A probabilistic model of local sequence alignment that simplifies statistical significance estimation

Venom exaptation and adaptation during the trophic switch to blood-feeding by kissing bugs

Kissing bugs are known to produce anticoagulant venom that facilitates blood-feeding. However, it is unknown how this saliva evolved and if the venom produced by the entomophagous ancestors of kissing bugs would have helped or hindered the trophic shift. In this study, we show that venoms produced by extant predatory assassin bugs have strong anticoagulant properties mediated chiefly by proteolytic degradation of fibrinogen, and additionally contain anticoagulant disulfide-rich peptides. However, venom produced by predatory species also has pain-inducing and membrane-permeabilizing activities that would be maladaptive for blood-feeding, and which venom of the blood-feeding species lack. This study demonstrates that venom produced by the predatory ancestors of kissing bugs was exapted for the trophic switch to blood-feeding by virtue of its anticoagulant properties. Further adaptation to blood-feeding occurred by downregulation of venom toxins with proteolytic, cytolytic, and pain-inducing activities, and upregulation and neofunctionalization of toxins with anticoagulant activity independent of proteolysis.

learnMSA2: deep protein multiple alignments with large language and hidden Markov models

MOTIVATION

For the alignment of large numbers of protein sequences, tools are predominant that decide to align two residues using only simple prior knowledge, e.g. amino acid substitution matrices, and using only part of the available data. The accuracy of state-of-the-art programs declines with decreasing sequence identity and when increasingly large numbers of sequences are aligned. Recently, transformer-based deep-learning models started to harness the vast amount of protein sequence data, resulting in powerful pretrained language models with the main purpose of generating high-dimensional numerical representations, embeddings, for individual sites that agglomerate evolutionary, structural, and biophysical information.

RESULTS

We extend the traditional profile hidden Markov model so that it takes as inputs unaligned protein sequences and the corresponding embeddings. We fit the model with gradient descent using our existing differentiable hidden Markov layer. All sequences and their embeddings are jointly aligned to a model of the protein family. We report that our upgraded HMM-based aligner, learnMSA2, combined with the ProtT5-XL protein language model aligns on average almost 6% points more columns correctly than the best amino acid-based competitor and scales well with sequence number. The relative advantage of learnMSA2 over other programs tends to be greater when the sequence identity is lower and when the number of sequences is larger. Our results strengthen the evidence on the rich information contained in protein language models' embeddings and their potential downstream impact on the field of bioinformatics. Availability and implementation:  https://github.com/Gaius-Augustus/learnMSA, PyPI and Bioconda, evaluation: https://github.com/felbecker/snakeMSA.

Computational analysis of human gut microbial prolyl oligopeptidases (POPs) reveal candidate genes as therapeutics for celiac disease

Celiac disease (CD) is a common autoimmune disorder in which the patients are unable to digest gluten, which is present in foods made up of wheat, barley and rye. Whilst diagnosis happens late in 80% of the cases, avoidance of such foods appears to be the common solution. Alternative management strategies are required for the patients and their families since CD is also genetically carried over. Probiotic therapeutics and the consumption of appropriate enzymes, such as prolyloligopeptidases (POPs), from gut-friendly bacteria could reduce the disease burden and provide a better lifestyle for CD patients. We have examined around 5000 gut bacterial genomes and identified nearly 4000 non-redundant putative POPs. A select set of 10 gut bacterial POP sequences were subject to three-dimensional modelling, ligand docking and molecular dynamics simulations where stable interactions were observed between the POPs and gluten peptides. Our study provides sequence and structural analysis of potential POP enzymes in gut bacterial genomes, which form a strong basis to offer probiotic solutions to CD patients. In particular, these enzymes could be lead future therapeutics for this disease.

OrthoPhyl-streamlining large-scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales

There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

BACKGROUND

Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER.

RESULTS

The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a ∼  $3000 Xilinx Alveo U50 FPGA accelerator card, ∼  227× faster than the optimized SSV implementation in nhmmer. Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer's SSV with one thread and 35× faster than nhmmer with four threads, and uses ∼  31% the energy of a traditional high end Intel CPU.

CONCLUSIONS

HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

Extant and extinct bilby genomes combined with Indigenous knowledge improve conservation of a unique Australian marsupial

Ninu (greater bilby, Macrotis lagotis) are desert-dwelling, culturally and ecologically important marsupials. In collaboration with Indigenous rangers and conservation managers, we generated the Ninu chromosome-level genome assembly (3.66 Gbp) and genome sequences for the extinct Yallara (lesser bilby, Macrotis leucura). We developed and tested a scat single-nucleotide polymorphism panel to inform current and future conservation actions, undertake ecological assessments and improve our understanding of Ninu genetic diversity in managed and wild populations. We also assessed the beneficial impact of translocations in the metapopulation (N = 363 Ninu). Resequenced genomes (temperate Ninu, 6; semi-arid Ninu, 6; and Yallara, 4) revealed two major population crashes during global cooling events for both species and differences in Ninu genes involved in anatomical and metabolic pathways. Despite their 45-year captive history, Ninu have fewer long runs of homozygosity than other larger mammals, which may be attributable to their boom-bust life history. Here we investigated the unique Ninu biology using 12 tissue transcriptomes revealing expression of all 115 conserved eutherian chorioallantoic placentation genes in the uterus, an XY1Y2 sex chromosome system and olfactory receptor gene expansions. Together, we demonstrate the holistic value of genomics in improving key conservation actions, understanding unique biological traits and developing tools for Indigenous rangers to monitor remote wild populations.

Phylogeny, envenomation syndrome, and membrane permeabilising venom produced by Australia's electric caterpillar Comana monomorpha

Zygaenoidea is a superfamily of lepidopterans containing many venomous species, including the Limacodidae (nettle caterpillars) and Megalopygidae (asp caterpillars). Venom proteomes have been recently documented for several species from each of these families, but further data are required to understand the evolution of venom in Zygaenoidea. In this study, we examined the 'electric' caterpillar from North-Eastern Australia, a limacodid caterpillar densely covered in venomous spines. We used DNA barcoding to identify this caterpillar as the larva of the moth Comana monomorpha (Turner, 1904). We report the clinical symptoms of C. monomorpha envenomation, which include acute pain, and erythema and oedema lasting for more than a week. Combining transcriptomics of venom spines with proteomics of venom harvested from the spine tips revealed a venom markedly different in composition from previously examined limacodid venoms that are rich in peptides. In contrast, the venom of C. monomorpha is rich in aerolysin-like proteins similar to those found in venoms of asp caterpillars (Megalopygidae). Consistent with this composition, the venom potently permeabilises sensory neurons and human neuroblastoma cells. This study highlights the diversity of venom composition in Limacodidae.

Sensitive and error-tolerant annotation of protein-coding DNA with BATH

Summary

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes.

