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Abstract Theobroma cacao, the chocolate tree, is indigenous to the Amazon basin, the greatest biodiversity hotspot on earth. Recent advancement in plant genomics highlights the importance of de novo sequencing of multiple reference genomes to capture the genome diversity present in different cacao populations. In this study, three high-quality chromosome-level genomes of wild cacao were constructed, de novo assembled with HiFi long reads sequencing, and scaffolded using a reference-free strategy. These genomes represent the three most important genetic clusters of cacao trees from the Upper Amazon region. The three wild cacao genomes were compared with two reference genomes of domesticated cacao. The five cacao genetic clusters were inferred to have diverged in the early and middle Pleistocene period, approximately 1.83–0.69 million years ago. The results shown here serve as an example of understanding how the Amazonian biodiversity was developed. The three wild cacao genomes provide valuable resources for studying genetic diversity and advancing genetic improvement of this species.

Potatoes (Solanum tuberosum L.) are the third largest food crop globally and are pivotal for global food security. Widespread N fertilizer waste in potato cultivation has caused diverse environmental issues. This study employed microbial metagenomic sequencing to analyze the causes behind the declining N use efficiency (NUE) and escalating greenhouse gas emissions resulting from excessive N fertilizer application. Addressing N fertilizer inefficiency through breeding has emerged as a viable solution for mitigating overuse in potato cultivation. In this study, transcriptome and metabolome analyses were applied to identify N fertilizer-responsive genes. Metagenomic sequencing revealed that excessive N fertilizer application triggered alterations in the population dynamics of 11 major bacterial phyla, consequently affecting soil microbial functions, particularly N metabolism pathways and bacterial secretion systems. Notably, the enzyme levels associated with NO3- increased, and those associated with NO and N2O increased. Furthermore, excessive N fertilizer application enhanced soil virulence factors and increased potato susceptibility to diseases. Transcriptome and metabolome sequencing revealed significant impacts of excessive N fertilizer use on lipid and amino acid metabolism pathways. Weighted gene co‑expression network analysis (WGCNA) was adopted to identify two genes associated with N fertilizer response: PGSC0003DMG400021157 and PGSC0003DMG400009544.

In this study, we present a novel approach to enhancing the interpretability of medical image classification by integrating formal concept analysis (FCA) with convolutional neural networks (CNNs). While CNNs are increasingly applied in medical diagnoses, understanding their decision-making remains a challenge. Although visualization techniques like saliency maps offer insights into CNNs’ decision-making for individual images, they do not explicitly establish a relationship between the high-level features learned by CNNs and the class labels across entire dataset. To bridge this gap, we leverage the FCA framework as an image classification model, presenting a novel method for understanding the relationship between abstract features and class labels in medical imaging. Building on our previous work, which applied this method to the MNIST handwritten image dataset and demonstrated that the performance is comparable to CNNs, we extend our approach and evaluation to histopathological image datasets, including Warwick-QU and BreakHIS. Our results show that the FCA-based classifier offers comparable accuracy to deep neural classifiers while providing transparency into the classification process, an important factor in clinical decision-making.

To investigate slag carryover in the last stage of ladle teeming during continuous casting, a water model system was established. The nozzle flow rate of model ladle was controlled by slide gate, which was automatically governed by a control module. The prototype teeming process can be accurately modelled through the system. However, under the condition that the slide gate is fully opened, the teeming process in water model has a great deviation with the actual. The vortex critical height in the mode of dynamic changing of slide gate is obviously less than that under the condition of 100% opening. Furthermore, under the dynamic changing mode, effects of operating parameters on vortex formation and slag carryover have been investigated. The vortex critical height decreases as the residence time before teeming increases. The effect of initial water level on critical height of vortex is little. As the flow rate increases, the critical height of vortex increases significantly. The variation of vortex diameter experiences developing stage and matured stage. When oil is added into model ladle, an important change is that the critical height of vortex decreases. By contrast, the vortex diameter obviously increases. The vortex critical height decreases while oil is added into ladle, but it increases as the oil layer thickness increases.

Annona cherimola (cherimoya) is a species renowned for its delectable fruit and medicinal properties. In this study, we developed a chromosome-level genome assembly for the cherimoya ‘Booth’ cultivar from the United States. The genome assembly has a size of 794 Mb with a N50 = 97.59 Mb. The seven longest scaffolds account for 87.6% of the total genome length, which corresponds to the seven pseudo-chromosomes. A total of 45,272 protein-coding genes (≥30 aa) were predicted with 92.9% gene content completeness. No recent whole genome duplications were identified by an intra-genome collinearity analysis. Phylogenetic analysis supports that eudicots and magnoliids are more closely related to each other than to monocots. Moreover, the Magnoliales was found to be more closely related to the Laurales than the Piperales. Genome comparison revealed that the ‘Booth’ cultivar has 200 Mb less repeats than the Spanish cultivar ‘Fino de Jete’, despite their highly similar (>99%) genome sequence identity and collinearity. These two cultivars were diverged during the early Pleistocene (1.93 Mya), which suggests a different origin and domestication of the cherimoya. Terpene/terpenoid metabolism functions were found to be enriched in Magnoliales, while TNL (Toll/Interleukin-1-NBS-LRR) disease resistance gene has been lost in Magnoliales during evolution. We have also identified a gene cluster that is potentially responsible for the biosynthesis of acetogenins, a class of natural products found exclusively in Annonaceae. The cherimoya genome provides an invaluable resource for supporting characterization, conservation, and utilization of Annona genetic resources.

