleotide-binding regions (PBRs) of the capsid interior. In this paper, we review models presupposing (i) the influence of the viral polynucleotide on surface charge or (ii) the contribution of only exterior residues to surface charge. We then compare these models to the approach of excluding only PBRs and hypothesize a conceptual electrostatic model that aligns with this approach. The PBR exclusion method outperformed methods based on three-dimensional (3D) structure and accounted for major discrepancies in predicted pIs without adversely affecting pI prediction for a diverse range of viruses. In addition, the PBR exclusion method was determined to be the best available method for predicting virus pI, since (i) PBRs are predicted independently of the impact on pI, (ii) PBR prediction relies on proteome sequences rather than detailed structural models, and (iii) PBR exclusion was successfully demonstrated on a diverse set of viruses. These models apply to nonenveloped viruses only. A similar model for enveloped viruses is complicated by a lack of data on enveloped virus pI, as well as uncertainties regarding the influence of the phospholipid envelope on charge and ion gradients.

Plant miRNAs Reduce Cancer Cell Proliferation by Targeting MALAT1 and NEAT1: A Beneficial Cross-Kingdom Interaction

MicroRNAs (miRNAs) are ubiquitous regulators of gene expression, evolutionarily conserved in plants and mammals. In recent years, although a growing number of papers debate the role of plant miRNAs on human gene expression, the molecular mechanisms through which this effect is achieved are still not completely elucidated. Some evidence suggest that this interaction might be sequence specific, and in this work, we investigated this possibility by transcriptomic and bioinformatics approaches. Plant and human miRNA sequences from primary databases were collected and compared for their similarities (global or local alignments). Out of 2,588 human miRNAs, 1,606 showed a perfect match of their seed sequence with the 5′ end of 3,172 plant miRNAs. Further selections were applied based on the role of the human target genes or of the miRNA in cell cycle regulation (as an oncogene, tumor suppressor, or a biomarker for prognosis, or diagnosis in cancer). Based on these criteria, 20 human miRNAs were selected as potential functional analogous of 7 plant miRNAs, which were in turn transfected in different cell lines to evaluate their effect on cell proliferation. A significant decrease was observed in colorectal carcinoma HCT116 cell line. RNA-Seq demonstrated that 446 genes were differentially expressed 72 h after transfection. Noteworthy, we demonstrated that the plant mtr-miR-5754 and gma-miR4995 directly target the tumor-associated long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1) in a sequence-specific manner. In conclusion, according to other recent discoveries, our study strengthens and expands the hypothesis that plant miRNAs can have a regulatory effect in mammals by targeting both protein-coding and non-coding RNA, thus suggesting new biotechnological applications.

An Improved hgcAB Primer Set and Direct High-Throughput Sequencing Expand Hg-Methylator Diversity in Nature

The gene pair hgcAB is essential for microbial mercury methylation. Our understanding of its abundance and diversity in nature is rapidly evolving. In this study we developed a new broad-range primer set for hgcAB, plus an expanded hgcAB reference library, and used these to characterize Hg-methylating communities from diverse environments. We applied this new Hg-methylator database to assign taxonomy to hgcA sequences from clone, amplicon, and metagenomic datasets. We evaluated potential biases introduced in primer design, sequence length, and classification, and suggest best practices for studying Hg-methylator diversity. Our study confirms the emerging picture of an expanded diversity of HgcAB-encoding microbes in many types of ecosystems, with abundant putative mercury methylators Nitrospirae and Chloroflexi in several new environments including salt marsh and peat soils. Other common microbes encoding HgcAB included Phycisphaerae, Aminicenantes, Spirochaetes, and Elusimicrobia. Combined with high-throughput amplicon specific sequencing, the new primer set also indentified novel hgcAB sequences similar to Lentisphaerae, Bacteroidetes, Atribacteria, and candidate phyla WOR-3 and KSB1 bacteria. Gene abundance data also corroborate the important role of two "classic" groups of methylators (Deltaproteobacteria and Methanomicrobia) in many environments, but generally show a scarcity of hgcAB+ Firmicutes. The new primer set was developed to specifically target hgcAB sequences found in nature, reducing degeneracy and providing increased sensitivity while maintaining broad diversity capture. We evaluated mock communities to confirm primer improvements, including culture spikes to environmental samples with variable DNA extraction and PCR amplification efficiencies. For select sites, this new workflow was combined with direct high-throughput hgcAB sequencing. The hgcAB diversity generated by direct amplicon sequencing confirmed the potential for novel Hg-methylators previously identified using metagenomic screens. A new phylogenetic analysis using sequences from freshwater, saline, and terrestrial environments showed Deltaproteobacteria HgcA sequences generally clustered among themselves, while metagenome-resolved HgcA sequences in other phyla tended to cluster by environment, suggesting horizontal gene transfer into many clades. HgcA from marine metagenomes often formed distinct subtrees from those sequenced from freshwater ecosystems. Overall the majority of HgcA sequences branch from a cluster of HgcAB fused proteins related to Thermococci, Atribacteria (candidate division OP9), Aminicenantes (OP8), and Chloroflexi. The improved primer set and library, combined with direct amplicon sequencing, provide a significantly improved assessment of the abundance and diversity of hgcAB+ microbes in nature.

