An exploratory in silico comparison of open-source codon harmonization tools.

BACKGROUND: Not changing the native constitution of genes prior to their expression by a heterologous host can affect the amount of proteins synthesized as well as their folding, hampering their activity and even cell viability. Over the past decades, several strategies have been developed to optimize the translation of heterologous genes by accommodating the difference in codon usage between species. While there have been a handful of studies assessing various codon optimization strategies, to the best of our knowledge, no research has been performed towards the evaluation and comparison of codon harmonization algorithms. To highlight their importance and encourage meaningful discussion, we compared different open-source codon harmonization tools pertaining to their in silico performance, and we investigated the influence of different gene-specific factors. RESULTS: In total, 27 genes were harmonized with four tools toward two different heterologous hosts. The difference in %MinMax values between the harmonized and the original sequences was calculated (ΔMinMax), and statistical analysis of the obtained results was carried out. It became clear that not all tools perform similarly, and the choice of tool should depend on the intended application. Almost all biological factors under investigation (GC content, RNA secondary structures and choice of heterologous host) had a significant influence on the harmonization results and thus must be taken into account. These findings were substantiated using a validation dataset consisting of 8 strategically chosen genes. CONCLUSIONS: Due to the size of the dataset, no complex models could be developed. However, this initial study showcases significant differences between the results of various codon harmonization tools. Although more elaborate investigation is needed, it is clear that biological factors such as GC content, RNA secondary structures and heterologous hosts must be taken into account when selecting the codon harmonization tool.

Novel Alkaloids from Marine Actinobacteria: Discovery and Characterization.

The marine environment is an excellent resource for natural products with therapeutic potential. Its microbial inhabitants, often associated with other marine organisms, are specialized in the synthesis of bioactive secondary metabolites. Similar to their terrestrial counterparts, marine Actinobacteria are a prevalent source of these natural products. Here, we discuss 77 newly discovered alkaloids produced by such marine Actinobacteria between 2017 and mid-2021, as well as the strategies employed in their elucidation. While 12 different classes of alkaloids were unraveled, indoles, diketopiperazines, glutarimides, indolizidines, and pyrroles were most dominant. Discoveries were mainly based on experimental approaches where microbial extracts were analyzed in relation to novel compounds. Although such experimental procedures have proven useful in the past, the methodologies need adaptations to limit the chance of compound rediscovery. On the other hand, genome mining provides a different angle for natural product discovery. While the technology is still relatively young compared to experimental screening, significant improvement has been made in recent years. Together with synthetic biology tools, both genome mining and extract screening provide excellent opportunities for continued drug discovery from marine Actinobacteria.

Alkaloids from Marine Fungi: Promising Antimicrobials.

Resistance of pathogenic microorganisms against antimicrobials is a major threat to contemporary human society. It necessitates a perpetual influx of novel antimicrobial compounds. More specifically, Gram- pathogens emerged as the most exigent danger. In our continuing quest to search for novel antimicrobial molecules, alkaloids from marine fungi show great promise. However, current reports of such newly discovered alkaloids are often limited to cytotoxicity studies and, moreover, neglect to discuss the enigma of their biosynthesis. Yet, the latter is often a prerequisite to make them available through sufficiently efficient processes. This review aims to summarize novel alkaloids with promising antimicrobial properties discovered in the past five years and produced by marine fungi. Several discovery strategies are summarized, and knowledge gaps in biochemical production routes are identified. Finally, links between the structure of the newly discovered molecules and their activity are proposed. Since 2015, a total of 35 new antimicrobial alkaloids from marine fungi were identified, of which 22 showed an antibacterial activity against Gram- microorganisms. Eight of them can be classified as narrow-spectrum Gram- antibiotics. Despite this promising ratio of novel alkaloids active against Gram- microorganisms, the number of newly discovered antimicrobial alkaloids is low, due to the narrow spectrum of discovery protocols that are used and the fact that antimicrobial properties of newly discovered alkaloids are barely characterized. Alternatives are proposed in this review. In conclusion, this review summarizes novel findings on antimicrobial alkaloids from marine fungi, shows their potential as promising therapeutic candidates, and hints on how to further improve this potential.