Previously, we described the RNA recombinants accumulating in tissues infected with the bromoviruses BMV (Brome mosaic virus) and CCMV (Cowpea chlorotic mottle virus). In this work, we characterize the recombinants encapsidated inside the purified virion particles of BMV and CCMV. By using a tool called the Viral Recombination Mapper (ViReMa) that detects recombination junctions, we analyzed a high number of high-throughput sequencing (HTS) short RNA sequence reads. Over 28% of BMV or CCMV RNA reads did not perfectly map to the viral genomes. ViReMa identified 1.40% and 1.83% of these unmapped reads as the RNA recombinants, respectively, in BMV and CCMV. Intra-segmental crosses were more frequent than the inter-segmental ones. Most intra-segmental junctions carried short insertions/deletions (indels) and caused frameshift mutations. The mutation hotspots clustered mainly within the open reading frames. Substitutions of various lengths were also identified, whereas a small fraction of crosses occurred between viral and their host RNAs. Our data reveal that the virions can package detectable amounts of multivariate recombinant RNAs, contributing to the flexible nature of the viral genomes.

Panicle development is crucial to increase the grain yield of rice (Oryza sativa). The molecular mechanisms of the control of panicle development in rice remain unclear. In this study, we identified a mutant with abnormal panicles, termed branch one seed 1-1 (bos1-1). The bos1-1 mutant showed pleiotropic defects in panicle development, such as the abortion of lateral spikelets and the decreased number of primary panicle branches and secondary panicle branches. A combined map-based cloning and MutMap approach was used to clone BOS1 gene. The bos1-1 mutation was located in chromosome 1. A T-to-A mutation in BOS1 was identified, which changed the codon from TAC to AAC, resulting in the amino acid change from tyrosine to asparagine. BOS1 gene encoded a grass-specific basic helix–loop–helix transcription factor, which is a novel allele of the previously cloned LAX PANICLE 1 (LAX1) gene. Spatial and temporal expression profile analyses showed that BOS1 was expressed in young panicles and was induced by phytohormones. BOS1 protein was mainly localized in the nucleus. The expression of panicle development-related genes, such as OsPIN2, OsPIN3, APO1, and FZP, was changed by bos1-1 mutation, suggesting that the genes may be the direct or indirect targets of BOS1 to regulate panicle development. The analysis of BOS1 genomic variation, haplotype, and haplotype network showed that BOS1 gene had several genomic variations and haplotypes. These results laid the foundation for us to further dissect the functions of BOS1.

In fungi, conserved homeobox-domain proteins are transcriptional regulators governing development. In Aspergillus species, several homeobox-domain transcription factor genes have been identified, among them, hbxA/hbx1. For instance, in the opportunistic human pathogen Aspergillus fumigatus, hbxA is involved in conidial production and germination, as well as virulence and secondary metabolism, including production of fumigaclavines, fumiquinazolines, and chaetominine. In the agriculturally important fungus Aspergillus flavus, disruption of hbx1 results in fluffy aconidial colonies unable to produce sclerotia. hbx1 also regulates production of aflatoxins, cyclopiazonic acid and aflatrem. Furthermore, transcriptome studies revealed that hbx1 has a broad effect on the A. flavus genome, including numerous genes involved in secondary metabolism. These studies underline the importance of the HbxA/Hbx1 regulator, not only in developmental processes but also in the biosynthesis of a broad number of fungal natural products, including potential medical drugs and mycotoxins. To gain further insight into the regulatory scope of HbxA in Aspergilli, we studied its role in the model fungus Aspergillus nidulans. Our present study of the A. nidulans hbxA-dependent transcriptome revealed that more than one thousand genes are differentially expressed when this regulator was not transcribed at wild-type levels, among them numerous transcription factors, including those involved in development as well as in secondary metabolism regulation. Furthermore, our metabolomics analyses revealed that production of several secondary metabolites, some of them associated with A. nidulans hbxA-dependent gene clusters, was also altered in deletion and overexpression hbxA strains compared to the wild type, including synthesis of nidulanins A, B and D, versicolorin A, sterigmatocystin, austinol, dehydroaustinol, and three unknown novel compounds.

The potato is the third largest food crop, and nitrogen fertilizer is important for increasing potato yields; however, the shallow root system of potatoes causes the nitrogen fertilizer utilization rate to be low, which results in waste and environmental pollution, meaning that high nitrogen efficiency breeding is highly significant for potatoes. In the high nitrogen efficiency breeding of potatoes, genes with a nitrogen-deficient response should first be identified, and RNA-seq is an efficient method for identifying nitrogen-deficiency-response genes. In this study, two potato cultivars, Dongnong 322 (DN322) and Dongnong 314 (DN314), were utilized, and two nitrogen fertilizer application rates (N0 and N1) were set for both cultivars. Through the determination of physiological indicators, we identified that DN314 is more sensitive to nitrogen fertilizer, while DN322 is relatively insensitive to nitrogen fertilizer. Samples were taken at the seedling and tuber formation stage. At the seedling stage, DN322 and DN314 had 573 and 150 differentially expressed genes (DEGs), while at the tuber formation stage, they had 59 and 1905 DEGs, respectively. A total of three genes related to a low-nitrogen response were obtained via the combined analysis of differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA), of which two genes were obtained at the tuber formation stage and one gene in the seedling stage, providing theoretical guidance for the high nitrogen efficiency breeding of potatoes.