Orchid conservation and research: An analysis of gaps and priorities for globally Red Listed species

Orchids are among the most threatened taxa globally due to increasing anthropogenic threats, inherent rarity and specific conservation needs. But what are the global research and conservation priorities for this charismatic group of plants? Using information for 595 orchids on the IUCN Red List, we reviewed past research and identified key research and conservation priorities. These included understanding threats, monitoring orchid populations and habitats, species management in ex situ conservation, genome resource banks and artificial propagation, land and habitat protection and education and awareness through communication. Based on the available data, we recommend future orchid conservation and research should focus on the current gaps in knowledge and practice including monitoring population trends and distributions, ecology, threats, protection and management of species and their habitats and increasing education and awareness.

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

BACKGROUND

It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of "incremental learning" addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data.

RESULTS

We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model's knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4th of the non-incremental time with no accuracy loss.

CONCLUSIONS

It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.

Microbiome and Metagenome Analyses of a Closed Habitat during Human Occupation

This study provides the first assessment of monitoring cultivable and viable microorganisms on surfaces within a submerged, closed, analog habitat. The results of the analyses presented herein suggest that the surface material plays a role in microbial community structure, as the microbial populations differed between LDP and metal/glass surfaces. The metal/glass surfaces had less-complex community, lower bioburden, and more closely resembled the controls. These results indicated that material choice is crucial when building closed habitats, even if they are simply analogs. Finally, while a few species were associated with previously cultivated isolates from the International Space Station and MIR spacecraft, the majority of the microbial ecology of the submerged analog habitat differs greatly from that of previously studied analog habitats.

Microbial contamination during long-term confinements of space exploration presents potential risks for both crew members and spacecraft life support systems. A novel swab kit was used to sample various surfaces from a submerged, closed, analog habitat to characterize the microbial populations. Samples were collected from various locations across the habitat which were constructed from various surface materials (linoleum, dry wall, particle board, glass, and metal), and microbial populations were examined by culture, quantitative PCR (qPCR), microbiome 16S rRNA gene sequencing, and shotgun metagenomics. Propidium monoazide (PMA)-treated samples identified the viable/intact microbial population of the habitat. The cultivable microbial population ranged from below the detection limit to 10⁶ CFU/sample, and their identity was characterized using Sanger sequencing. Both 16S rRNA amplicon and shotgun sequencing were used to characterize the microbial dynamics, community profiles, and functional attributes (metabolism, virulence, and antimicrobial resistance). The 16S rRNA amplicon sequencing revealed abundance of viable (after PMA treatment) Actinobacteria (Brevibacterium, Nesternkonia, Mycobacterium, Pseudonocardia, and Corynebacterium), Firmicutes (Virgibacillus, Staphylococcus, and Oceanobacillus), and Proteobacteria (especially Acinetobacter) on linoleum, dry wall, and particle board (LDP) surfaces, while members of Firmicutes (Leuconostocaceae) and Proteobacteria (Enterobacteriaceae) were high on the glass/metal surfaces. Nonmetric multidimensional scaling determined from both 16S rRNA and metagenomic analyses revealed differential microbial species on LDP surfaces and glass/metal surfaces. The shotgun metagenomic sequencing of samples after PMA treatment showed bacterial predominance of viable Brevibacterium (53.6%), Brachybacterium (7.8%), Pseudonocardia (9.9%), Mycobacterium (3.7%), and Staphylococcus (2.1%), while fungal analyses revealed Aspergillus and Penicillium dominance.