Availability and implementation

The software is available at https://github.com/TravisWheelerLab/BATH.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Phenotypic, genotypic, and resistome of mesophilic spore-forming bacteria isolated from pasteurized liquid whole egg

The production of whole-liquid eggs is of significant economic and nutritional importance. This study aimed to assess the phenotypic and genotypic diversity of mesophilic aerobic spore-forming bacteria (n = 200) isolated from pasteurized whole liquid egg and liquid egg yolk. The majority of the isolates were identified as belonging to the genera Bacillus (86 %), followed by Brevibacillus (10 %) and Lysinibacillus (4 %). For the phenotypic characterization, isolates were subjected to various heat shocks, with the most significant reductions observed at 80 °C/30 min and 90 °C/10 min for isolates recovered from raw materials. On the other hand, the decrease was similar for isolates recovered from raw material and final product at 100 °C/5 min and 110 °C/5 min. Genotypic genes related to heat resistance (cdnL, spoVAD, dacB, clpC, dnaK, and yitF/Tn1546) were examined for genotypic characterization. The dnaK gene showed a positive correlation with the highest thermal condition tested (110 °C/5 min), while 100 °C/5 min had the highest number of positively correlated genes (clpC, cdnL, yitF/Tn1546, and spoVAD). Whole Genome Sequencing of four strains revealed genes related to sporulation, structure formation, initiation and regulation, stress response, and DNA repair in vegetative cells. The findings of this study indicate that these mesophilic aerobic spore-forming bacteria may adopt several strategies to persist through the process and reach the final product. As the inactivation of these microorganisms during egg processing is challenging, preventing raw materials contamination and their establishment in processing premises must be reinforced.

WAS IT A MATch I SAW? Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences

Background

Software for labeling biological sequences typically produces a theory-based statistic for each match (the E-value) that indicates the likelihood of seeing that match's score by chance. E-values accurately predict false match rate for comparisons of random (shuffled) sequences, and thus provide a reasoned mechanism for setting score thresholds that enable high sensitivity with low expected false match rate. This threshold-setting strategy is challenged by real biological sequences, which contain regions of local repetition and low sequence complexity that cause excess matches between non-homologous sequences. Knowing this, tool developers often develop benchmarks that use realistic-seeming decoy sequences to explore empirical tradeoffs between sensitivity and false match rate. A recent trend has been to employ reversed biological sequences as realistic decoys, because these preserve the distribution of letters and the existence of local repeats, while disrupting the original sequence's functional properties. However, we and others have observed that sequences appear to produce high scoring alignments to their reversals with surprising frequency, leading to overstatement of false match risk that may negatively affect downstream analysis.

Results

We demonstrate that an alignment between a sequence S and its (possibly mutated) reversal tends to produce higher scores than alignment between truly unrelated sequences, even when S is a shuffled string with no notable repetitive or low-complexity regions. This phenomenon is due to the unintuitive fact that (even randomly shuffled) sequences contain palindromes that are on average longer than the longest common substrings (LCS) shared between permuted variants of the same sequence. Though the expected palindrome length is only slightly larger than the expected LCS, the distribution of alignment scores involving reversed sequences is strongly right-shifted, leading to greatly increased frequency of high-scoring alignments to reversed sequences.

Impact

Overestimates of false match risk can motivate unnecessarily high score thresholds, leading to potentially reduced true match sensitivity. Also, when tool sensitivity is only reported up to the score of the first matched decoy sequence, a large decoy set consisting of reversed sequences can obscure sensitivity differences between tools. As a result of these observations, we advise that reversed biological sequences be used as decoys only when care is taken to remove positive matches in the original (un-reversed) sequences, or when overstatement of false labeling is not a concern. Though the primary focus of the analysis is on sequence annotation, we also demonstrate that the prevalence of internal palindromes may lead to an overstatement of the rate of false labels in protein identification with mass spectrometry.

The defensome of complex bacterial communities

Bacteria have developed various defense mechanisms to avoid infection and killing in response to the fast evolution and turnover of viruses and other genetic parasites. Such pan-immune system (defensome) encompasses a growing number of defense lines that include well-studied innate and adaptive systems such as restriction-modification, CRISPR-Cas and abortive infection, but also newly found ones whose mechanisms are still poorly understood. While the abundance and distribution of defense systems is well-known in complete and culturable genomes, there is a void in our understanding of their diversity and richness in complex microbial communities. Here we performed a large-scale in-depth analysis of the defensomes of 7759 high-quality bacterial population genomes reconstructed from soil, marine, and human gut environments. We observed a wide variation in the frequency and nature of the defensome among large phyla, which correlated with lifestyle, genome size, habitat, and geographic background. The defensome's genetic mobility, its clustering in defense islands, and genetic variability was found to be system-specific and shaped by the bacterial environment. Hence, our results provide a detailed picture of the multiple immune barriers present in environmentally distinct bacterial communities and set the stage for subsequent identification of novel and ingenious strategies of diversification among uncultivated microbes.

Molecular Characterization and Phylogenetic Analysis of Centipedegrass [Eremochloa ophiuroides (Munro) Hack.] Based on the Complete Chloroplast Genome Sequence

Centipedegrass (Eremochloa ophiuroides) is an important warm-season grass plant used as a turfgrass as well as pasture grass in tropical and subtropical regions, with wide application in land surface greening and soil conservation in South China and southern United States. In this study, the complete cp genome of E. ophiuroides was assembled using high-throughput Illumina sequencing technology. The circle pseudomolecule for E. ophiuroides cp genome is 139,107 bp in length, with a quadripartite structure consisting of a large single copyregion of 82,081 bp and a small single copy region of 12,566 bp separated by a pair of inverted repeat regions of 22,230 bp each. The overall A + T content of the whole genome is 61.60%, showing an asymmetric nucleotide composition. The genome encodes a total of 131 gene species, composed of 20 duplicated genes within the IR regions and 111 unique genes comprising 77 protein-coding genes, 30 transfer RNA genes, and 4 ribosome RNA genes. The complete cp genome sequence contains 51 long repeats and 197 simple sequence repeats, and a high degree of collinearity among E. ophiuroide and other Gramineae plants was disclosed. Phylogenetic analysis showed E. ophiuroides, together with the other two Eremochloa species, is closely related to Mnesithea helferi within the subtribe Rottboelliinae. These findings will be beneficial for the classification and identification of the Eremochloa taxa, phylogenetic resolution, novel gene discovery, and functional genomic studies for the genus Eremochloa.

nail: software for high-speed, high-sensitivity protein sequence annotation

" Fast is fine, but accuracy is final. " -- Wyatt Earp.

Background

The extreme diversity of newly sequenced organisms and considerable scale of modern sequence databases lead to a tension between competing needs for sensitivity and speed in sequence annotation, with multiple tools displacing the venerable BLAST software suite on one axis or another. Alignment based on profile hidden Markov models (pHMMs) has demonstrated state of art sensitivity, while recent algorithmic advances have resulted in hyper-fast annotation tools with sensitivity close to that of BLAST.

Results

Here, we introduce a new tool that bridges the gap between advances in these two directions, reaching speeds comparable to fast annotation methods such as MMseqs2 while retaining most of the sensitivity offered by pHMMs. The tool, called nail, implements a heuristic approximation of the pHMM Forward/Backward (FB) algorithm by identifying a sparse subset of the cells in the FB dynamic programming matrix that contains most of the probability mass. The method produces an accurate approximation of pHMM scores and E-values with high speed and small memory requirements. On a protein benchmark, nail recovers the majority of recall difference between MMseqs2 and HMMER, with run time ~26x faster than HMMER3 (only ~2.4x slower than MMseqs2's sensitive variant). nail is released under the open BSD-3-clause license and is available for download at https://github.com/TravisWheelerLab/nail.

A chromosome-level genome assembly of a free-living white-crowned sparrow (Zonotrichia leucophrys gambelii)

The white-crowned sparrow, Zonotrichia leucophrys, is a passerine bird with a wide distribution and it is extensively adapted to environmental changes. It has historically acted as a model species in studies on avian ecology, physiology and behaviour. Here, we present a high-quality chromosome-level genome of Zonotrichia leucophrys using PacBio and OmniC sequencing data. Gene models were constructed by combining RNA-seq and Iso-seq data from liver, hypothalamus, and ovary. In total a 1,123,996,003 bp genome was generated, including 31 chromosomes assembled in complete scaffolds along with other, unplaced scaffolds. This high-quality genome assembly offers an important genomic resource for the research community using the white-crowned sparrow as a model for understanding avian genome biology and development, and provides a genomic basis for future studies, both fundamental and applied.