ABSTRACT Anti-CRISPR (Acr) proteins are encoded by (pro)viruses to inhibit their host’s CRISPR-Cas systems. Genes encoding Acr and Aca (Acr associated) proteins often colocalize to form acr-aca operons. Here, we present AcaFinder as the first Aca genome mining tool. AcaFinder can (i) predict Acas and their associated acr-aca operons using guilt-by-association (GBA); (ii) identify homologs of known Acas using an HMM (Hidden Markov model) database; (iii) take input genomes for potential prophages, CRISPR-Cas systems, and self-targeting spacers (STSs); and (iv) provide a standalone program (https://github.com/boweny920/AcaFinder) and a web server (http://aca.unl.edu/Aca). AcaFinder was applied to mining over 16,000 prokaryotic and 142,000 gut phage genomes. After a multistep filtering, 36 high-confident new Aca families were identified, which is three times that of the 12 known Aca families. Seven new Aca families were from major human gut bacteria (Bacteroidota, Actinobacteria, and Fusobacteria) and their phages, while most known Aca families were from Proteobacteria and Firmicutes. A complex association network between Acrs and Acas was revealed by analyzing their operonic colocalizations. It appears very common in evolution that the same aca genes can recombine with different acr genes and vice versa to form diverse acr-aca operon combinations. IMPORTANCE At least four bioinformatics programs have been published for genome mining of Acrs since 2020. In contrast, no bioinformatics tools are available for automated Aca discovery. As the self-transcriptional repressor of acr-aca operons, Aca can be viewed as anti-anti-CRISPRs, with great potential in the improvement of CRISPR-Cas technology. Although all the 12 known Aca proteins contain a conserved helix-turn-helix (HTH) domain, not all HTH-containing proteins are Acas. However, HTH-containing proteins with adjacent Acr homologs encoded in the same genetic operon are likely Aca proteins. AcaFinder implements this guilt-by-association idea and the idea of using HMMs of known Acas for homologs into one software package. Applying AcaFinder in screening prokaryotic and gut phage genomes reveals a complex acr-aca operonic colocalization network between different families of Acrs and Acas.

Abstract Background Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA .

Heavy metal pollutions in the soils are increasingly threatening the global crop and food production. Using plant associated bacteria to remediate heavy metal contamination is a promising approach. We have isolated a cadmium (Cd) resistant Enterococcus faecium strain CX 2–6 from a heavy metal contaminated farmland. We have shown that: (i) CX 2–6 can tolerate cadmium (Cd) with a slower growth rate; (ii) The CX 2–6 complete genome is fully assembled using PacBio long reads; (iii) Differential expression analysis found 47% of CX 2–6 genes are significantly affected by Cd treatment and form three gene groups with distinct expression profiles; (iv) Differentially expressed genes (DEGs) form physically linked gene clusters in the CX 2–6 genome, and one of the gene clusters corresponds to a prophage that is unique to CX 2–6 and is strongly activated when Cd concentration is higher; (v) A majority of DEGs responding to Cd treatment are present in the core genome; and (vi) 55 noncoding RNA genes are identified and 49 of them are DEGs responding to cadmium stress. Our pan-genome analysis and comparative RNA-seq data analysis has significantly improved our understanding of the metabolic reprogramming of E. faecium CX 2–6 under Cd stress.

To optimize the submerged entry nozzle (SEN) for an ultra-thick slab mold, a mathematical model has been established. The molten steel flow and solidification, inclusion transports, and meniscus fluctuation have been investigated through the model. Compared with the concave-bottom SEN cases, the convex-bottom SEN decreases the imping depth of the jet flow and increases the horizontal velocity and temperature on the meniscus. However, the remelting of the solidified shell is dramatic for the convex-bottom case. The well depth of the concave-bottom SEN and the SEN’s submerged depth have little influence on molten steel flow and solidification. The effects of SEN port shape and port angle on the molten steel flow are significant. As the port shape changes from rectangle to square or the port downward angle decreases, the imping depth of jet flow decreases, the horizontal velocity and the temperature on the mold free surface increase. For the ultra-thick mold, a square-shaped-port SEN with a −10° downward angle is more beneficial by comprehensive consideration of molten steel flow and solidification, inclusion removal, and mold powder melting. The optimized SEN has been applied to the actual caster and its performance has been assessed, indicating that the SEN optimization is efficient.

Abstract Photoelectrocatalytic reduction of CO2 to fuels has great potential for reducing anthropogenic CO2 emissions and also lessening our dependence on fossil fuel energy. Herein, we report the successful development of a novel photoelectrocatalytic catalyst for the selective reduction of CO2 to methanol, comprising a copper catalyst modified with flower-like cerium oxide nanoparticles (CeO2 NPs) (a n-type semiconductor) and copper oxide nanoparticles (CuO NPs) (a p-type semiconductor). At an applied potential of − 1.0 V (vs SCE) under visible light irradiation, the CeO2 NPs/CuO NPs/Cu catalyst yielded methanol at a rate of 3.44 μmol cm−2 h−1, which was approximately five times higher than that of a CuO NPs/Cu catalyst (0.67 μmol cm−2 h−1). The carrier concentration increased by ~ 108 times when the flower-like CeO2 NPs were deposited on the CuO NPs/Cu catalyst, due to synergistic transfer of photoexcited electrons from the conduction band of CuO to that of CeO2, which enhanced both photocatalytic and photoelectrocatalytic CO2 reduction on the CeO2 NPs. The facile migration of photoexcited electrons and holes across the p–n heterojunction that formed between the CeO2 and CuO components was thus critical to excellent light-induced CO2 reduction properties of the CeO2 NPs/CuO NPs/Cu catalyst. Results encourage the wider application of composite semiconductor electrodes in carbon dioxide reduction.

