Improved Strength Recovery and Reduced Fatigue with Suppressed Plasma Myostatin Following Supplementation of a Vicia faba Hydrolysate, in a Healthy Male Population

Delayed onset muscle soreness (DOMS) due to intense physical exertion can negatively impact contractility and performance. Previously, NPN_1 (PeptiStrong™), a Vicia faba hydrolysate derived from a protein concentrate discovered through artificial intelligence (AI), was preclinically shown to help maintain muscle health, indicating the potential to mediate the effect of DOMS and alter molecular markers of muscle damage to improve recovery and performance. A randomised double-blind placebo-controlled trial was conducted on 30 healthy male (30-45 years old) volunteers (NCT05159375). Following initial strength testing on day 0, subjects were administered either placebo or NPN_1 (2.4 g/day). On day 14, DOMS was induced using resistance exercise. Strength recovery and fatigue were measured after 48 and 72 h. Biomarker analysis was performed on blood samples collected prior to DOMS induction and 0, 2, 48 and 72 h post-DOMS induction. NPN_1 supplementation significantly improved strength recovery compared to placebo over the 72 h period post-resistance exercise (p = 0.027), measured by peak torque per bodyweight, but not at individual timepoints. Muscle fatigue was significantly reduced over the same 72 h period (p = 0.041), as was myostatin expression (p = 0.006). A concomitant increase in other acute markers regulating muscle protein synthesis, regeneration and myoblast differentiation was also observed. NPN_1 significantly improves strength recovery and restoration, reduces fatigue and positively modulates alterations in markers related to muscle homeostasis.

Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides

Scientific research consistently demonstrates that diseases may be delayed, treated, or even prevented and, thereby, health may be maintained with health-promoting functional food ingredients (FFIs). Consumers are increasingly demanding sound information about food, nutrition, nutrients, and their associated health benefits. Consequently, a nutrition industry is being formed around natural foods and FFIs, the economic growth of which is increasingly driven by consumer decisions. Information technology, in particular artificial intelligence (AI), is primed to vastly expand the pool of characterised and annotated FFIs available to consumers, by systematically discovering and characterising natural, efficacious, and safe bioactive ingredients (bioactives) that address specific health needs. However, FFI-producing companies are lagging in adopting AI technology for their ingredient development pipelines for several reasons, resulting in a lack of efficient means for large-scale and high-throughput molecular and functional ingredient characterisation. The arrival of the AI-led technological revolution allows for the comprehensive characterisation and understanding of the universe of FFI molecules, enabling the mining of the food and natural product space in an unprecedented manner. In turn, this expansion of bioactives dramatically increases the repertoire of FFIs available to the consumer, ultimately resulting in bioactives being specifically developed to target unmet health needs.

Preservatives from food-For food: Pea protein hydrolysate as a novel bio-preservative against Escherichia coli O157:H7 on a lettuce leaf

Fresh-cut fruits and vegetables are becoming particularly popular as healthy fast-food options; however, they present challenges such as accelerated rates of decay and increased risk for contamination when compared to whole produce. Given that food safety must remain paramount for producers and manufacturers, research into novel, natural food preservation solutions which can help to ensure food safety and protect against spoilage is on the rise. In this work, we investigated the potential of using a novel protein hydrolysate, produced by the enzymatic hydrolysis of Pisum sativum (PSH), as a novel bio-preservative and its abilities to reduce populations of Escherichia coli O157:H7 after inoculation on a lettuce leaf. While unhydrolyzed P. sativum proteins show no antimicrobial activity, once digested, and purified, the enzymatically released peptides induced in vitro bactericidal effects on the foodborne pathogen at 8 mg/ml. When applied on an infected lettuce leaf, the PSH significantly reduced the number of bacteria recovered after 2 hr of treatment. PSH may be preferred over other preservation strategies based on its natural, inexpensive, sustainable source, environmentally friendly process, nontoxic nature, good batch to batch consistency, and ability to significantly reduce counts of E. coli both in vitro and in a lettuce leaf.

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.

Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient

Characterising key components within functional ingredients as well as assessing efficacy and bioavailability is an important step in validating nutritional interventions. Machine learning can assess large and complex data sets, such as proteomic data from plants sources, and so offers a prime opportunity to predict key bioactive components within a larger matrix. Using machine learning, we identified two potentially bioactive peptides within a Vicia faba derived hydrolysate, NPN_1, an ingredient which was previously identified for preventing muscle loss in a murine disuse model. We investigated the predicted efficacy of these peptides in vitro and observed that HLPSYSPSPQ and TIKIPAGT were capable of increasing protein synthesis and reducing TNF-α secretion, respectively. Following confirmation of efficacy, we assessed bioavailability and stability of these predicted peptides and found that as part of NPN_1, both HLPSYSPSPQ and TIKIPAGT survived upper gut digestion, were transported across the intestinal barrier and exhibited notable stability in human plasma. This work is a first step in utilising machine learning to untangle the complex nature of functional ingredients to predict active components, followed by subsequent assessment of their efficacy, bioavailability and human plasma stability in an effort to assist in the characterisation of nutritional interventions.

Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides

While there have been significant advances in drug discovery for diabetes mellitus over the past couple of decades, there is an opportunity and need for improved therapies. While type 2 diabetic patients better manage their illness, many of the therapeutics in this area are peptide hormones with lengthy sequences and a molecular structure that makes them challenging and expensive to produce. Using machine learning, we present novel anti-diabetic peptides which are less than 16 amino acids in length, distinct from human signalling peptides. We validate the capacity of these peptides to stimulate glucose uptake and Glucose transporter type 4 (GLUT4) translocation in vitro. In obese insulin-resistant mice, predicted peptides significantly lower plasma glucose, reduce glycated haemoglobin and even improve hepatic steatosis when compared to treatments currently in use in a clinical setting. These unoptimised, linear peptides represent promising candidates for blood glucose regulation which require further evaluation. Further, this indicates that perhaps we have overlooked the class of natural short linear peptides, which usually come with an excellent safety profile, as therapeutic modalities.

Human milk and infant formulae: Peptide differences and the opportunity to address the functional gap

Bovine-derived formula milk (FM) is a common substitute to human milk (HM), but lacks key functional benefits associated with HM. Accordingly, there have been significant efforts to humanise FM. Recent research has demonstrated that HM-derived peptides convey an array of beneficial bioactivities. Given that peptides serve as important signalling molecules offering high specificity and potency, they represent a prime opportunity to humanise FM. To further understand how HM-derived peptides contribute to infant health, we used peptidomics and bioinformatics to compare the peptide profile of HM to commercially available FM. We found clear and substantial differences between HM and FM in terms of peptide physicochemical properties, protein coverage and abundance. We additionally identified 618 peptides specific to HM that represent an important untapped source to be explored for novel bioactivities. While further study is required, our findings highlight the potential of a peptide-based approach to address the functional gap in FM.

An Artificial Intelligence Characterised Functional Ingredient, Derived from Rice, Inhibits TNF-α and Significantly Improves Physical Strength in an Inflammaging Population

Food-derived bioactive peptides offer great potential for the treatment and maintenance of various health conditions, including chronic inflammation. Using in vitro testing in human macrophages, a rice derived functional ingredient natural peptide network (NPN) significantly reduced Tumour Necrosis Factor (TNF)-α secretion in response to lipopolysaccharides (LPS). Using artificial intelligence (AI) to characterize rice NPNs lead to the identification of seven potentially active peptides, the presence of which was confirmed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Characterization of this network revealed the constituent peptides displayed anti-inflammatory properties as predicted in vitro. The rice NPN was then tested in an elderly "inflammaging" population with a view to subjectively assess symptoms of digestive discomfort through a questionnaire. While the primary subjective endpoint was not achieved, analysis of objectively measured physiological and physical secondary readouts showed clear significant benefits on the ability to carry out physical challenges such as a chair stand test that correlated with a decrease in blood circulating TNF-α. Importantly, the changes observed were without additional exercise or specific dietary alterations. Further health benefits were reported such as significant improvement in glucose control, a decrease in serum LDL concentration, and an increase in HDL concentration; however, this was compliance dependent. Here we provide in vitro and human efficacy data for a safe immunomodulatory functional ingredient characterized by AI.