Sensitive and error-tolerant annotation of protein-coding DNA with BATH

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long read sequencing data and in the context of pseudogenes.

Denitrification fractionates N and O isotopes of nitrate following a ratio independent of carbon sources in freshwaters

The stable isotope technique has been used in tracking nitrogen cycling processes, but the isotopic characteristics are influenced by environmental conditions. To better understand the variability of nitrate isotopes in nature, we investigated the influence of organic carbon sources on isotope fractionation characteristics during microbial denitrification. Denitrifying cultures were inoculated with freshwater samples and enriched with five forms of organic compounds, that is, acetate, citrate, glucose, cellobiose, and leucine. Though the isotope enrichment factors of nitrogen and oxygen (15 ε and 18 ε) changed with carbon sources, 18 ε/15 ε always followed a proportionality near 1. Genome-centred metagenomics revealed the enrichment of a few populations, such as Pseudomonas, Enterobacter, and Atlantibacter, most of which contained both NapA- and NarG-type nitrate reductases. Metatranscriptome showed that both NapA and NarG were expressed but to different extents in the enrichments. Furthermore, isotopic data collected from a deep reservoir was analysed. The results showed δ18 O- and δ15 N-nitrate did not correlate in the surface water where nitrification was active, but 18 ε/15 ε followed a proportionality of 1.05 ± 011 in deeper waters (≥ 12 m) where denitrification controlled the nitrate isotope. The independence of 18 ε/15 ε from carbon sources provides an opportunity to determine heterotrophic denitrification and helps the interpretation of nitrate isotopes in freshwaters.

Life strategies and metabolic interactions of core microbes during thiosulphate-based denitrification

Sulphur-driven denitrification is a low-cost process for the treatment of nitrate-contaminated water. However, a comprehensive understanding of core populations and microbial interactions of a sulphur-based denitrifying system is lacking. This study presents results from three replicated denitrifying systems amended with thiosulphate and operated under a low C/N ratio. Amplicon sequencing revealed gradual enrichments of a few abundant denitrifiers. Based on genome-centred metagenomics and metatranscriptomics, a core set of microbes was identified in the systems, with Pseudomonas 1 and Thauera 2 being the most abundant ones. Although the replicates showed different enrichments, generalized observations were summarized. Most core populations conserved energy from denitrification coupled with sulphur. Pseudomonas 1 and Thauera 2 were able to finish complete denitrification. Surprisingly, they were also able to synthesize almost all amino acids and vitamins. In contrast, less abundant members, including Pseudomonas 2, were relatively auxotrophic and required an exogenous supply of amino acids and vitamins. The high expression of enzymes involved in biosynthesis and transport systems indicated their syntrophic relationships. The genomic findings suggested life strategies and interactions of the core thiosulphate-based denitrifying microbiome, with implications for nitrate-polluted water remediation.

Long-term mitigation of drought changes the functional potential and life-strategies of the forest soil microbiome involved in organic matter decomposition

Climate change can alter the flow of nutrients and energy through terrestrial ecosystems. Using an inverse climate change field experiment in the central European Alps, we explored how long-term irrigation of a naturally drought-stressed pine forest altered the metabolic potential of the soil microbiome and its ability to decompose lignocellulolytic compounds as a critical ecosystem function. Drought mitigation by a decade of irrigation stimulated profound changes in the functional capacity encoded in the soil microbiome, revealing alterations in carbon and nitrogen metabolism as well as regulatory processes protecting microorganisms from starvation and desiccation. Despite the structural and functional shifts from oligotrophic to copiotrophic microbial lifestyles under irrigation and the observation that different microbial taxa were involved in the degradation of cellulose and lignin as determined by a time-series stable-isotope probing incubation experiment with 13C-labeled substrates, degradation rates of these compounds were not affected by different water availabilities. These findings provide new insights into the impact of precipitation changes on the soil microbiome and associated ecosystem functioning in a drought-prone pine forest and will help to improve our understanding of alterations in biogeochemical cycling under a changing climate.

Performance, microbial growth and community interactions of iron-dependent denitrification in freshwaters

Iron-dependent denitrification is a safe and promising technology for nitrogen removal in freshwaters. However, the understanding of microbial physiology and interactions during the process was limited. Denitrifying systems inoculated with freshwater samples were operated with and without iron(II) at a low C/N ratio for 54 days. Iron addition improved nitrogen removal. Batch experiments confirmed that microbially mediated reaction rather than abiotic reaction dominated during the process. Metagenomics recovered genomes of the five most abundant microorganisms, which accounted for over 99% of the community in every triplicate of the iron-based system. Based on codon usage bias, all of them were fast-growing organisms. The total abundance of fast-growing organisms was 38% higher in the system with iron than in the system without iron. Notably, the most abundant organism Diaphorobacter did not have enzymes for asparagine and aspartate biosynthesis, whereas Rhodanobacter could not produce serine and cobalamin. Algoriphagus and Areminomonas lost synthesis enzymes for more amino acids and vitamins. However, they could always obtain these growth-required substances from another microorganism in the community. The two-partner relationship minimized the limitation on microbial reproduction and increased community stability. Our results indicated that iron addition improved nitrogen removal by supplying electron donors, promoting microbial growth, and building up syntrophic interactions among microorganisms with timely communications. The findings provided new insights into the process, with implications for freshwater remediation.

Venom composition and bioactive RF-amide peptide toxins of the saddleback caterpillar, Acharia stimulea (Lepidoptera: Limacodidae)

Limacodidae is a family of lepidopteran insects comprising >1500 species. More than half of these species produce pain-inducing defensive venoms in the larval stage, but little is known about their venom toxins. Recently, we characterised proteinaceous toxins from the Australian limacodid caterpillar Doratifera vulnerans, but it is unknown if the venom of this species is typical of other Limacodidae. Here, we use single animal transcriptomics and venom proteomics to investigate the venom of an iconic limacodid, the North American saddleback caterpillar Acharia stimulea. We identified 65 venom polypeptides, grouped into 31 different families. Neurohormones, knottins, and homologues of the immune signaller Diedel make up the majority of A.stimulea venom, indicating strong similarities to D. vulnerans venom, despite the large geographic separation of these caterpillars. One notable difference is the presence of RF-amide peptide toxins in A. stimulea venom. Synthetic versions of one of these RF-amide toxins potently activated the human neuropeptide FF1 receptor, displayed insecticidal activity when injected into Drosophila melanogaster, and moderately inhibited larval development of the parasitic nematode Haemonchus contortus. This study provides insights into the evolution and activity of venom toxins in Limacodidae, and provides a platform for future structure-function characterisation of A.stimulea peptide toxins.