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

Aspergillus flavus colonizes numerous oil seed crops such as maize, peanuts, treenuts and cottonseed worldwide, contaminating them with aflatoxins and other harmful toxins. Previously our lab characterized the gene rmtA, which encodes an arginine methyltransferase in A. flavus, and demonstrated its role governing the expression of regulators in the aflatoxin gene cluster and subsequent synthesis of toxin. Furthermore, our studies revealed that rmtA also controls conidial and sclerotial development implicating it as an epigenetic regulator in A. flavus. To confirm this, we performed a RNA sequencing analysis to ascertain the extent of rmtA’s influence on the transcriptome of A. flavus. In this analysis we identified over 2000 genes that were rmtA-dependent, including over 200 transcription factor genes, as well as an uncharacterized secondary metabolite gene cluster possibly responsible for the synthesis of an epidithiodiketopiperazine-like compound. Our results also revealed rmtA-dependent genes involved in multiple types of abiotic stress response in A. flavus. Importantly, hundreds of genes active during maize infection were also regulated by rmtA. In addition, in the animal infection model, rmtA was dispensable for virulence, however forced overexpression of rmtA increased mortality with respect to the wild type.

In filamentous fungi, homeobox proteins are conserved transcriptional regulators described to control conidiogenesis and fruiting body formation. Eight homeobox (hbx) genes are found in the genome of the aflatoxin-producing ascomycete, Aspergillus flavus. While loss-of-function of seven of the eight genes had little to no effect on fungal growth and development, disruption of hbx1, resulted in aconidial colonies and lack of sclerotial production. Furthermore, the hbx1 mutant was unable to produce aflatoxins B1 and B2, cyclopiazonic acid and aflatrem. In the present study, hbx1 transcriptome analysis revealed that hbx1 has a broad effect on A. flavus gene expression, and the effect of hbx1 increases overtime, impacting more than five thousand protein-coding genes. Among the affected genes, those in the category of secondary metabolism (SM), followed by that of cellular transport, were the most affected. Specifically, regarding the effect of hbx1 on SM, we found that genes in 44 SM gene clusters where upregulated while 49 were downregulated in the absence of hbx1, including genes in the SM clusters responsible for the synthesis of asparasone, piperazine and aflavarin, all known to be associated with sclerotia. In addition, our study revealed that hbx1 affects the expression of other transcription factor genes involved in development, including the conidiation central regulatory pathway and flb genes.

Zygnematophyceae green algae (ZGA) have been shown to be the closest relatives of land plants. Three nuclear genomes (Spirogloea muscicola, Mesotaenium endlicherianum, and Penium margaritaceum) of ZGA have been recently published, and more genomes are underway. Here we analyzed two Zygnema circumcarinatum strains SAG 698-1a (mating +) and SAG 698-1b (mating −) and found distinct cell sizes and other morphological differences. The molecular identities of the two strains were further investigated by sequencing their 18S rRNA, psaA and rbcL genes. These marker genes of SAG 698-1a were surprisingly much more similar to Z. cylindricum (SAG 698-2) than to SAG 698-1b. Phylogenies of these marker genes also showed that SAG 698-1a and SAG 698-1b were well separated into two different Zygnema clades, where SAG 698-1a was clustered with Z. cylindricum, while SAG 698-1b was clustered with Z. tunetanum. Additionally, physiological parameters like ETRmax values differed between SAG 698-1a and SAG 698-1b after 2 months of cultivation. The de-epoxidation state (DEPS) of the xanthophyll cycle pigments also showed significant differences. Surprisingly, the two strains could not conjugate, and significantly differed in the thickness of the mucilage layer. Additionally, ZGA cell walls are highly enriched with sticky and acidic polysaccharides, and therefore the widely used plant nuclear extraction protocols do not work well in ZGA. Here, we also report a fast and simple method, by mechanical chopping, for efficient nuclear extraction in the two SAG strains. More importantly, the extracted nuclei were further used for nuclear genome size estimation of the two SAG strains by flow cytometry (FC). To confirm the FC result, we have also used other experimental methods for nuclear genome size estimation of the two strains. Interestingly, the two strains were found to have very distinct nuclear genome sizes (313.2 ± 2.0 Mb in SAG 698-1a vs. 63.5 ± 0.5 Mb in SAG 698-1b). Our multiple lines of evidence strongly indicate that SAG 698-1a possibly had been confused with SAG 698-2 prior to 2005, and most likely represents Z. cylindricum or a closely related species.