The anti-ageing effects of a natural peptide discovered by artificial intelligence

OBJECTIVE

As skin ages, impaired extracellular matrix (ECM) protein synthesis and increased action of degradative enzymes manifest as atrophy, wrinkling and laxity. There is mounting evidence for the functional role of exogenous peptides across many areas, including in offsetting the effects of cutaneous ageing. Here, using an artificial intelligence (AI) approach, we identified peptide RTE62G (pep_RTE62G), a naturally occurring, unmodified peptide with ECM stimulatory properties. The AI-predicted anti-ageing properties of pep_RTE62G were then validated through in vitro, ex vivo and proof of concept clinical testing.

METHODS

A deep learning approach was applied to unlock pep_RTE62G from a plant source, Pisum sativum (pea). Cell culture assays of human dermal fibroblasts (HDFs) and keratinocytes (HaCaTs) were subsequently used to evaluate the in vitro effect of pep_RTE62G. Distinct activities such as cell proliferation and ECM protein production properties were determined by ELISA assays. Cell migration was assessed using a wound healing assay, while ECM protein synthesis and gene expression were analysed, respectively, by immunofluorescence microscopy and PCR. Immunohistochemistry of human skin explants was employed to further investigate the induction of ECM proteins by pep_RTE62G ex vivo. Finally, the clinical effect of pep_RTE626 was evaluated in a proof of concept 28-day pilot study.

RESULTS

In vitro testing confirmed that pep_RTE62G is an effective multi-functional anti-ageing ingredient. In HaCaTs, pep_RTE62G treatment significantly increases both cellular proliferation and migration. Similarly, in HDFs, pep_RTE62G consistently induced the neosynthesis of ECM protein elastin and collagen, effects that are upheld in human skin explants. Lastly, in our proof of concept clinical study, application of pep_RTE626 over 28 days demonstrated anti-wrinkle and collagen stimulatory potential.

CONCLUSION

pep_RTE62G represents a natural, unmodified peptide with AI-predicted and experimentally validated anti-ageing properties. Our results affirm the utility of AI in the discovery of novel, functional topical ingredients.

Unlocking NuriPep 1653 From Common Pea Protein: A Potent Antimicrobial Peptide to Tackle a Pan-Drug Resistant Acinetobacter baumannii

While the antibiotic era has come and gone, antimicrobial peptides (AMPs) hold promise as novel therapies to treat multidrug resistant (MDR) pathogens in an age where the threat of multidrug resistance escalates worldwide. Here, we report the bactericidal properties of NuriPep 1653, a novel 22 mer and non-modified peptide. NuriPep 1653 was identified within the sequence of the non-antimicrobial P54 protein, which is involved in nutrient reservoir activity in Pisum sativum. Total bacterial clearance of Acinetobacter baumannii cells (1 × 10⁸ cells/mL) was observed using only 4 × MIC (48 μg/mL) of NuriPep 1653 after just 20 min of treatment. We uncovered a synergistic interaction between NuriPep 1653 and another antimicrobial peptide, colistin. The MIC of NuriPep 1653 and colistin dropped from 12 and 8 μg/mL to 2 and 1 μg/mL, respectively, when they were combined. NuriPep 1653 exhibits no cytotoxicity in different human cell lines and has a low propensity to induce bacterial resistance in a colistin resistant clinical isolate of A. baumannii. The existence of these peptides embedded in proteins unearths potentially new classes of antimicrobials with activity against clinically relevant pathogens. Our findings push the boundaries of traditional peptide discovery and represent a leading edge for natural bioactive compounds which may have a common existence in nature but remain unexposed.

Following the digestion of milk proteins from mother to baby

Little is known about the digestive process in infants. In particular, the chronological activity of enzymes across the course of digestion in the infant remains largely unknown. To create a temporal picture of how milk proteins are digested, enzyme activity was compared between intact human milk samples from three mothers and the gastric samples from each of their 4-12 day postpartum infants, 2 h after breast milk ingestion. The activities of 7 distinct enzymes are predicted in the infant stomach based on their observed cleavage pattern in peptidomics data. We found that the same patterns of cleavage were evident in both intact human milk and gastric milk samples, demonstrating that the enzyme activities that begin in milk persist in the infant stomach. However, the extent of enzyme activity is found to vary greatly between the intact milk and gastric samples. Overall, we observe that milk-specific proteins are cleaved at higher levels in the stomach compared to human milk. Notably, the enzymes we predict here only explain 78% of the cleavages uniquely observed in the gastric samples, highlighting that further investigation of the specific enzyme activities associated with digestion in infants is warranted.