IMPORTANCE This study provides the first assessment of monitoring cultivable and viable microorganisms on surfaces within a submerged, closed, analog habitat. The results of the analyses presented herein suggest that the surface material plays a role in microbial community structure, as the microbial populations differed between LDP and metal/glass surfaces. The metal/glass surfaces had less-complex community, lower bioburden, and more closely resembled the controls. These results indicated that material choice is crucial when building closed habitats, even if they are simply analogs. Finally, while a few species were associated with previously cultivated isolates from the International Space Station and MIR spacecraft, the majority of the microbial ecology of the submerged analog habitat differs greatly from that of previously studied analog habitats.

Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes

Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf.

Genes that are involved in the same biological process tend to co-evolve. This property is exploited by the technique of phylogenetic profiling, which identifies co-evolving (and therefore likely functionally related) genes through patterns of correlated gene retention and loss in evolution and across species. However, conventional methods to computing and clustering these correlated genes do not scale with increasing numbers of genomes. HogProf is a novel phylogenetic profiling tool built on probabilistic data structures. It allows the user to construct searchable databases containing the evolutionary history of hundreds of thousands of protein families. Such fast detection of coevolution takes advantage of the rapidly increasing amount of genomic data publicly available, and can uncover unknown biological networks and guide in-vivo research and experimentation. We have applied our tool to describe the biological networks underpinning sexual reproduction in eukaryotes.

Metabolomics-Guided Isolation of Anti-Trypanosomal Compounds from Endophytic Fungi of the Mangrove plant Avicennia Lanata

Endophytic fungi have been explored not just for their ecological functions but also for their secondary metabolites as a new source of these pharmacologically active natural products. Accordingly, many structurally unique and biologically active compounds have been obtained from the cultures of endophytic fungi. Fusarium sp. and Lasiodiplodia theobromae were isolated from the root and stem of the mangrove plant Avicennia lanata, respectively, collected from Terengganu, Malaysia. High-resolution mass spectrometry and NMR spectroscopy were used as metabolomics profiling tools to identify and optimize the production of bioactive secondary metabolites in both strains at different growth stages and culture media. The spectral data was processed by utilizing Mzmine 2, a quantitative expression analysis software and an in house MS-Excel macro coupled with the Dictionary of Natural Products databases for dereplication studies. The investigation for the potential bioactive metabolites from a 15-day rice culture of Fusarium sp. yielded four 1,4- naphthoquinone with naphthazarin structures (1-4). On the other hand, the endophytic fungus L. theobromae grown on the 15-day solid rice culture produced dihydroisocoumarins (5-8). All the isolated compounds (1-8) showed significant activity against Trypanosoma brucei brucei with MIC values of 0.32-12.5 µM. Preliminary cytotoxicity screening against normal prostate cells (PNT2A) was also performed. All compounds exhibited low cytotoxicity, with compounds 3 and 4 showing the lowest cytotoxicity of only 22.3% and 38.6% of the control values at 100 µg/mL, respectively. Structure elucidation of the isolated secondary metabolites was achieved using 2D-NMR and HRESI-MS as well as comparison with literature data.

BRret: Retrieval of Brain Research Related Literature

Research Domain Criteria (RDoC), which is a recently introduced framework for mental illness, utilizes various units of analysis from genetics, neural circuits, etc., for accurate multi-dimensional classification of mental illnesses. Due to the large amount of relevant biomedical research available, automating the process of extracting evidence from the literature to assist with the curation of the RDoC matrix is essential for processing the full breadth of data in an accurate and cost-effective manner. In this work, we formulate the task of information retrieval of brain research literature from general PubMed abstracts. We develop BRret (Brain Research retriever), a novel algorithm for brain research related article retrieval. We use a large dataset of PubMed abstracts annotated with RDoC concepts to demonstrate the effectiveness of BRret. To the best of our knowledge, this is the first study aimed at automated retrieval of brain research related literature.