ABSTRACT Water bloom development due to eutrophication constitutes a case of niche specialization among planktonic cyanobacteria, but the genomic repertoire allowing bloom formation in only some species has not been fully characterized. We posited that the habitat relevance of a trait begets its underlying genomic complexity, so that traits within the repertoire would be differentially more complex in species successfully thriving in that habitat than in close species that cannot. To test this for the case of bloom-forming cyanobacteria, we curated 17 potentially relevant query metabolic pathways and five core pathways selected according to existing ecophysiological literature. The available 113 genomes were split into those of blooming (45) or nonblooming (68) strains, and an index of genomic complexity for each strain’s version of each pathway was derived. We show that strain versions of all query pathways were significantly more complex in bloomers, with complexity in fact correlating positively with strain blooming incidence in 14 of those pathways. Five core pathways, relevant everywhere, showed no differential complexity or correlations. Gas vesicle, toxin and fatty acid synthesis, amino acid uptake, and C, N, and S acquisition systems were most strikingly relevant in the blooming repertoire. Further, we validated our findings using metagenomic gene expression analyses of blooming and nonblooming cyanobacteria in natural settings, where pathways in the repertoire were differentially overexpressed according to their relative complexity in bloomers, but not in nonbloomers. We expect that this approach may find applications to other habitats and organismal groups. IMPORTANCE We pragmatically delineate the trait repertoire that enables organismal niche specialization. We based our approach on the tenet, derived from evolutionary and complex-system considerations, that genomic units that can significantly contribute to fitness in a certain habitat will be comparatively more complex in organisms specialized to that habitat than their genomic homologs found in organisms from other habitats. We tested this in cyanobacteria forming harmful water blooms, for which decades-long efforts in ecological physiology and genomics exist. Our results essentially confirm that genomics and ecology can be linked through comparative complexity analyses, providing a tool that should be of general applicability for any group of organisms and any habitat, and enabling the posing of grounded hypotheses regarding the ecogenomic basis for diversification.

Gene expression profiling has been widely used to characterize cell status to reflect the health of the body, to diagnose genetic diseases, etc. In recent years, although the cost of genome-wide expression profiling is gradually decreasing, the cost of collecting expression profiles for thousands of genes is still very high. Considering gene expressions are usually highly correlated in humans, the expression values of the remaining target genes can be predicted by analyzing the values of 943 landmark genes. Hence, we designed an algorithm for predicting gene expression values based on XGBoost, which integrates multiple tree models and has stronger interpretability. We tested the performance of XGBoost model on the GEO dataset and RNA-seq dataset and compared the result with other existing models. Experiments showed that the XGBoost model achieved a significantly lower overall error than the existing D-GEX algorithm, linear regression, and KNN methods. In conclusion, the XGBoost algorithm outperforms existing models and will be a significant contribution to the toolbox for gene expression value prediction.

ABSTRACT Anti-CRISPR (Acr) loci/operons encode Acr proteins and Acr-associated (Aca) proteins. Forty-five Acr families have been experimentally characterized inhibiting seven subtypes of CRISPR-Cas systems. We have developed a bioinformatics pipeline to identify genomic loci containing Acr homologs and/or Aca homologs by combining three computational approaches: homology, guilt-by-association, and self-targeting spacers. Homology search found thousands of Acr homologs in bacterial and viral genomes, but most are homologous to AcrIIA7 and AcrIIA9. Investigating the gene neighborhood of these Acr homologs revealed that only a small percentage (23.0% in bacteria and 8.2% in viruses) of them have neighboring Aca homologs and thus form Acr-Aca operons. Surprisingly, although a self-targeting spacer is a strong indicator of the presence of Acr genes in a genome, a large percentage of Acr-Aca loci are found in bacterial genomes without self-targeting spacers or even without complete CRISPR-Cas systems. Additionally, for Acr homologs from genomes with self-targeting spacers, homology-based Acr family assignments do not always agree with the self-targeting CRISPR-Cas subtypes. Last, by investigating Acr genomic loci coexisting with self-targeting spacers in the same genomes, five known subtypes (I-C, I-E, I-F, II-A, and II-C) and five new subtypes (I-B, III-A, III-B, IV-A, and V-U4) of Acrs were inferred. Based on these findings, we conclude that the discovery of new anti-CRISPRs should not be restricted to genomes with self-targeting spacers and loci with Acr homologs. The evolutionary arms race of CRISPR-Cas systems and anti-CRISPR systems may have driven the adaptive and rapid gain and loss of these elements in closely related genomes. IMPORTANCE As a naturally occurring adaptive immune system, CRISPR-Cas (clustered regularly interspersed short palindromic repeats–CRISPR-associated genes) systems are widely found in bacteria and archaea to defend against viruses. Since 2013, the application of various bacterial CRISPR-Cas systems has become very popular due to their development into targeted and programmable genome engineering tools with the ability to edit almost any genome. As the natural off-switch of CRISPR-Cas systems, anti-CRISPRs have a great potential to serve as regulators of CRISPR-Cas tools and enable safer and more controllable genome editing. This study will help understand the relative usefulness of the three bioinformatics approaches for new Acr discovery, as well as guide the future development of new bioinformatics tools to facilitate anti-CRISPR research. The thousands of Acr homologs and hundreds of new anti-CRISPR loci identified in this study will be a valuable data resource for genome engineers to search for new CRISPR-Cas regulators.

Crassulacean acid metabolism (CAM) is a metabolic adaptation of photosynthesis that enhances water use efficiency. Here, via genomic analysis of Kalanchoë, the authors provide evidence for convergent evolution of protein sequence and temporal gene expression underpinning the multiple independent emergences of CAM.