Predicting the important enzymes in human breast milk digestion

Human milk is known to contain several proteases, but little is known about whether these enzymes are active, which proteins they cleave, and their relative contribution to milk protein digestion in vivo. This study analyzed the mass spectrometry-identified protein fragments found in pooled human milk by comparing their cleavage sites with the enzyme specificity patterns of an array of enzymes. The results indicate that several enzymes are actively taking part in the digestion of human milk proteins within the mammary gland, including plasmin and/or trypsin, elastase, cathepsin D, pepsin, chymotrypsin, a glutamyl endopeptidase-like enzyme, and proline endopeptidase. Two proteins were most affected by enzyme hydrolysis: β-casein and polymeric immunoglobulin receptor. In contrast, other highly abundant milk proteins such as α-lactalbumin and lactoferrin appear to have undergone no proteolytic cleavage. A peptide sequence containing a known antimicrobial peptide is released in breast milk by elastase and cathepsin D.

A peptidomic analysis of human milk digestion in the infant stomach reveals protein-specific degradation patterns

In vitro digestion of isolated milk proteins results in milk peptides with a variety of actions. However, it remains unclear to what degree protein degradation occurs in vivo in the infant stomach and whether peptides previously annotated for bioactivity are released. This study combined nanospray LC separation with time-of-flight mass spectrometry, comprehensive structural libraries, and informatics to analyze milk from 3 human mothers and the gastric aspirates from their 4- to 12-d-old postpartum infants. Milk from the mothers contained almost 200 distinct peptides, demonstrating enzymatic degradation of milk proteins beginning either during lactation or between milk collection and feeding. In the gastric samples, 649 milk peptides were identified, demonstrating that digestion continues in the infant stomach. Most peptides in both the intact milk and gastric samples were derived from β-casein. The numbers of peptides from β-casein, lactoferrin, α-lactalbumin, lactadherin, κ-casein, serum albumin, bile salt-associated lipase, and xanthine dehydrogenase/oxidase were significantly higher in the gastric samples than in the milk samples (P < 0.05). A total of 603 peptides differed significantly in abundance between milk and gastric samples (P < 0.05). Most of the identified peptides have previously identified biologic activity. Gastric proteolysis occurs in the term infant in the first 2 wk of life, releasing biologically active milk peptides with immunomodulatory and antibacterial properties of clinical relevance to the proximal intestinal tract. Data are available via ProteomeXchange (identifier PXD000688).

Extensive in vivo human milk peptidomics reveals specific proteolysis yielding protective antimicrobial peptides

Milk is traditionally considered an ideal source of the basic elemental nutrients required by infants. More detailed examination is revealing that milk represents a more functional ensemble of components with benefits to both infants and mothers. A comprehensive peptidomics method was developed and used to analyze human milk yielding an extensive array of protein products present in the fluid. Over 300 milk peptides were identified originating from major and many minor protein components of milk. As expected, the majority of peptides derived from β-casein, however no peptide fragments from the major milk proteins lactoferrin, α-lactalbumin, and secretory immunoglobulin A were identified. Proteolysis in the mammary gland is selective-released peptides were drawn only from specific proteins and typically from only select parts of the parent sequence. A large number of the peptides showed significant sequence overlap with peptides with known antimicrobial or immunomodulatory functions. Antibacterial assays showed the milk peptide mixtures inhibited the growth of Escherichia coli and Staphylococcus aureus . The predigestion of milk proteins and the consequent release of antibacterial peptides may provide a selective advantage through evolution by protecting both the mother's mammary gland and her nursing offspring from infection.