Codon Pairs are Phylogenetically Conserved: A comprehensive analysis of codon pairing conservation across the Tree of Life

Identical codon pairing and co-tRNA codon pairing increase translational efficiency within genes when two codons that encode the same amino acid are translated by the same tRNA before it diffuses from the ribosome. We examine the phylogenetic signal in both identical and co-tRNA codon pairing across 23 428 species using alignment-free and parsimony methods. We determined that conserved codon pairing typically has a smaller window size than the length of a ribosome, and codon pairing tracks phylogenies across various taxonomic groups. We report a comprehensive analysis of codon pairing, including the extent to which each codon pairs. Our parsimony method generally recovers phylogenies that are more congruent with the established phylogenies than our alignment-free method. However, four of the ten taxonomic groups did not have sufficient orthologous codon pairings and were therefore analyzed using only the alignment-free methods. Since the recovered phylogenies using only codon pairing largely match phylogenies from the Open Tree of Life and the NCBI taxonomy, and are comparable to trees recovered by other algorithms, we propose that codon pairing biases are phylogenetically conserved and should be considered in conjunction with other phylogenomic techniques.

The Controversial Role of Human Gut Lachnospiraceae

The complex polymicrobial composition of human gut microbiota plays a key role in health and disease. Lachnospiraceae belong to the core of gut microbiota, colonizing the intestinal lumen from birth and increasing, in terms of species richness and their relative abundances during the host's life. Although, members of Lachnospiraceae are among the main producers of short-chain fatty acids, different taxa of Lachnospiraceae are also associated with different intra- and extraintestinal diseases. Their impact on the host physiology is often inconsistent across different studies. Here, we discuss changes in Lachnospiraceae abundances according to health and disease. With the aim of harnessing Lachnospiraceae to promote human health, we also analyze how nutrients from the host diet can influence their growth and how their metabolites can, in turn, influence host physiology.

Draft genomes of two Atlantic bay scallop subspecies Argopecten irradians irradians and A. i. concentricus

The two subspecies of Atlantic bay scallop (Argopecten irradians), A. i. irradians and A. i. concentricus, are economically important aquacultural species in northern and southern China. Here, we performed the whole-genome sequencing, assembly, and gene annotation and produced draft genomes for both subspecies. In total, 253.17 and 272.97 gigabases (Gb) of raw reads were generated from Illumina Hiseq and PacBio platforms for A. i. irradians and A. i. concentricus, respectively. Draft genomes of 835.7 Mb and 874.82 Mb were assembled for the two subspecies, accounting for 83.9% and 89.79% of the estimated sizes of their corresponding genomes, respectively. The contig N50 and scaffold N50 were 78.54 kb and 1.53 Mb for the A. i. irradians genome, and those for the A. i. concentricus genome were 63.73 kb and 1.25 Mb. Moreover, 26,777 and 25,979 protein-coding genes were predicted for A. i. irradians and A. i. concentricus, respectively. These valuable genome assemblies lay a solid foundation for future theoretical studies and provide guidance for practical scallop breeding.

The sex with the reduced sex chromosome dies earlier: a comparison across the tree of life

Many taxa show substantial differences in lifespan between the sexes. However, these differences are not always in the same direction. In mammals, females tend to live longer than males, while in birds, males tend to live longer than females. One possible explanation for these differences in lifespan is the unguarded X hypothesis, which suggests that the reduced or absent chromosome in the heterogametic sex (e.g. the Y chromosome in mammals and the W chromosome in birds) exposes recessive deleterious mutations on the other sex chromosome. While the unguarded X hypothesis is intuitively appealing, it had never been subject to a broad test. We compiled male and female longevity data for 229 species spanning 99 families, 38 orders and eight classes across the tree of life. Consistent with the unguarded X hypothesis, a meta-analysis showed that the homogametic sex, on average, lives 17.6% longer than the heterogametic sex. Surprisingly, we found substantial differences in lifespan dimorphism between female heterogametic species (in which the homogametic sex lives 7.1% longer) and male heterogametic species (in which the homogametic sex lives 20.9% longer). Our findings demonstrate the importance of considering chromosome morphology in addition to sexual selection and environment as potential drivers of sexual dimorphism, and advance our fundamental understanding of the mechanisms that shape an organism's lifespan.