Genome-wide identification and expression analyses of phenylalanine ammonia-lyase gene family members from tomato (Solanum lycopersicum) reveal their role in root-knot nematode infection

Phenylalanine ammonia-lyase (PAL) is a key enzyme and rate-limiting enzyme of phenylpropanoid metabolism, which is a very important pathway in plants, and the secondary products it produces play an important role in plant growth and development, disease resistance, and stress resistance responses. However, PALs still lack systematic characterization in tomato. Based on a bioinformatics methods, PAL family genes were identified and characterized from tomato. qRT-PCR was used to study the expression of PAL genes in cultivated tomato after root-knot nematode infection. In this study, 14 and 11 PAL genes were identified in cultivated and wild tomatoes, and phylogenetic analysis classified them into three subfamilies, with different subfamilies of PAL proteins evolving in different directions in monocotyledonous and dicotyledonous plants. The extensive presence of stress, growth, hormone, and light response elements in the promoter sequences of SlPAL (Solanum lycopersicum) and SpenPAL (Solanum pennellii) genes suggests that this family has a critical role in abiotic stress. Collinearity indicates that members of the tomato and Arabidopsis PAL genes family are from the same ancestor, and the SlPAL10 gene is directly homologous to monocotyledonous rice and maize, suggesting that the SlPAL10 gene was present before monocotyledonous differentiation. Two co-expressed gene modules containing PAL genes were screened by WGCNA, and the core genes in the network were mined and functionally annotated by calculating the connectivity of genes within the modules. In addition, the expression of some genes changed significantly after root-knot nematode infection, with up-regulation of 4 genes and down-regulation of 3 genes. This result provides a data reference for the study of PAL family gene functions in tomato, and also provides a potential application for the subsequent selection of PAL genes in tomato for root-knot nematode resistance.

Construction of a de novo assembly pipeline using multiple transcriptome data sets from Cypripedium macranthos (Orchidaceae)

The family Orchidaceae comprises the most species of any monocotyledonous family and has interesting characteristics such as seed germination induced by mycorrhizal fungi and flower morphology that co-adapted with pollinators. In orchid species, genomes have been decoded for only a few horticultural species, and there is little genetic information available. Generally, for species lacking sequenced genomes, gene sequences are predicted by de novo assembly of transcriptome data. Here, we devised a de novo assembly pipeline for transcriptome data from the wild orchid Cypripedium (lady slipper orchid) in Japan by mixing multiple data sets and integrating assemblies to create a more complete and less redundant contig set. Among the assemblies generated by combining various assemblers, Trinity and IDBA-Tran yielded good assembly with higher mapping rates and percentages of BLAST hit contigs and complete BUSCO. Using this contig set as a reference, we analyzed differential gene expression between protocorms grown aseptically or with mycorrhizal fungi to detect gene expressions required for mycorrhizal interaction. A pipeline proposed in this study can construct a highly reliable contig set with little redundancy even when multiple transcriptome data are mixed, and can provide a reference that is adaptable to DEG analysis and other downstream analysis in RNA-seq.

Establishment of wastewater-based SARS-CoV-2 monitoring system over two years: Case studies in South Korea

With the global COVID-19 pandemic, wastewater surveillance has received a considerable attention as a method for the early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater treatment plant (WWTP) and sewer systems. For the first time in Korea, this study utilized the wastewater surveillance technique to monitor the COVID-19 outbreak. Sampling efforts were carried out at the WWTPs in the capital city of Korea, Seoul, and Daegu the place where the first severe outbreak was reported. The RNA of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been extracted from the collected wastewater influent and primary sewage sludge samples. The outcomes were contrasted with the COVID-19 cases in the WWTPs served area. Additionally, whole transcriptome sequencing was used to compare the microbial community alterations before and after the COVID-19 outbreak and SARS-CoV-2 variations. The results demonstrated that the changes in SARS-CoV-2 RNA concentrations in the influent and sludge matched the trends of reported COVID-19 cases, especially sludge showed high-resolution data, which is well-matched when fewer COVID-19 cases (0-250) are reported. Interestingly, one month before the clinical report, we found that the SARS-CoV-2 Beta variant (South Africa, B.1.351) in the wastewater. In addition, the Aeromonas bacterial species was dominated (21.2%) among other bacterial species in wastewater after the COVID-19 outbreak, suggesting a potential indirect microbial indicator of the COVID-19 outbreak.

A ubiquitous gammaproteobacterial clade dominates expression of sulfur oxidation genes across the mesopelagic ocean

The deep ocean (>200 m depth) is the largest habitat on Earth. Recent evidence suggests sulfur oxidation could be a major energy source for deep ocean microbes. However, the global relevance and the identity of the major players in sulfur oxidation in the oxygenated deep-water column remain elusive. Here we combined single-cell genomics, community metagenomics, metatranscriptomics and single-cell activity measurements on samples collected beneath the Ross Ice Shelf in Antarctica to characterize a ubiquitous mixotrophic bacterial group (UBA868) that dominates expression of RuBisCO genes and of key sulfur oxidation genes. Further analyses of the gene libraries from the 'Tara Oceans' and 'Malaspina' expeditions confirmed the ubiquitous distribution and global relevance of this enigmatic group in the expression of sulfur oxidation and dissolved inorganic carbon fixation genes across the global mesopelagic ocean. Our study also underscores the unrecognized importance of mixotrophic microbes in the biogeochemical cycles of the deep ocean.

Improved DNA-Versus-Protein Homology Search for Protein Fossils

Protein fossils, i.e., noncoding DNA descended from coding DNA, arise frequently from transposable elements (TEs), decayed genes, and viral integrations. They can reveal, and mislead about, evolutionary history and relationships. They have been detected by comparing DNA to protein sequences, but current methods are not optimized for this task. We describe a powerful DNA-protein homology search method. We use a 64×21 substitution matrix, which is fitted to sequence data, automatically learning the genetic code. We detect subtly homologous regions by considering alternative possible alignments between them, and calculate significance (probability of occurring by chance between random sequences). Our method detects TE protein fossils much more sensitively than blastx, and faster. Of the  ∼ 7 major categories of eukaryotic TE, three were long thought absent in mammals: we find two of them in the human genome, polinton and DIRS/Ngaro. This method increases our power to find ancient fossils, and perhaps to detect non-standard genetic codes. The alternative-alignments and significance paradigm is not specific to DNA-protein comparison, and could benefit homology search generally. This is an extended version of a conference paper (Yao & Frith, 2021).

Computational analysis of the flexibility in the disordered linker region connecting LIM domains in cysteine–glycine-rich protein

One of the key proteins that are present in the Z-disc of cardiac tissues, CSRP3, has been implicated in dilated and hypertrophic cardiomyopathy leading to heart failure. Although multiple cardiomyopathy-related mutations have been reported to reside on the two LIM domains and the disordered regions connecting the domains in this protein, the exact role of the disordered linker region is not clear. The linker harbors a few post-translational modification sites and is expected to be a regulatory site. We have carried out evolutionary studies on 5614 homologs spanning across taxa. We also performed molecular dynamics simulations of full-length CSRP3 to show that the length variations and conformational flexibility of the disordered linker could provide additional levels of functional modulation. Finally, we show that the CSRP3 homologs with widely different lengths of the linker regions could display diversity in their functional specifications. The present study provides a useful perspective to our understanding of the evolution of the disordered region between CSRP3 LIM domains.

An untargeted exometabolomics approach to characterize dissolved organic matter in groundwater of the Samail Ophiolite

The process of serpentinization supports life on Earth and gives rise to the habitability of other worlds in our Solar System. While numerous studies have provided clues to the survival strategies of microbial communities in serpentinizing environments on the modern Earth, characterizing microbial activity in such environments remains challenging due to low biomass and extreme conditions. Here, we used an untargeted metabolomics approach to characterize dissolved organic matter in groundwater in the Samail Ophiolite, the largest and best characterized example of actively serpentinizing uplifted ocean crust and mantle. We found that dissolved organic matter composition is strongly correlated with both fluid type and microbial community composition, and that the fluids that were most influenced by serpentinization contained the greatest number of unique compounds, none of which could be identified using the current metabolite databases. Using metabolomics in conjunction with metagenomic data, we detected numerous products and intermediates of microbial metabolic processes and identified potential biosignatures of microbial activity, including pigments, porphyrins, quinones, fatty acids, and metabolites involved in methanogenesis. Metabolomics techniques like the ones used in this study may be used to further our understanding of life in serpentinizing environments, and aid in the identification of biosignatures that can be used to search for life in serpentinizing systems on other worlds.