Bioinformatics approaches for identifying new therapeutic bioactive peptides in food

The traditional methods for mining foods for bioactive peptides are tedious and long. Similar to the drug industry, the length of time to identify and deliver a commercial health ingredient that reduces disease symptoms can take anything between 5 to 10 years. Reducing this time and effort is crucial in order to create new commercially viable products with clear and important health benefits. In the past few years, bioinformatics, the science that brings together fast computational biology, and efficient genome mining, is appearing as the long awaited solution to this problem. By quickly mining food genomes for characteristics of certain food therapeutic ingredients, researchers can potentially find new ones in a matter of a few weeks. Yet, surprisingly, very little success has been achieved so far using bioinformatics in mining for food bioactives. The absence of food specific bioinformatic mining tools, the slow integration of both experimental mining and bioinformatics, and the important difference between different experimental platforms are some of the reasons for the slow progress of bioinformatics in the field of functional food and more specifically in bioactive peptide discovery. In this paper I discuss some methods that could be easily translated, using a rational peptide bioinformatics design, to food bioactive peptide mining. I highlight the need for an integrated food peptide database. I also discuss how to better integrate experimental work with bioinformatics in order to improve the mining of food for bioactive peptides, therefore achieving a higher success rates.Keywords: bioactive peptides, bioinformatics, mining food, therapeutic properties, food proteins, functional food.

Protein disorder and short conserved motifs in disordered regions are enriched near the cytoplasmic side of single-pass transmembrane proteins

Intracellular juxtamembrane regions of transmembrane proteins play pivotal roles in cell signalling, mediated by protein-protein interactions. Disordered protein regions, and short conserved motifs within them, are emerging as key determinants of many such interactions. Here, we investigated whether disorder and conserved motifs are enriched in the juxtamembrane area of human single-pass transmembrane proteins. Conserved motifs were defined as short disordered regions that were much more conserved than the adjacent disordered residues. Human single-pass proteins had higher mean disorder in their cytoplasmic segments than their extracellular parts. Some, but not all, of this effect reflected the shorter length of the cytoplasmic tail. A peak of cytoplasmic disorder was seen at around 30 residues from the membrane. We noted a significant increase in the incidence of conserved motifs within the disordered regions at the same location, even after correcting for the extent of disorder. We conclude that elevated disorder within the cytoplasmic tail of many transmembrane proteins is likely to be associated with enrichment for signalling interactions mediated by conserved short motifs.

Differences in the number of intrinsically disordered regions between yeast duplicated proteins, and their relationship with functional divergence

Background

Intrinsically disordered regions are enriched in short interaction motifs that play a critical role in many protein-protein interactions. Since new short interaction motifs may easily evolve, they have the potential to rapidly change protein interactions and cellular signaling. In this work we examined the dynamics of gain and loss of intrinsically disordered regions in duplicated proteins to inspect if changes after genome duplication can create functional divergence. For this purpose we used Saccharomyces cerevisiae and the outgroup species Lachancea kluyveri.

Principal Findings

We find that genes duplicated as part of a genome duplication (ohnologs) are significantly more intrinsically disordered than singletons (p<2.2^e-16, Wilcoxon), reflecting a preference for retaining intrinsically disordered proteins in duplicate. In addition, there have been marked changes in the extent of intrinsic disorder following duplication. A large number of duplicated genes have more intrinsic disorder than their L. kluyveri ortholog (29% for duplicates versus 25% for singletons) and an even greater number have less intrinsic disorder than the L. kluyveri ortholog (37% for duplicates versus 25% for singletons). Finally, we show that the number of physical interactions is significantly greater in the more intrinsically disordered ohnolog of a pair (p = 0.003, Wilcoxon).

Conclusion

This work shows that intrinsic disorder gain and loss in a protein is a mechanism by which a genome can also diverge and innovate. The higher number of interactors for proteins that have gained intrinsic disorder compared with their duplicates may reflect the acquisition of new interaction partners or new functional roles.

Shift in the isoelectric-point of milk proteins as a consequence of adaptive divergence between the milks of mammalian species

BACKGROUND

Milk proteins are required to proceed through a variety of conditions of radically varying pH, which are not identical across mammalian digestive systems. We wished to investigate if the shifts in these requirements have resulted in marked changes in the isoelectric point and charge of milk proteins during evolution.