Impact of posttranslational modifications on atomistic structure of fibrinogen

Oxidative stress in humans is related to various pathophysiological processes, which can manifest in numerous diseases including cancer, cardiovascular diseases, and Alzheimer’s disease. On the atomistic level, oxidative stress causes posttranslational modifications, thus inducing structural and functional changes into the proteins structure. This study focuses on fibrinogen, a blood plasma protein that is frequently targeted by reagents causing posttranslational modifications in proteins. Fibrinogen was in vitro modified by three reagents, namely sodium hypochlorite, malondialdehyde, and 3-morpholinosydnonimine that mimic the oxidative stress in diseases. Newly induced posttranslational modifications were detected via mass spectrometry. Electron microscopy was used to visualize changes in the fibrin networks, which highlight the extent of disturbances in fibrinogen behavior after exposure to reagents. We used molecular dynamics simulations to observe the impact of selected posttranslational modifications on the fibrinogen structure at the atomistic level. In total, 154 posttranslational modifications were identified, 84 of them were in fibrinogen treated with hypochlorite, 51 resulted from a reaction of fibrinogen with malondialdehyde, and 19 were caused by 3-morpholinosydnonimine. Our data reveal that the stronger reagents induce more posttranslational modifications in the fibrinogen structure than the weaker ones, and they extensively alter the architecture of the fibrin network. Molecular dynamics simulations revealed that the effect of posttranslational modifications on fibrinogen secondary structure varies from negligible alternations to serious disruptions. Among the serious disruptions is the oxidation of γR375 resulting in the release of Ca²⁺ ion that is necessary for appropriate fibrin fiber formation. Folding of amino acids γE72–γN77 into a short α-helix is a result of oxidation of γP76 to glutamic acid. The study describes behaviour of fibrinogen coiled-coil connecter in the vicinity of plasmin and hementin cleavage sites.

Big Data and Artificial Intelligence Modeling for Drug Discovery

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

OPA1: 516 unique variants and 831 patients registered in an updated centralized Variome database

BACKGROUND

The dysfunction of OPA1, a dynamin GTPase involved in mitochondrial fusion, is responsible for a large spectrum of neurological disorders, each of which includes optic neuropathy. The database dedicated to OPA1 ( https://www.lovd.nl/OPA1 ), created in 2005, has now evolved towards a centralized and more reliable database using the Global Variome shared Leiden Open-source Variation Database (LOVD) installation.

RESULTS

The updated OPA1 database, which registers all the patients from our center as well as those reported in the literature, now covers a total of 831 patients: 697 with isolated dominant optic atrophy (DOA), 47 with DOA "plus", and 83 with asymptomatic or unclassified DOA. It comprises 516 unique OPA1 variants, of which more than 80% (414) are considered pathogenic. Full clinical data for 118 patients are documented using the Human Phenotype Ontology, a standard vocabulary for referencing phenotypic abnormalities. Contributors may now make online submissions of phenotypes related to OPA1 mutations, giving clinical and molecular descriptions together with detailed ophthalmological and neurological data, according to an international thesaurus.

CONCLUSIONS

The evolution of the OPA1 database towards the LOVD, using unified nomenclature, should ensure its interoperability with other databases and prove useful for molecular diagnoses based on gene-panel sequencing, large-scale mutation statistics, and genotype-phenotype correlations.