Phytopathogenic Ascomycota are responsible for substantial economic losses each year, destroying valuable crops. The present study aims to provide new insights into phytopathogenicity in Ascomycota from a comparative genomic perspective. This has been achieved by categorizing orthologous gene groups (orthogroups) from 68 phytopathogenic and 24 non-phytopathogenic Ascomycota genomes into three classes: Core, (pathogen or non-pathogen) group-specific, and genome-specific accessory orthogroups. We found that (i) ~20% orthogroups are group-specific and accessory in the 92 Ascomycota genomes, (ii) phytopathogenicity is not phylogenetically determined, (iii) group-specific orthogroups have more enriched functional terms than accessory orthogroups and this trend is particularly evident in phytopathogenic fungi, (iv) secreted proteins with signal peptides and horizontal gene transfers (HGTs) are the two functional terms that show the highest occurrence and significance in group-specific orthogroups, (v) a number of other functional terms are also identified to have higher significance and occurrence in group-specific orthogroups. Overall, our comparative genomics analysis determined positive enrichment existing between orthogroup classes and revealed a prediction of what genomic characteristics make an Ascomycete phytopathogenic. We conclude that genes shared by multiple phytopathogenic genomes are more important for phytopathogenicity than those that are unique in each genome.

Previous analysis of charophyte green algal (CGA) genomes and transcriptomes for specific protein families revealed that numerous land plant characteristics had already evolved in CGA. In this study, we have sequenced and assembled the transcriptome of Zygnema circumcarinatum UTEX 1559, and combined its predicted protein sequences with those of 13 additional species [five embryophytes (Emb), eight charophytes (Cha), and two chlorophytes (Chl) as the outgroup] for a comprehensive comparative genomics analysis. In total 25,485 orthologous gene clusters (OGCs, equivalent to protein families) of the 14 species were classified into nine OGC groups. For example, the Cha+Emb group contains 4,174 OGCs found in both Cha and Emb but not Chl species, representing protein families that have evolved in the common ancestor of Cha and Emb. Different OGC groups were subjected to a Gene Ontology (GO) enrichment analysis with the Chl+Cha+Emb group (including 5,031 OGCs found in Chl and Cha and Emb) as the control. Interestingly, nine of the 20 top enriched GO terms in the Cha+Emb group are cell wall-related, such as biological processes involving celluloses, pectins, lignins, and xyloglucans. Furthermore, three glycosyltransferase families (GT2, 8, 43) were selected for in-depth phylogenetic analyses, which confirmed their presence in UTEX 1559. More importantly, of different CGA groups, only Zygnematophyceae has land plant cellulose synthase (CesA) orthologs, while other charophyte CesAs form a CGA-specific CesA-like (Csl) subfamily (likely also carries cellulose synthesis activity). Quantitative real-time-PCR experiments were performed on selected GT family genes in UTEX 1559. After osmotic stress treatment, significantly elevated expression was found for GT2 family genes ZcCesA, ZcCslC and ZcCslA-like (possibly mannan and xyloglucan synthases, respectively), as well as for GT8 family genes (possibly pectin synthases). All these suggest that the UTEX 1559 cell wall polysaccharide synthesis-related genes respond to osmotic stress in a manner that is similar to land plants.

ABSTRACT Orphan genes (also known as ORFans [i.e., orphan open reading frames]) are new genes that enable an organism to adapt to its specific living environment. Our focus in this study is to compare ORFans between pathogens (P) and nonpathogens (NP) of the same genus. Using the pangenome idea, we have identified 130,169 ORFans in nine bacterial genera (505 genomes) and classified these ORFans into four groups: (i) SS-ORFans (P), which are only found in a single pathogenic genome; (ii) SS-ORFans (NP), which are only found in a single nonpathogenic genome; (iii) PS-ORFans (P), which are found in multiple pathogenic genomes; and (iv) NS-ORFans (NP), which are found in multiple nonpathogenic genomes. Within the same genus, pathogens do not always have more genes, more ORFans, or more pathogenicity-related genes (PRGs)—including prophages, pathogenicity islands (PAIs), virulence factors (VFs), and horizontal gene transfers (HGTs)—than nonpathogens. Interestingly, in pathogens of the nine genera, the percentages of PS-ORFans are consistently higher than those of SS-ORFans, which is not true in nonpathogens. Similarly, in pathogens of the nine genera, the percentages of PS-ORFans matching the four types of PRGs are also always higher than those of SS-ORFans, but this is not true in nonpathogens. All of these findings suggest the greater importance of PS-ORFans for bacterial pathogenicity. IMPORTANCE Recent pangenome analyses of numerous bacterial species have suggested that each genome of a single species may have a significant fraction of its gene content unique or shared by a very few genomes (i.e., ORFans). We selected nine bacterial genera, each containing at least five pathogenic and five nonpathogenic genomes, to compare their ORFans in relation to pathogenicity-related genes. Pathogens in these genera are known to cause a number of common and devastating human diseases such as pneumonia, diphtheria, melioidosis, and tuberculosis. Thus, they are worthy of in-depth systems microbiology investigations, including the comparative study of ORFans between pathogens and nonpathogens. We provide direct evidence to suggest that ORFans shared by more pathogens are more associated with pathogenicity-related genes and thus are more important targets for development of new diagnostic markers or therapeutic drugs for bacterial infectious diseases.

Blue mold is a postharvest rot of pomaceous fruits caused by Penicillium expansum and a number of other Penicillium species. The genome of the highly aggressive P. expansum strain R19 was re-sequenced and analyzed together with the genome of the less aggressive P. solitum strain RS1. Whole genome scale similarities and differences were examined. A phylogenetic analysis of P. expansum, P. solitum, and several closely related Penicillium species revealed that the two pathogens isolated from decayed apple with blue mold symptoms are not each other’s closest relatives. Among a total of 10,560 and 10,672 protein coding sequences respectively, a comparative genomics analysis revealed 41 genes in P. expansum R19 and 43 genes in P. solitum RS1 that are unique to these two species. These genes may be associated with pome fruit–fungal interactions, subsequent decay processes, and mycotoxin accumulation. An intact patulin gene cluster consisting of 15 biosynthetic genes was identified in the patulin producing P. expansum strain R19, while only a remnant, seven-gene cluster was identified in the patulin-deficient P. solitum strain. However, P. solitum contained a large number of additional secondary metabolite gene clusters, indicating that this species has the potential capacity to produce an array of known as well as not-yet-identified products of possible toxicological or biotechnological interest.