Host Immune Responses to Surface S-Layer Proteins (SLPs) of Clostridioides difficile

Clostridioides difficile, a nosocomial pathogen, is an emerging gut pathobiont causing antibiotic-associated diarrhea. C. difficile infection involves gut colonization and disruption of the gut epithelial barrier, leading to the induction of inflammatory/immune responses. The expression of two major exotoxins, TcdA and TcdB is the major cause of C. difficile pathogenicity. Attachment of bacterial abundant cell wall proteins or surface S-layer proteins (SLPs) such as SlpA with host epithelial cells is critical for virulence. In addition to being toxins, these surface components have been shown to be highly immunogenic. Recent studies indicate that C. difficile SLPs play important roles in the adhesion of the bacteria to the intestinal epithelial cells, disruption of tight junctions, and modulation of the immune response of the host cells. These proteins might serve as new targets for vaccines and new therapeutic agents. This review summarizes our current understanding of the immunological role of SLPs in inducing host immunity and their use in the development of vaccines and novel therapeutics to combat C. difficile infection.

DALI shines a light on remote homologs: One hundred discoveries

Structural comparison reveals remote homology that often fails to be detected by sequence comparison. The DALI web server (http://ekhidna2.biocenter.helsinki.fi/dali) is a platform for structural analysis that provides database searches and interactive visualization, including structural alignments annotated with secondary structure, protein families and sequence logos, and 3D structure superimposition supported by color-coded sequence and structure conservation. Here, we are using DALI to mine the AlphaFold Database version 1, which increased the structural coverage of protein families by 20%. We found 100 remote homologous relationships hitherto unreported in the current reference database for protein domains, Pfam 35.0. In particular, we linked 35 domains of unknown function (DUFs) to the previously characterized families, generating a functional hypothesis that can be explored downstream in structural biology studies. Other findings include gene fusions, tandem duplications, and adjustments to domain boundaries. The evidence for homology can be browsed interactively through live examples on DALI's website.

Composition and toxicity of venom produced by araneophagous white-tailed spiders (Lamponidae: Lampona sp.)

Prey-specialised spiders are adapted to capture specific prey items, including dangerous prey. The venoms of specialists are often prey-specific and less complex than those of generalists, but their venom composition has not been studied in detail. Here, we investigated the venom of the prey-specialised white-tailed spiders (Lamponidae: Lampona), which utilise specialised morphological and behavioural adaptations to capture spider prey. We analysed the venom composition using proteo-transcriptomics and taxon-specific toxicity using venom bioassays. Our analysis identified 208 putative toxin sequences, comprising 103 peptides < 10 kDa and 105 proteins > 10 kDa. Most peptides belonged to one of two families characterised by scaffolds containing eight or ten cysteine residues. Toxin-like proteins showed similarity to galectins, leucine-rich repeat proteins, trypsins and neprilysins. The venom of Lampona was shown to be more potent against the preferred spider prey than against alternative cricket prey. In contrast, the venom of a related generalist was similarly potent against both prey types. These data provide insights into the molecular adaptations of venoms produced by prey-specialised spiders.

HELIOS: High-speed sequence alignment in optics

In response to the imperfections of current sequence alignment methods, originated from the inherent serialism within their corresponding electrical systems, a few optical approaches for biological data comparison have been proposed recently. However, due to their low performance, raised from their inefficient coding scheme, this paper presents a novel all-optical high-throughput method for aligning DNA, RNA, and protein sequences, named HELIOS. The HELIOS method employs highly sophisticated operations to locate character matches, single or multiple mutations, and single or multiple indels within various biological sequences. On the other hand, the HELIOS optical architecture exploits high-speed processing and operational parallelism in optics, by adopting wavelength and polarization of optical beams. For evaluation, the functionality and accuracy of the HELIOS method are approved through behavioral and optical simulation studies, while its complexity and performance are estimated through analytical computation. The accuracy evaluations indicate that the HELIOS method achieves a precise pairwise alignment of two sequences, highly similar to those of Smith-Waterman, Needleman-Wunsch, BLAST, MUSCLE, ClustalW, ClustalΩ, T-Coffee, Kalign, and MAFFT. According to our performance evaluations, the HELIOS optical architecture outperforms all alternative electrical and optical algorithms in terms of processing time and memory requirement, relying on its highly sophisticated method and optical architecture. Moreover, the employed compact coding scheme highly escalates the number of input characters, and hence, it offers reduced time and space complexities, compared to the electrical and optical alternatives. It makes the HELIOS method and optical architecture highly applicable for biomedical applications.

learnMSA: learning and aligning large protein families

BACKGROUND

The alignment of large numbers of protein sequences is a challenging task and its importance grows rapidly along with the size of biological datasets. State-of-the-art algorithms have a tendency to produce less accurate alignments with an increasing number of sequences. This is a fundamental problem since many downstream tasks rely on accurate alignments.

RESULTS

We present learnMSA, a novel statistical learning approach of profile hidden Markov models (pHMMs) based on batch gradient descent. Fundamentally different from popular aligners, we fit a custom recurrent neural network architecture for (p)HMMs to potentially millions of sequences with respect to a maximum a posteriori objective and decode an alignment. We rely on automatic differentiation of the log-likelihood, and thus, our approach is different from existing HMM training algorithms like Baum-Welch. Our method does not involve progressive, regressive, or divide-and-conquer heuristics. We use uniform batch sampling to adapt to large datasets in linear time without the requirement of a tree. When tested on ultra-large protein families with up to 3.5 million sequences, learnMSA is both more accurate and faster than state-of-the-art tools. On the established benchmarks HomFam and BaliFam with smaller sequence sets, it matches state-of-the-art performance. All experiments were done on a standard workstation with a GPU.

CONCLUSIONS

Our results show that learnMSA does not share the counterintuitive drawback of many popular heuristic aligners, which can substantially lose accuracy when many additional homologs are input. LearnMSA is a future-proof framework for large alignments with many opportunities for further improvements.

Influences of carbon sources on N2O production during denitrification in freshwaters: Activity, isotopes and functional microbes

Denitrification is one of the major sources of N₂O in freshwaters. Diverse forms of organic compounds act as the electron donors for microbial denitrification. However, the influences of carbon sources on N₂O production, N₂O reduction, isotope fractionation and functional microbes during denitrification were largely unknown. In this study, five forms of carbon sources (i.e. acetate, citrate, glucose, cellobiose and leucine) were used to enrich denitrifiers in freshwater sediments. N₂O conversion in the enrichments was investigated by a combination of inhibition technique, natural stable isotope method and metagenomics. Acetylene was effective in inhibiting N₂O reduction without influencing the isotopic characteristics during N₂O production. Glucose led to the least N₂O production and reduction, in accordance with the lowest abundance of both NO and N₂O reductases in this enrichment. δ¹⁸O and site preference value (SP, =δ¹⁵N^α-δ¹⁵N^β) of N₂O were sensitive to discriminate the five carbon sources, except when comparing acetate and leucine. Isotopic values of N₂O were not significantly different in these two enrichments due to the similarity of NO reductases - Pseudomonas-type cNorB. Specifically, the enrichment with cellobiose produced N₂O with the lowest δ¹⁸O values (39.4‰±1.1‰), due to Alicycliphilus with both cNorB and qNorB. The enrichment with glucose led to the highest SP values (8.9‰±8.6‰), caused by Thiobacillus-type cNorB. Our results demonstrated the link between carbon sources, N₂O production and reduction, isotopic signatures, microbial populations and enzymes during denitrification in freshwaters.