Immediate Effects of Ammonia Shock on Transcription and Composition of a Biogas Reactor Microbiome

The biotechnological process of biogas production from organic material is carried out by a diverse microbial community under anaerobic conditions. However, the complex and sensitive microbial network present in anaerobic degradation of organic material can be disturbed by increased ammonia concentration introduced into the system by protein-rich substrates and imbalanced feeding. Here, we report on a simulated increase of ammonia concentration in a fed batch lab-scale biogas reactor experiment. Two treatment conditions were used simulating total ammonia nitrogen concentrations of 4.9 and 8.0 g/L with four replicate reactors. Each reactor was monitored concerning methane generation and microbial composition using 16S rRNA gene amplicon sequencing, while the transcriptional activity of the overall process was investigated by metatranscriptomic analysis. This allowed investigating the response of the microbial community in terms of species composition and transcriptional activity to a rapid upshift to high ammonia conditions. Clostridia and Methanomicrobiales dominated the microbial community throughout the entire experiment under both experimental conditions, while Methanosarcinales were only present in minor abundance. Transcription analysis demonstrated clostridial dominance with respect to genes encoding for enzymes of the hydrolysis step (cellulase, EC 3.2.1.4) as well as dominance of key genes for enzymes of the methanogenic pathway (methyl-CoM reductase, EC 2.8.4.1; heterodisulfide reductase, EC 1.8.98.1). Upon ammonia shock, the selected marker genes showed significant changes in transcriptional activity. Cellulose hydrolysis as well as methanogenesis were significantly reduced at high ammonia concentrations as indicated by reduced transcription levels of the corresponding genes. Based on these experiments we concluded that, apart from the methanogenic archaea, hydrolytic cellulose-degrading microorganisms are negatively affected by high ammonia concentrations. Further, Acholeplasma and Erysipelotrichia showed lower abundance under increased ammonia concentrations and thus might serve as indicator species for an earlier detection in order to counteract against ammonia crises.

CAM: an alignment-free method to recover phylogenies using codon aversion motifs

BACKGROUND

Common phylogenomic approaches for recovering phylogenies are often time-consuming and require annotations for orthologous gene relationships that are not always available. In contrast, alignment-free phylogenomic approaches typically use structure and oligomer frequencies to calculate pairwise distances between species. We have developed an approach to quickly calculate distances between species based on codon aversion.

METHODS

Utilizing a novel alignment-free character state, we present CAM, an alignment-free approach to recover phylogenies by comparing differences in codon aversion motifs (i.e., the set of unused codons within each gene) across all genes within a species. Synonymous codon usage is non-random and differs between organisms, between genes, and even within a single gene, and many genes do not use all possible codons. We report a comprehensive analysis of codon aversion within 229,742,339 genes from 23,428 species across all kingdoms of life, and we provide an alignment-free framework for its use in a phylogenetic construct. For each species, we first construct a set of codon aversion motifs spanning all genes within that species. We define the pairwise distance between two species, A and B, as one minus the number of shared codon aversion motifs divided by the total codon aversion motifs of the species, A or B, containing the fewest motifs. This approach allows us to calculate pairwise distances even when substantial differences in the number of genes or a high rate of divergence between species exists. Finally, we use neighbor-joining to recover phylogenies.

RESULTS

Using the Open Tree of Life and NCBI Taxonomy Database as expected phylogenies, our approach compares well, recovering phylogenies that largely match expected trees and are comparable to trees recovered using maximum likelihood and other alignment-free approaches. Our technique is much faster than maximum likelihood and similar in accuracy to other alignment-free approaches. Therefore, we propose that codon aversion be considered a phylogenetically conserved character that may be used in future phylogenomic studies.

AVAILABILITY

CAM, documentation, and test files are freely available on GitHub at https://github.com/ridgelab/cam.

Tree shape-based approaches for the comparative study of cophylogeny

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long-term interaction. We investigated the use of tree shape distance measures to quantify the degree of cophylogeny. We implemented a reverse-time simulation model of pathogen phylogenies within a fixed host tree, given cospeciation probability, host switching, and pathogen speciation rates. We used this model to evaluate 18 distance measures between host and pathogen trees including two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and accommodate different size trees. Finally, we used these measures to revisit published cophylogenetic studies, where authors described the observed associations as representing a high or low degree of cophylogeny. Our simulations demonstrated that some measures are more informative than others with respect to specific coevolution parameters especially when these did not assume extreme values. For real datasets, trees' associations projection revealed clustering of high concordance studies suggesting that investigators are describing it in a consistent way. Our results support the hypothesis that measures can be useful for quantifying cophylogeny. This motivates their usage in the field of coevolution and supports the development of simulation-based methods, i.e., approximate Bayesian computation, to estimate the underlying coevolutionary parameters.

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.