No description available.

Despite their homology and analogous function, the Hox gene clusters of vertebrates and invertebrates are subject to different constraints on their structural organization. This is demonstrated by a drastically different distribution of repetitive DNA elements in the Hox cluster regions. While gnathostomes have a strong tendency to exclude repetitive DNA elements from the inside of their Hox clusters, no such trend can be detected in the Hox gene clusters of protostomes. Repeats “invade” the gnathostome Hox clusters from the 5′ and 3′ ends while the core of the clusters remains virtually free of repetitive DNA. This invasion appears to be correlated with relaxed constraints associated with gene loss after cluster duplications.

Identifying biosynthetic gene clusters from genomic data is challenging, with many in-silico tools suffering from a high rediscovery rate due to their dependence on rule-based algorithms. Next generation sequencing has provided an abundance of genomic information, and it has been hypothesized that there are many undiscovered biosynthetic gene clusters within this dataset. Here, we aim to develop a machine learning tool, ML-Miner, that infers patterns that describe a biosynthetic gene cluster in an unbiased manner and, as such, enables the identification of new biosynthetic gene clusters from genomic data. To solve this challenging problem, we define a simpler one to predict the class of a known BGC. Specifically, ML-Miner receives as input the concatenation of sequences that are known or believed to be part of a biosynthetic gene cluster. Its task is to identify which class it belongs, i.e. NPRS, PKS terpene and RiPPs. ML-Miner is a machine learning tool that uses Natural Language Processing, dimensionality reduction, and supervised learning to identify novel biosynthetic gene clusters. BioVec is a biological word embedding that we use to transform protein sequences from the highly curated MIBiG database of characterized biosynthetic gene clusters into their respective continuous distributed vector representations. Because the resulting protein vectors are of high dimensionality, a supervised Uniform Manifold and Approximation algorithm was employed to transform the high dimensional vectors into a robust lower-dimensional representation, as evaluated by Silhouette analysis, Hopkins’ statistic, and trustworthiness analysis. The density-Based Spatial Clustering of Applications and Noise algorithm showed that the clusters identified from the low dimensional datasets mapped to biosynthetic gene cluster types, defined with high accuracy in the MIBiG database. A random forest classifier was then trained and evaluated using the low dimensional vectors. It was shown to classify each biosynthetic gene cluster from the MIBiG database with excellent performance metrics. Finally, the model's ability to generalize was evaluated using biosynthetic gene clusters from the antiSMASH dataset, an uncurated database containing uncharacterized biosynthetic gene clusters. The performance metrics were high, with a balanced accuracy of ~85%. After a hyperparameter search, the balanced accuracy rose to ~90%. This suggests that ML-Miner is a robust machine learning pipeline that can be used to identify novel biosynthetic gene clusters. Future development of a confidence score for classification and a workflow for processing bacterial genomes into gene clusters will significantly improve the utility of this tool.

The gene that produces the precursor RNA transcript to the three largest ribosomal RNA molecules (rDNA) is present in multiple copies and organized into gene clusters. They represent 0.5% of the diploid human genome but are critical for cellular viability. The individual genes possess very high levels of sequence identity and are present in high local concentration, making them ideal substrates for genomic rearrangement driven by dysregulated homologous recombination. Our laboratory has developed a sensitive physical assay capable of detecting recombination-mediated genomic restructuring in the rDNA by monitoring changes in lengths of the individual clusters. In order to determine whether dysregulated recombination is a potential driving force of genomic instability in human cancer, adult patients with either lung or colorectal cancer, and pediatric patients with leukemia were prospectively recruited and assayed. Over half of the adult solid tumors show detectable rDNA rearrangements relative to either surrounding non-tumor tissue or normal peripheral blood. In contrast, there is a greatly reduced frequency of alteration in pediatric leukemia. This finding makes rDNA restructuring one of the most common chromosomal alterations in adult solid tumors, illustrates the dynamic plasticity of the human genome, and may have prognostic or predictive value in disease progression.