To investigate the gas agitation characteristics of side blowing, the fluid flow and mixing phenomenon in a 1:3 scale model ladle of a 150 t industrial gas-stirred ladle with bottom and side plugs were studied by using physical and numerical modelings together. Side blowing enhanced the horizontal flow of water in the model ladle. Compared with bottom blowing, side blowing that is close to the ladle bottom with more than two plugs increases the average velocity of water, which represents the agitation power, improves the uniformity of water velocity distribution, reduces the stagnant region rate, and shortens the mixing time. The mixing time of dual bottom plugs is almost 1.5 times of that of four side plugs at 116 mm under the same flow rate. The mixing time is not only influenced by the agitation power but also by the uniformity of water velocity distribution. Although the agitation power of four side plugs at 450 mm under the flow rate of 1.8 m3/h is about 1.5 times of that at 116 mm with 0.6 m3/h. The mixing time of the 1.8 m3/h flow rate is about 1.2 times of that of the 0.6 m3/h because of the different water velocity distributions.

Sedimentation of free-floating grains is the main origin of the negative centerline segregation in direct-chill casting of aluminum alloys. This study examines the motion and distribution of the floating grains during casting using experimental measurements and numerical modeling. The typical floating grains consisting of interior solute-lean coarse dendrites and periphery fine dendrites were experimentally observed only in the central region of the billet along with the negative segregation. The billet exhibits the strongest segregation at the center where the most floating grains are found. In simulations, under the action of the convection and the underlying forces, the grains floating in the transition region exhibit different motion behaviors, i.e., settling to the mushy zone, floating in the slurry zone, and moving upward to the liquid zone. However, most grains were transported to the central region of the billet and then were captured by the mushy zone and settled. Therefore, the floating grains comprise the largest share of the grain structure at the center of the billet, in agreement with the experimental results. Moreover, the simulation results indicate that the increased size of the grains promotes the sedimentation of the floating grains. These results are important for the future alleviation of negative centerline segregation in direct-chill casting of aluminum alloys.

Horizontal gene transfer (HGT) is a fast-track mechanism that allows genetically unrelated organisms to exchange genes for rapid environmental adaptation. We developed a new phyletic distribution-based software, HGT-Finder, which implements a novel bioinformatics algorithm to calculate a horizontal transfer index and a probability value for each query gene. Applying this new tool to the Aspergillus fumigatus, Aspergillus flavus, and Aspergillus nidulans genomes, we found 273, 542, and 715 transferred genes (HTGs), respectively. HTGs have shorter length, higher guanine-cytosine (GC) content, and relaxed selection pressure. Metabolic process and secondary metabolism functions are significantly enriched in HTGs. Gene clustering analysis showed that 61%, 41% and 74% of HTGs in the three genomes form physically linked gene clusters (HTGCs). Overlapping manually curated, secondary metabolite gene clusters (SMGCs) with HTGCs found that 9 of the 33 A. fumigatus SMGCs and 31 of the 65 A. nidulans SMGCs share genes with HTGCs, and that HTGs are significantly enriched in SMGCs. Our genome-wide analysis thus presented very strong evidence to support the hypothesis that HGT has played a very critical role in the evolution of SMGCs. The program is freely available at http://cys.bios.niu.edu/HGTFinder/ HGTFinder.tar.gz.

Detecting disease-related gene modules by analyzing gene expression data is of great significance. It is helpful for exploratory analysis of the interaction mechanisms of genes under complex disease phenotypes. The multi-label propagation algorithm (MLPA) has been widely used in module detection for its fast and easy implementation. The accuracy of MLPA greatly depends on the connections between nodes, and most existing research focuses on measuring the similarity between nodes. However, MLPA does not perform well with loose connections between disease-related genes. Moreover, the biological significance of modules obtained by MLPA has not been demonstrated. To solve these problems, we designed a double label propagation clustering algorithm (DLPCA) based on MLPA to study Huntington's disease. In DLPCA, in addition to category labels, we introduced pathogenic labels to supervise the process of multi-label propagation clustering. The pathogenic labels contain pathogenic information about disease genes and the hierarchical structure of gene expression data. Experimental results demonstrated the superior performance of DLPCA compared with other conventional gene-clustering algorithms.

The glycosyltransferase family 43 (GT43) has been suggested to be involved in the synthesis of xylans in plant cell walls and proteoglycans in animals. Very recently GT43 family was also found in Charophycean green algae (CGA), the closest relatives of extant land plants. Here we present evidence that non-plant and non-animal early eukaryotes such as fungi, Haptophyceae, Choanoflagellida, Ichthyosporea and Haptophyceae also have GT43-like genes, which are phylogenetically close to animal GT43 genes. By mining RNA sequencing data (RNA-Seq) of selected plants, we showed that CGA have evolved three major groups of GT43 genes, one orthologous to IRX14 (IRREGULAR XYLEM14), one orthologous to IRX9/IRX9L and the third one ancestral to all land plant GT43 genes. We confirmed that land plant GT43 has two major clades A and B, while in angiosperms, clade A further evolved into three subclades and the expression and motif pattern of A3 (containing IRX9) are fairly different from the other two clades likely due to rapid evolution. Our in-depth sequence analysis contributed to our overall understanding of the early evolution of GT43 family and could serve as an example for the study of other plant cell wall-related enzyme families.