Best genome sequencing strategies for annotation of complex immune gene families in wildlife

BACKGROUND

The biodiversity crisis and increasing impact of wildlife disease on animal and human health provides impetus for studying immune genes in wildlife. Despite the recent boom in genomes for wildlife species, immune genes are poorly annotated in nonmodel species owing to their high level of polymorphism and complex genomic organisation. Our research over the past decade and a half on Tasmanian devils and koalas highlights the importance of genomics and accurate immune annotations to investigate disease in wildlife. Given this, we have increasingly been asked the minimum levels of genome quality required to effectively annotate immune genes in order to study immunogenetic diversity. Here we set out to answer this question by manually annotating immune genes in 5 marsupial genomes and 1 monotreme genome to determine the impact of sequencing data type, assembly quality, and automated annotation on accurate immune annotation.

RESULTS

Genome quality is directly linked to our ability to annotate complex immune gene families, with long reads and scaffolding technologies required to reassemble immune gene clusters and elucidate evolution, organisation, and true gene content of the immune repertoire. Draft-quality genomes generated from short reads with HiC or 10× Chromium linked reads were unable to achieve this. Despite mammalian BUSCOv5 scores of up to 94.1% amongst the 6 genomes, automated annotation pipelines incorrectly annotated up to 59% of manually annotated immune genes regardless of assembly quality or method of automated annotation.

CONCLUSIONS

Our results demonstrate that long reads and scaffolding technologies, alongside manual annotation, are required to accurately study the immune gene repertoire of wildlife species.

Comparative Genomic Analysis Uncovers the Chloroplast Genome Variation and Phylogenetic Relationships of Camellia Species

Camellia is the largest genus in the family Theaceae. Due to phenotypic diversity, frequent hybridization, and polyploidization, an understanding of the phylogenetic relationships between Camellia species remains challenging. Comparative chloroplast (cp) genomics provides an informative resource for phylogenetic analyses of Camellia. In this study, 12 chloroplast genome sequences from nine Camellia species were determined using Illumina sequencing technology via de novo assembly. The cp genome sizes ranged from 156,545 to 157,021 bp and were organized into quadripartite regions with the typical angiosperm cp genomes. Each genome harbored 87 protein-coding, 37 transfer RNA, and 8 ribosomal RNA genes in the same order and orientation. Differences in long and short sequence repeats, SNPs, and InDels were detected across the 12 cp genomes. Combining with the complete cp sequences of seven other species in the genus Camellia, a total of nine intergenic sequence divergent hotspots and 14 protein-coding genes with high sequence polymorphism were identified. These hotspots, especially the InDel (~400 bp) located in atpH-atpI region, had sufficient potential to be used as barcode markers for further phylogenetic analysis and species identification. Principal component and phylogenetic analysis suggested that regional constraints, rather than functional constraints, strongly affected the sequence evolution of the cp genomes in this study. These cp genomes could facilitate the development of new molecular markers, accurate species identification, and investigations of the phylogenomic relationships of the genus Camellia.

Phylogenomic analysis of phenylalanine ammonia-lyase (PAL) multigene family and their differential expression analysis in wheat (Triticum aestivum L.) suggested their roles during different stress responses

Phenylalanine ammonia-lyase (PAL) is a key enzyme in the phenylalanine metabolism pathway and plays an important role in plant growth and stress response. It has been widely reported in plants, but less studied in wheat. In this study, 54 PAL genes were identified in the wheat genome. Based on phylogenetic analysis, the 54 TaPAL genes were divided into four groups (I, II, III, and IV). Then, the expression levels of TaPALs under biotic stresses were analyzed by transcriptome data analysis. The results showed that 31 genes were up-regulated and one gene was down-regulated after inoculation with Fusarium graminearum, 11 genes were up-regulated and 14 genes were down-regulated after inoculation with Puccinia striiformis, and 32 up-regulated and three down-regulated genes after inoculation with powdery mildew. The expression patterns of the five TaPALs were further analyzed by qRT-PCR. After inoculation with F. graminearum, the expression levels of five TaPALs were up-regulated. However, the TaPALs (expect TaPAL49) were down-regulated when inoculated with P. striiformis. Finally, the functions of TaPAL32 and TaPAL42 in resistance of wheat to the stripe rust were further analyzed by virus induced gene silencing (VIGS) assays. The results showed that the disease severity of TaPAL32 and TaPAL42 silenced plants was higher than that of control plants at 14 days after inoculation. It indicated that these two genes played a positive role in wheat stripe rust resistance. This study provided new evidence support for the functional study of PAL genes in wheat, and provided potential application value for the breeding of wheat resistant varieties.

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Fungal communities represent the majority of root-specific transcripts in the transcriptomes of Agave plants grown in semiarid regions

Agave plants present drought resistance mechanisms, commercial applications, and potential for bioenergy production. Currently, Agave species are used to produce alcoholic beverages and sisal fibers in semi-arid regions, mainly in Mexico and Brazil. Because of their high productivities, low lignin content, and high shoot-to-root ratio, agaves show potential as biomass feedstock to bioenergy production in marginal areas. Plants host many microorganisms and understanding their metabolism can inform biotechnological purposes. Here, we identify and characterize fungal transcripts found in three fiber-producing agave cultivars (Agave fourcroydes, A. sisalana, and hybrid 11648). We used leaf, stem, and root samples collected from the agave germplasm bank located in the state of Paraiba, in the Brazilian semiarid region, which has faced irregular precipitation periods. We used data from a de novo assembled transcriptome assembly (all tissues together). Regardless of the cultivar, around 10% of the transcripts mapped to fungi. Surprisingly, most root-specific transcripts were fungal (58%); of these around 64% were identified as Ascomycota and 28% as Basidiomycota in the three communities. Transcripts that code for heat shock proteins (HSPs) and enzymes involved in transport across the membrane in Ascomycota and Basidiomycota, abounded in libraries generated from the three cultivars. Indeed, among the most expressed transcripts, many were annotated as HSPs, which appear involved in abiotic stress resistance. Most HSPs expressed by Ascomycota are small HSPs, highly related to dealing with temperature stresses. Also, some KEGG pathways suggest interaction with the roots, related to transport to outside the cell, such as exosome (present in the three Ascomycota communities) and membrane trafficking, which were further investigated. We also found chitinases among secreted CAZymes, that can be related to pathogen control. We anticipate that our results can provide a starting point to the study of the potential uses of agaves' fungi as biotechnological tools.

Comparative Genomics Reveals Evolutionary Traits, Mating Strategies, and Pathogenicity-Related Genes Variation of Botryosphaeriaceae

Botryosphaeriaceae, as a major family of the largest class of kingdom fungi Dothideomycetes, encompasses phytopathogens, saprobes, and endophytes. Many members of this family are opportunistic phytopathogens with a wide host range and worldwide geographical distribution, and can infect many economically important plants, including food crops and raw material plants for biofuel production. To date, however, little is known about the family evolutionary characterization, mating strategies, and pathogenicity-related genes variation from a comparative genome perspective. Here, we conducted a large-scale whole-genome comparison of 271 Dothideomycetes, including 19 species in Botryosphaeriaceae. The comparative genome analysis provided a clear classification of Botryosphaeriaceae in Dothideomycetes and indicated that the evolution of lifestyle within Dothideomycetes underwent four major transitions from non-phytopathogenic to phytopathogenic. Mating strategies analysis demonstrated that at least 3 transitions were found within Botryosphaeriaceae from heterothallism to homothallism. Additionally, pathogenicity-related genes contents in different genera varied greatly, indicative of genus-lineage expansion within Botryosphaeriaceae. These findings shed new light on evolutionary traits, mating strategies and pathogenicity-related genes variation of Botryosphaeriaceae.