Les cyanobactéries des genres Microcystis et Planktothrix sont des micro-organismes capables de proliférer dans de nombreux plans d'eau douce. Ces proliférations sont associées à des risques pour la santé humaine et animale en raison de la production de produits naturels, parmi lesquels des toxines. Ces molécules présentent une diversité de structure chimique de d'activités biologiques d'intérêt pour l'industrie pharmaceutique et biotechnologique. Nous avons revisité le potentiel en produits naturels de Microcystis et Planktothrix. Par des approches complémentaires de biologie moléculaire, génomique et transcriptomique, nous avons caractérisé des gènes impliqués dans la synthèse de ces produits naturels ainsi qu'exploré leur évolution et la régulation de leur expression. Ces travaux ont permis de mettre à jour une distribution, des évènements évolutifs et des profils d'expression surprenants et permettent d'envisager de nouvelles applications pour les produits naturels. / Microcystis and Planktothrix are cyanobacterial genus commonly proliferating in freshwater ecosystems. These blooms are associated with human and animal health threat because of synthesis of natural products and cyanotoxins. These compounds are of great chemical diversity and of interest for biotechnological and pharmaceutical applications. We revisited the natural products potential of Microcystis and Planktothrix. Combining molecular biology, genomics and transcriptomics investigations, we characterized natural products gene clusters. We studied their distribution, evolution and transcription as well. This work uncovered new distribution pattern, evolutionary events and unexpected expression patterns. These insights will allow new investigations and applications for cyanobacterial natural products.

<p>  </p> <p>Actinobacteria are a large and diverse group of bacteria that are known to produce a wide range of secondary metabolites, many of which have important biological activities, including antibiotics, anti-cancer agents, and immunosuppressants. The biosynthesis of these compounds is often highly regulated, with many natural products being produced at very low levels in laboratory settings. Environmental factors, such as small molecule elicitors, can induce the production of secondary metabolites. These elicitors can be natural products, including antibiotics or hormones, or synthetic compounds. The use of small molecule elicitors to induce the production of secondary metabolites has several advantages. First, addition of elicitors to fermentation media can result in increased titers of known natural products. Second, elicitors can enable the discovery of novel natural products typically produced at undetectable levels. In recent years, there has been a growing interest in the use of small molecule elicitors to induce the production of secondary metabolites from actinobacteria, especially for the discovery of “silent” natural products. In this work, we sought to expand on the method of chemical induction by utilizing oxytetracycline at a sub-MIC concentration to induce secondary metabolite production in Streptomyces. We have shown that translation-inhibiting antibiotics, specifically oxytetracycline, have a profound effect on the production of coeliomycin P1, actinorhodin, and calcium-dependent antibiotics (CDAs) in S. coelicolor and S. lividans. The expression of actinorhodin in S. lividans under these conditions is unique, unlike its counterpart, S. coelicolor, which can produce actinorhodin under standard conditions. In addition to the increased production of known secondary metabolites, we have also demonstrated the induction of BGCs in several other strains of Streptomyces, which were observed via LC-MS. </p> <p>In addition to exploring antibiotics as elicitors we have explored the traditional approach of natural product discovery by taking an bioactivity guided approach. Several strain that we isolated from soil collect of Hawaii were screened for activity against several pathogenic strains primarily looking for which strain will inhibit the growth of a. baumannii, which is an intriguing target because the rate of resistance to common antibacterial medication is rising and it’s membrane composition is vastly different compared to other gram negative bacterium like E.coli. From this preliminary screening 1 strain (Streptomyces sp. CS62) out of the 8 that tested exhibited the desired biological activity. The supernatant of Streptomyces sp .CS62 was processed and screen by LC-MS to gain insight on the type of molecules that Streptomyces CS62 could produce. Upon our initial screening process none of the masses observed in the mass spec were matched to knowns. However, after 2D NMR analysis and genomic analysis it was unveiled that Streptomyces sp. CS62 produces factumycin a known antibacterial agent that targets A.baumannii .This unfourtunate turn of events illustrates the issues with natural product discovery and the need to improve natural product databases.</p> <p>In conjunction to discovering a novel producer of factumycin we are also investigating the production of antifungal compounds from Staphylococcus lugdunensis  a commensal strain that modulates the microbiome by producing lugdunin. The supernatant collected of Staphylococcus lugdunensis  is exclusively being test against Candida auris due to the immense health risk it possess to society because of its innate resistance to many antifungal drugs and its ability to rapidly gain resistance to other classes of antifungals.</p> <p>In addition to exploring the influence of antibiotics on secondary metabolite production and using bioactivity as a guide to discovering antibiotics. We are evaluating the soils collected from unique environments as potential sources for novel natural products. Specifically, we are evaluating the biosynthetic potential of bacteria from ore-forming environments, specifically fluorspar and topaz mines. Soils from ore-forming environments tend have low pH, high saline content, low water holding capacity, and poor nutrient availability. Therefore, ore-forming environments pose a hostile environment for life. To date, no one has explored the natural product potential, or the bacterial diversity, exhibited in these harsh environments. To assess the bacterial diversity, bacteria were isolated from various ore-forming environments using a procedure that is selective for actinobacteria. Following bacterial isolation, genomic DNA was isolated and 16s rRNA gene sequencing was performed to gauge the type of bacteria that were isolated. To stimulate secondary metabolite production, bacteria were then subjected to 7 different media conditions. The supernatant collected from these media conditions were tested against ESKAPE pathogens utilizing the CTSI broth microdilution assay. LC-MS MS analysis was performed for samples exhibiting biological activity. GNPS molecular networking was then utilized to determine potential molecules present in each sample.  Through this process we were able to identify one strain, which we named Streptomyces sp. S1A that exhibited a board range of biological activity (anticancer and antibacterial) and possess a wide array of biosynthetic gene clusters ranging complex macrolides (PKS and NRPS) to terpenes. </p> <p>In summary this multifaced approach to natural product discovery may lead to the discovery of novel antibiotics, enable us to increase production of known or unknown antibiotics through chemical induction, and the characterization of metabolites from Streptomyces sp. S1A will shed insight on the biochemical potential of organisms that inhabit ore-forming environments </p>