Macrosegregation, serving as a major defect in billets, can severely degrade material homogeneity. Better understanding of the physical characteristics of macrosegregation through numerical simulation could significantly contribute to the segregation control. The main purpose of this study was to predict macrosegregation in continuously cast billets with a newly developed three-dimensional macrosegregation model. The fluid flow, solidification, and solute transport in the entire billet region were solved and analyzed. Flow patterns, revealing a typical melt recirculation at the upper region of mold and thermosolutal convection at the secondary cooling zone, significantly affect the solidification and solute distribution. The solute redistribution occurring with thermosolutal convection at the solidification front contributes significantly to continued macrosegregation as solidification proceeds. The results of this study show that the equilibrium partition coefficient is mostly responsible for the magnitude of macrosegregation, while comparison between solute P and S indicated that diffusion coefficients also have some amount of influence. Typical macrosegregation patterns containing a positively segregated peak at the centerline and negatively segregated minima at either side were obtained via the proposed three-dimensional macrosegregation model, which validated by the measured surface temperatures and segregation degree.

A challenge in phylogenetic inference of gene trees is how to properly sample a large pool of homologous sequences to derive a good representative subset of sequences. Such a need arises in various applications, e.g. when (1) accuracy-oriented phylogenetic reconstruction methods may not be able to deal with a large pool of sequences due to their high demand in computing resources; (2) applications analyzing a collection of gene trees may prefer to use trees with fewer operational taxonomic units (OTUs), for instance for the detection of horizontal gene transfer events by identifying phylogenetic conflicts; and (3) the pool of available sequences is biased towards extensively studied species. In the past, the creation of subsamples often relied on manual selection. Here we present an Automated sequence-Sampling method for improving the Taxonomic diversity of gene phylogenetic trees, AST, to obtain representative sequences that maximize the taxonomic diversity of the sampled sequences. To demonstrate the effectiveness of AST, we have tested it to solve four problems, namely, inference of the evolutionary histories of the small ribosomal subunit protein S5 of E. coli, 16 S ribosomal RNAs and glycosyl-transferase gene family 8, and a study of ancient horizontal gene transfers from bacteria to plants. Our results show that the resolution of our computational results is almost as good as that of manual inference by domain experts, hence making the tool generally useful to phylogenetic studies by non-phylogeny specialists. The program is available at http://csbl.bmb.uga.edu/~zhouchan/AST.php.

Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.

Regulons, as groups of transcriptionally co-regulated operons, are the basic units of cellular response systems in bacterial cells. While the concept has been long and widely used in bacterial studies since it was first proposed in 1964, very little is known about how its component operons are arranged in a bacterial genome. We present a computational study to elucidate of the organizational principles of regulons in a bacterial genome, based on the experimentally validated regulons of E. coli and B. subtilis. Our results indicate that (1) genomic locations of transcriptional factors (TFs) are under stronger evolutionary constraints than those of the operons they regulate so changing a TF's genomic location will have larger impact to the bacterium than changing the genomic position of any of its target operons; (2) operons of regulons are generally not uniformly distributed in the genome but tend to form a few closely located clusters, which generally consist of genes working in the same metabolic pathways; and (3) the global arrangement of the component operons of all the regulons in a genome tends to minimize a simple scoring function, indicating that the global arrangement of regulons follows simple organizational principles.

Nucleotide-diphospho-sugars (NDP-sugars) are the building blocks of diverse polysaccharides and glycoconjugates in all organisms. In plants, 11 families of NDP-sugar interconversion enzymes (NSEs) have been identified, each of which interconverts one NDP-sugar to another. While the functions of these enzyme families have been characterized in various plants, very little is known about their evolution and origin. Our phylogenetic analyses indicate that all the 11 plant NSE families are distantly related and most of them originated from different progenitor genes, which have already diverged in ancient prokaryotes. For instance, all NSE families are found in the lower land plant mosses and most of them are also found in aquatic algae, implicating that they have already evolved to be capable of synthesizing all the 11 different NDP-sugars. Particularly interesting is that the evolution of RHM (UDP-L-rhamnose synthase) manifests the fusion of genes of three enzymatic activities in early eukaryotes in a rather intriguing manner. The plant NRS/ER (nucleotide-rhamnose synthase/epimerase-reductase), on the other hand, evolved much later from the ancient plant RHMs through losing the N-terminal domain. Based on these findings, an evolutionary model is proposed to explain the origin and evolution of different NSE families. For instance, the UGlcAE (UDP-D-glucuronic acid 4-epimerase) family is suggested to have evolved from some chlamydial bacteria. Our data also show considerably higher sequence diversity among NSE-like genes in modern prokaryotes, consistent with the higher sugar diversity found in prokaryotes. All the NSE families are widely found in plants and algae containing carbohydrate-rich cell walls, while sporadically found in animals, fungi and other eukaryotes, which do not have or have cell walls with distinct compositions. Results of this study were shown to be highly useful for identifying unknown genes for further experimental characterization to determine their functions in the synthesis of diverse glycosylated molecules.