RESULTS

We investigated nine major milk proteins in 13 mammals. In comparison with a group of orthologous non-milk proteins, we found that 3 proteins κ-casein, lactadherin, and muc1 have undergone the highest change in isoelectric point during evolution. The pattern of non-synonymous substitutions indicate that selection has played a role in the isoelectric point shift, since residues that show significant evidence of positive selection are much more likely to be charged (p = 0.03 for κ-casein; p < 10-8 for muc1). However, this selection does not appear to be solely due to adaptation to the diversity of mammalian digestive systems, since striking changes are seen among species that resemble each other in terms of their digestion.

CONCLUSION

The changes in charge are most likely due to changes of other protein functions, rather than an adaptation to the different mammalian digestive systems. These functions may include differences in bioactive peptide releases in the gut between different mammals, which are known to be a major contributing factor in the functional and nutritional value of mammalian milk. This raises the question of whether bovine milk is optimal in terms of particular protein functions, for human nutrition and possibly disease resistance.

Evolutionary Origins of the Fumonisin Secondary Metabolite Gene Cluster in Fusarium verticillioides and Aspergillus niger

The secondary metabolite gene clusters of euascomycete fungi are among the largest known clusters of functionally related genes in eukaryotes. Most of these clusters are species specific or genus specific, and little is known about how they are formed during evolution. We used a comparative genomics approach to study the evolutionary origins of a secondary metabolite cluster that synthesizes a polyketide derivative, namely, the fumonisin (FUM) cluster of Fusarium verticillioides, and that of Aspergillus niger another fumonisin (fumonisin B) producing species. We identified homologs in other euascomycetes of the Fusarium verticillioides FUM genes and their flanking genes. We discuss four models for the origin of the FUM cluster in Fusarium verticillioides and argue that two of these are plausible: (i) assembly by relocation of initially scattered genes in a recent Fusarium verticillioides; or (ii) horizontal transfer of the FUM cluster from a distantly related Sordariomycete species. We also propose that the FUM cluster was horizontally transferred into Aspergillus niger, most probably from a Sordariomycete species.

Evolution of the isoelectric point of mammalian proteins as a consequence of indels and adaptive evolution

Although important shifts in the isoelectric point of prokaryotic proteins, mainly due to adaptation to environmental pH, have been widely reported, such studies have not covered mammalian proteins, where pH changes may relate to changes in subcellular or tissue compartmentalization. We explored the isoelectric point of the proteome of 13 mammalian species. We detected proteins that have shifted their pI the most among 13 mammalian species, and investigated if these differences reflect adaptations of the orthologous proteins to different conditions. We find that proteins exhibiting a high isoelectric point change are enriched in certain GO terms, including immune defense, and mitochondrial proteins. We show that the shift in pI between orthologous proteins is not strongly associated with the overall rate of protein evolution, nor with protein length. Our results reveal that insertions/deletions are the main reason behind the shift of pI. However, for some proteins we find evidence of selection shifting the pI of the protein through amino acid replacement. Finally, we argue that shifts in pI might relate to the gain of additional activities, such as new interacting partners, in one ortholog as opposed to the other, and may potentially relate to functional differences between mammals.

SMURF: Genomic mapping of fungal secondary metabolite clusters

Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters.

Genomic islands in the pathogenic filamentous fungus Aspergillus fumigatus

We present the genome sequences of a new clinical isolate of the important human pathogen, Aspergillus fumigatus, A1163, and two closely related but rarely pathogenic species, Neosartorya fischeri NRRL181 and Aspergillus clavatus NRRL1. Comparative genomic analysis of A1163 with the recently sequenced A. fumigatus isolate Af293 has identified core, variable and up to 2% unique genes in each genome. While the core genes are 99.8% identical at the nucleotide level, identity for variable genes can be as low 40%. The most divergent loci appear to contain heterokaryon incompatibility (het) genes associated with fungal programmed cell death such as developmental regulator rosA. Cross-species comparison has revealed that 8.5%, 13.5% and 12.6%, respectively, of A. fumigatus, N. fischeri and A. clavatus genes are species-specific. These genes are significantly smaller in size than core genes, contain fewer exons and exhibit a subtelomeric bias. Most of them cluster together in 13 chromosomal islands, which are enriched for pseudogenes, transposons and other repetitive elements. At least 20% of A. fumigatus-specific genes appear to be functional and involved in carbohydrate and chitin catabolism, transport, detoxification, secondary metabolism and other functions that may facilitate the adaptation to heterogeneous environments such as soil or a mammalian host. Contrary to what was suggested previously, their origin cannot be attributed to horizontal gene transfer (HGT), but instead is likely to involve duplication, diversification and differential gene loss (DDL). The role of duplication in the origin of lineage-specific genes is further underlined by the discovery of genomic islands that seem to function as designated “gene dumps” and, perhaps, simultaneously, as “gene factories”.