No description available.

The crucial part of the biosynthesis of lincosamide antibiotics lincomycin and celesticetin is the condensation of amino sugar and amino acid moieties. This reaction is catalysed by the oligomeric enzyme lincosamide synthetase (LS). One of the most important components of LS is adenylation domain recognizing and activating amino acid precursor. The substrate specificity of adenylation domain is determined by "nonribosomal code", 10 amino acids residues which side chains are in close contact with the activated substrate. The homologous adenylation domains LmbC from biosynthesis of lincomycin and CcbC from biosynthesis of celesticetin exhibit strong substrate specificity for their natural substrates (2S,4R)-4-propyl-L-proline (PPL) and L-proline, respectively. At first the effect of selected amino acid residues of LmbC nonribosomal code on the substrate specificity of the whole domain was tested. The amino acids residues, most important for preference of PPL substrate over L proline, were determined: G308, A207 and L246. Then the effect of double mutations in nonribosomal codes of both LmbC and CcbC on their substrate specificity was evaluated. The double mutants LmbC G308V + A207F and CcbC V306G + F205A were prepared and tested biochemically. The results brought new evidence of validity of homologous models...

No description available.

Odorant-binding proteins (OBPs) are small soluble proteins expressed at high levels in the proximity of olfactory receptors. OBPs act as solubilizers and carriers of the lipophilic odorants in the aqueous mucus of mammals and other vertebrates. OBPs have now been studied nearly thirty years, but in comparison to the wealth of data available on their structural chemistry and molecular biology, our knowledge about gene expression and function of these proteins is still insufficient. This work provides new insights into the tissue specificity of OBP and presents several new sequences of genes governing these proteins in selected species of mice.