Denitrification and dissimilatory nitrate reduction to ammonia in long-term lake sediment microcosms with iron(II)

Nitrate is an abundant pollutant in aquatic environments. Competition between the nitrate reduction processes, denitrification, which converts nitrate into nitrogen gas, and dissimilatory nitrate reduction to ammonia (DNRA), which converts nitrate into ammonia, decides whether an ecosystem removes or retains nitrogen. The presence of iron was previously reported to stimulate DNRA while sometimes inhibiting denitrification in in-situ studies, but long-term effect of iron(II) inputs on the competition is unknown. Here we inoculated long-term microcosms with sediments from two freshwater lakes. During 540 days of incubations, the microcosms with nitrate and Fe(II) additions of both lakes were able to sustain high nitrate reduction rates. Lepidocrocite was produced as a product of iron oxidation. We found both denitrification and DNRA were stimulated by nitrate and iron in the absence of external organic carbon addition. Phylogenetic analysis of denitrification genes, nirK and nirS, and DNRA genes, nirB and nrfA, was performed with metagenomic sequencing results. Enrichment was shown for reported Fe(II)-dependent nitrate reducers associated with nirS and nirB. Most of these bacteria are affiliated with Betaproteobacteria. From 16S rRNA gene analysis, Betaproteobacteria was enriched as well. In parallel, heterotrophic denitrifiers and methanotrophic DNRA archaea increased in abundance. Our results suggested heterotrophic and Fe(II)-dependent nitrate reducers both contributed to denitrification and DNRA in long-term microcosm incubations provided with iron.

AnnoSINE: a short interspersed nuclear elements annotation tool for plant genomes

Short interspersed nuclear elements (SINEs) are a widespread type of small transposable element (TE). With increasing evidence for their impact on gene function and genome evolution in plants, accurate genome-scale SINE annotation becomes a fundamental step for studying the regulatory roles of SINEs and their relationship with other components in the genomes. Despite the overall promising progress made in TE annotation, SINE annotation remains a major challenge. Unlike some other TEs, SINEs are short and heterogeneous, and they usually lack well-conserved sequence or structural features. Thus, current SINE annotation tools have either low sensitivity or high false discovery rates. Given the demand and challenges, we aimed to provide a more accurate and efficient SINE annotation tool for plant genomes. The pipeline starts with maximizing the pool of SINE candidates via profile hidden Markov model-based homology search and de novo SINE search using structural features. Then, it excludes the false positives by integrating all known features of SINEs and the features of other types of TEs that can often be misannotated as SINEs. As a result, the pipeline substantially improves the tradeoff between sensitivity and accuracy, with both values close to or over 90%. We tested our tool in Arabidopsis thaliana and rice (Oryza sativa), and the results show that our tool competes favorably against existing SINE annotation tools. The simplicity and effectiveness of this tool would potentially be useful for generating more accurate SINE annotations for other plant species. The pipeline is freely available at https://github.com/yangli557/AnnoSINE.

The complete chloroplast genomes of three Hamamelidaceae species: Comparative and phylogenetic analyses

Hamamelidaceae is an important group that represents the origin and early evolution of angiosperms. Its plants have many uses, such as timber, medical, spice, and ornamental uses. In this study, the complete chloroplast genomes of Loropetalum chinense (R. Br.) Oliver, Corylopsis glandulifera Hemsl., and Corylopsis velutina Hand.-Mazz. were sequenced using the Illumina NovaSeq 6000 platform. The sizes of the three chloroplast genomes were 159,402 bp (C. glandulifera), 159,414 bp (C. velutina), and 159,444 bp (L. chinense), respectively. These chloroplast genomes contained typical quadripartite structures with a pair of inverted repeat (IR) regions (26,283, 26,283, and 26,257 bp), a large single-copy (LSC) region (88,134, 88,146, and 88,160 bp), and a small single-copy (SSC) region (18,702, 18,702, and 18,770 bp). The chloroplast genomes encoded 132-133 genes, including 85-87 protein-coding genes, 37-38 tRNA genes, and 8 rRNA genes. The coding regions were composed of 26,797, 26,574, and 26,415 codons, respectively, most of which ended in A/U. A total of 37-43 long repeats and 175-178 simple sequence repeats (SSRs) were identified, and the SSRs contained a higher number of A + T than G + C bases. The genome comparison showed that the IR regions were more conserved than the LSC or SSC regions, while the noncoding regions contained higher variability than the gene coding regions. Phylogenetic analyses revealed that species in the same genus tended to cluster together. Chunia Hung T. Chang, Mytilaria Lecomte, and Disanthus Maxim. may have diverged early and Corylopsis Siebold & Zucc. was closely related to Loropetalum R. Br. This study provides valuable information for further species identification, evolution, and phylogenetic studies of Hamamelidaceae plants.

Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species

The methanogenic degradation of oil hydrocarbons can proceed through syntrophic partnerships of hydrocarbon-degrading bacteria and methanogenic archaea^1-3. However, recent culture-independent studies have suggested that the archaeon 'Candidatus Methanoliparum' alone can combine the degradation of long-chain alkanes with methanogenesis^4,5. Here we cultured Ca. Methanoliparum from a subsurface oil reservoir. Molecular analyses revealed that Ca. Methanoliparum contains and overexpresses genes encoding alkyl-coenzyme M reductases and methyl-coenzyme M reductases, the marker genes for archaeal multicarbon alkane and methane metabolism. Incubation experiments with different substrates and mass spectrometric detection of coenzyme-M-bound intermediates confirm that Ca. Methanoliparum thrives not only on a variety of long-chain alkanes, but also on n-alkylcyclohexanes and n-alkylbenzenes with long n-alkyl (C_≥13) moieties. By contrast, short-chain alkanes (such as ethane to octane) or aromatics with short alkyl chains (C_≤12) were not consumed. The wide distribution of Ca. Methanoliparum^4-6 in oil-rich environments indicates that this alkylotrophic methanogen may have a crucial role in the transformation of hydrocarbons into methane.

Taxon-Specific Shifts in Bacterial and Archaeal Transcription of Dissolved Organic Matter Cycling Genes in a Stratified Fjord

A considerable fraction of organic matter derived from photosynthesis in the euphotic zone settles into the ocean’s interior and, as it progresses, is degraded by diverse microbial consortia that utilize a suite of extracellular enzymes and membrane transporters. Still, the molecular details that regulate carbon cycling across depths remain little explored. As stratification in fjords has made them attractive models to explore patterns in biological oceanography, we here analyzed bacterial and archaeal transcription in samples from five depth layers in the Gullmar Fjord, Sweden. Transcriptional variation over depth correlated with gradients in chlorophyll a and nutrient concentrations. Differences in transcription between sampling dates (summer and early autumn) were strongly correlated with ammonium concentrations, which potentially was linked with a stronger influence of (micro-)zooplankton grazing in summer. Transcriptional investment in carbohydrate-active enzymes (CAZymes) decreased with depth and shifted toward peptidases, partly a result of elevated CAZyme transcription by Flavobacteriales, Cellvibrionales, and Synechococcales at 2 to 25 m and a dominance of peptidase transcription by Alteromonadales and Rhodobacterales from 50 m down. In particular, CAZymes for chitin, laminarin, and glycogen were important. High levels of transcription of ammonium transporter genes by Thaumarchaeota at depth (up to 18% of total transcription), along with the genes for ammonia oxidation and CO₂ fixation, indicated that chemolithoautotrophy contributed to the carbon flux in the fjord. The taxon-specific expression of functional genes for processing of the marine pool of dissolved organic matter and inorganic nutrients across depths emphasizes the importance of different microbial foraging mechanisms over spatiotemporal scales for shaping biogeochemical cycles.