Aspergillus is an extremely diverse genus of filamentous ascomycetous fungi (molds) found ubiquitously in soil and decomposing vegetation. Being supreme opportunists, aspergilli have adapted to overcome various chemical, physical, and biological stresses found in heterogeneous environments. While most species in the genus are saprophytes, a surprising number are able to infect wounded plants and animals. Remarkably, the allergic human host also responds abnormally to the aspergilli with lung and sinus disease. The advent of immunosuppressive agents and other medical advances have created a large worldwide pool of human hosts susceptible to some Aspergillus species, including the world's most harmful mold and the causative agent of invasive aspergillosis, Aspergillus fumigatus. In this study, we have used the power of comparative genomics to gain insight into genetic mechanisms that may contribute to the metabolic versatility and pathogenicity of this important human pathogen. Comparison of the genomes of two A. fumigatus clinical isolates and two closely related, but rarely pathogenic species showed that their genomes contain several large isolate- and species-specific chromosomal islands. The metabolic capabilities encoded by these highly labile regions are likely to contribute to their rapid adaptation to heterogeneous environments such as soil or a living host.

Evidence for horizontal transfer of a secondary metabolite gene cluster between fungi

BACKGROUND

Filamentous fungi synthesize many secondary metabolites and are rich in genes encoding proteins involved in their biosynthesis. Genes from the same pathway are often clustered and co-expressed in particular conditions. Such secondary metabolism gene clusters evolve rapidly through multiple rearrangements, duplications and losses. It has long been suspected that clusters can be transferred horizontally between species, but few concrete examples have been described so far.

RESULTS

In the rice blast fungus Magnaporthe grisea, the avirulence gene ACE1 that codes for a hybrid polyketide synthase-nonribosomal peptide synthetase (PKS-NRPS) belongs to a cluster of 15 genes involved in secondary metabolism. Additional related clusters were detected in the ascomycetes Chaetomium globosum, Stagonospora nodorum and Aspergillus clavatus. Gene-by-gene phylogenetic analysis showed that in C. globosum and M. grisea, the evolution of these ACE1-like clusters is characterized by successive complex duplication events including tandem duplication within the M. grisea cluster. The phylogenetic trees also present evidence that at least five of the six genes in the homologous ACE1 gene cluster in A. clavatus originated by horizontal transfer from a donor closely related to M. grisea.

CONCLUSION

The ACE1 cluster originally identified in M. grisea is shared by only few fungal species. Its sporadic distribution within euascomycetes is mainly explained by multiple events of duplication and losses. However, because A. clavatus contains an ACE1 cluster of only six genes, we propose that horizontal transfer from a relative of M. grisea into an ancestor of A. clavatus provides a much simpler explanation of the observed data than the alternative of multiple events of duplication and losses of parts of the cluster.

Postmortem findings in 22 victims due to two grain silo explosions in France

In 1982 and 1997 there were explosions of barley-containing silos in France, one in Metz (1982) and the other in Blaye (1997). There were a total of 25 victims, including 23 fatalities (12 in Metz and 11 in Blaye). In each case there was one survivor. This report is a retrospective study of 22 victims (11 at each site); all of them had multiple lesions due to the explosion and the immediate aftermath. The lesions demonstrated direct involvement of the blast effect associated with silo destruction, the heat from the explosion, the effects of toxic gases and asphyxia. The forensic pathologist is an important member of the emergency response team. This team has the responsibility for initially assessing the damage and for identifying the victims. The role of the forensic pathologist is therefore essential and they should be included in emergency planning to facilitate the initial assessment, shorten the time taken to identify the victims and improve safety procedures. Although dust explosions in agro-business plants are occurring more frequently, postmortem data are rare.