IMPORTANCE It is generally recognized that stratification in the ocean strongly influences both the community composition and the distribution of ecological functions of microbial communities, which in turn are expected to shape the biogeochemical cycling of essential elements over depth. Here, we used metatranscriptomics analysis to infer molecular detail on the distribution of gene systems central to the utilization of organic matter in a stratified marine system. We thereby uncovered that pronounced shifts in the transcription of genes encoding CAZymes, peptidases, and membrane transporters occurred over depth among key prokaryotic orders. This implies that sequential utilization and transformation of organic matter through the water column is a key feature that ultimately influences the efficiency of the biological carbon pump.

Increase of N2O production during nitrate reduction after long-term sulfide addition in lake sediment microcosms

Microbial denitrification is a main source of nitrous oxide (N₂O) emissions which have strong greenhouse effect and destroy stratospheric ozone. Though the importance of sulfide driven chemoautotrophic denitrification has been recognized, its contribution to N₂O emissions in nature remains elusive. We built up long-term sulfide-added microcosms with sediments from two freshwater lakes. Chemistry analysis confirmed sulfide could drive nitrate respiration in long term. N₂O accumulated to over 1.5% of nitrate load in both microcosms after long-term sulfide addition, which was up to 12.9 times higher than N₂O accumulation without sulfide addition. Metagenomes were extracted and sequenced during microcosm incubations. 16 S rRNA genes of Thiobacillus and Defluviimonas were gradually enriched. The nitric oxide reductase with c-type cytochromes as electron donors (cNorB) increased in abundance, while the nitric oxide reductase receiving electrons from quinols (qNorB) decreased in abundance. cnorB genes similar to Thiobacillus were enriched in both microcosms. In parallel, enrichment was observed for enzymes involved in sulfur oxidation, which supplied electrons to nitrate respiration, and enzymes involved in Calvin Cycle, which sustained autotrophic cell growth, implying the coupling relationship between carbon, nitrogen and sulfur cycling processes. Our results suggested sulfur pollution considerably increased N₂O emissions in natural environments.

Venom composition of the endoparasitoid wasp Cotesia flavipes (Hymenoptera: Braconidae) and functional characterization of a major venom peptide

Endoparasitoid wasps use complex biochemical arsenals to suppress the normal humoral and cellular immune responses of their hosts in order to transform them into a suitable environment for development of their eggs and larvae. Venom injected during oviposition is a key component of this arsenal, but the functions of individual venom toxins are still poorly understood. Furthermore, there has been little investigation of the potential biotechnological use of these venom toxins, for example for control of agricultural pests. The endoparasitoid Cotesia flavipes (Hymenoptera: Braconidae) is a biocontrol agent reared in biofactories and released extensively in Brazil to control the sugarcane borer Diatraea saccharalis (Lepidoptera: Crambidae). The objectives of this work were to reveal venom components produced by C. flavipes and explore the function of a major venom peptide, Cf4. Using a combined proteomic/transcriptomic approach, we identified 38 putative venom toxins including both linear and disulfide-rich peptides, hydrolases, protease inhibitors, apolipophorins, lipid-binding proteins, and proteins of the odorant binding families. Because of its high abundance in the venom, we selected Cf4, a 33-residue peptide with three disulfide bonds, for synthesis and further characterization. We found that synthetic Cf4 reduced the capacity of D. saccharalis hemocytes to encapsulate foreign bodies without any effect on phenoloxidase activity, consistent with a role in disruption of the cellular host immune response. Feeding leaves coated with Cf4 to neonate D. saccharalis resulted in increased mortality and significantly reduced feeding compared to caterpillars fed untreated leaves, indicating that Cf4 is a potential candidate for insect pest control through ingestion. This study adds to our knowledge of endoparasitoid wasp venoms composition, host regulation mechanisms and their biotechnological potential for pest management.

tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes

tRNAscan-SE has been widely used for transfer RNA (tRNA) gene prediction for over twenty years, developed just as the first genomes were decoded. With the massive increase in quantity and phylogenetic diversity of genomes, the accurate detection and functional prediction of tRNAs has become more challenging. Utilizing a vastly larger training set, we created nearly one hundred specialized isotype- and clade-specific models, greatly improving tRNAscan-SE’s ability to identify and classify both typical and atypical tRNAs. We employ a new comparative multi-model strategy where predicted tRNAs are scored against a full set of isotype-specific covariance models, allowing functional prediction based on both the anticodon and the highest-scoring isotype model. Comparative model scoring has also enhanced the program's ability to detect tRNA-derived SINEs and other likely pseudogenes. For the first time, tRNAscan-SE also includes fast and highly accurate detection of mitochondrial tRNAs using newly developed models. Overall, tRNA detection sensitivity and specificity is improved for all isotypes, particularly those utilizing specialized models for selenocysteine and the three subtypes of tRNA genes encoding a CAU anticodon. These enhancements will provide researchers with more accurate and detailed tRNA annotation for a wider variety of tRNAs, and may direct attention to tRNAs with novel traits.

Comprehensive analysis of formin gene family highlights candidate genes related to pollen cytoskeleton and male fertility in wheat (Triticum aestivum L.)

Background

Formin, a highly conserved multi-domain protein, interacts with microfilaments and microtubules. Although specifically expressed formin genes in anthers are potentially significant in research on male sterility and hybrid wheat breeding, similar reports in wheat, especially in thermo-sensitive genic male sterile (TGMS) wheat, remain elusive.

Results

Herein, we systematically characterized the formin genes in TGMS wheat line BS366 named TaFormins (TaFHs) and predicted their functions in inducing stress response. In total, 25 TaFH genes were uncovered, majorly localized in 2A, 2B, and 2D chromosomes. According to the neighbor-joining (NJ) method, all TaFH proteins from wheat and other plants clustered in 6 sub-groups (A-F). The modeled 3D structures of TaFH1-A/B, TaFH2-A/B, TaFH3-A/B and TaFH3-B/D were validated. And different numbers of stress and hormone-responsive regulatory elements in their 1500 base pair promoter regions were contained in the TaFH genes copies. TaFHs had specific temporal and spatial expression characteristics, whereby TaFH1, TaFH4, and TaFH5 were expressed highly in the stamen of BS366. Besides, the accumulation of TaFHs was remarkably lower in a low-temperature sterile condition (Nanyang) than fertile condition (Beijing), particularly at the early stamen development stage. The pollen cytoskeleton of BS366 was abnormal in the three stages under sterile and fertile environments. Furthermore, under different stress levels, TaFHs expression could be induced by drought, salt, abscisic acid (ABA), salicylic acid (SA), methyl jasmonate (MeJA), indole-3-acetic acid (IAA), polyethylene glycol (PEG), and low temperature. Some miRNAs, including miR167, miR1120, and miR172, interacts with TaFH genes; thus, we constructed an interaction network between microRNAs, TaFHs, phytohormone responses, and distribution of cytoskeleton to reveal the regulatory association between upstream genes of TaFH family members and sterile.

Conclusions

Collectively, this comprehensive analysis provides novel insights into TaFHs and miRNA resources for wheat breeding. These findings are, therefore, valuable in understanding the mechanism of TGMS fertility conversion in wheat.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07878-7.