Interpreting short tandem repeat variations in humans using mutational constraint.

Identifying regions of the genome that are depleted of mutations can distinguish potentially deleterious variants. Short tandem repeats (STRs), also known as microsatellites, are among the largest contributors of de novo mutations in humans. However, per-locus studies of STR mutations have been limited to highly ascertained panels of several dozen loci. Here we harnessed bioinformatics tools and a novel analytical framework to estimate mutation parameters for each STR in the human genome by correlating STR genotypes with local sequence heterozygosity. We applied our method to obtain robust estimates of the impact of local sequence features on mutation parameters and used these estimates to create a framework for measuring constraint at STRs by comparing observed versus expected mutation rates. Constraint scores identified known pathogenic variants with early-onset effects. Our metric will provide a valuable tool for prioritizing pathogenic STRs in medical genetics studies.

Genome-wide profiling of heritable and de novo STR variations.

Short tandem repeats (STRs) are highly variable elements that play a pivotal role in multiple genetic diseases, population genetics applications, and forensic casework. However, it has proven problematic to genotype STRs from high-throughput sequencing data. Here, we describe HipSTR, a novel haplotype-based method for robustly genotyping and phasing STRs from Illumina sequencing data, and we report a genome-wide analysis and validation of de novo STR mutations. HipSTR is freely available at https://hipstr-tool.github.io/HipSTR.

The Simons Genome Diversity Project: 300 genomes from 142 diverse populations.

Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.

Population-Scale Sequencing Data Enable Precise Estimates of Y-STR Mutation Rates.

Short tandem repeats (STRs) are mutation-prone loci that span nearly 1% of the human genome. Previous studies have estimated the mutation rates of highly polymorphic STRs by using capillary electrophoresis and pedigree-based designs. Although this work has provided insights into the mutational dynamics of highly mutable STRs, the mutation rates of most others remain unknown. Here, we harnessed whole-genome sequencing data to estimate the mutation rates of Y chromosome STRs (Y-STRs) with 2-6 bp repeat units that are accessible to Illumina sequencing. We genotyped 4,500 Y-STRs by using data from the 1000 Genomes Project and the Simons Genome Diversity Project. Next, we developed MUTEA, an algorithm that infers STR mutation rates from population-scale data by using a high-resolution SNP-based phylogeny. After extensive intrinsic and extrinsic validations, we harnessed MUTEA to derive mutation-rate estimates for 702 polymorphic STRs by tracing each locus over 222,000 meioses, resulting in the largest collection of Y-STR mutation rates to date. Using our estimates, we identified determinants of STR mutation rates and built a model to predict rates for STRs across the genome. These predictions indicate that the load of de novo STR mutations is at least 75 mutations per generation, rivaling the load of all other known variant types. Finally, we identified Y-STRs with potential applications in forensics and genetic genealogy, assessed the ability to differentiate between the Y chromosomes of father-son pairs, and imputed Y-STR genotypes.

Punctuated bursts in human male demography inferred from 1,244 worldwide Y-chromosome sequences.

We report the sequences of 1,244 human Y chromosomes randomly ascertained from 26 worldwide populations by the 1000 Genomes Project. We discovered more than 65,000 variants, including single-nucleotide variants, multiple-nucleotide variants, insertions and deletions, short tandem repeats, and copy number variants. Of these, copy number variants contribute the greatest predicted functional impact. We constructed a calibrated phylogenetic tree on the basis of binary single-nucleotide variants and projected the more complex variants onto it, estimating the number of mutations for each class. Our phylogeny shows bursts of extreme expansion in male numbers that have occurred independently among each of the five continental superpopulations examined, at times of known migrations and technological innovations.

Abundant contribution of short tandem repeats to gene expression variation in humans.

The contribution of repetitive elements to quantitative human traits is largely unknown. Here we report a genome-wide survey of the contribution of short tandem repeats (STRs), which constitute one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from that of linked SNPs and indels and found that eSTRs contribute 10-15% of the cis heritability mediated by all common variants. Further functional genomic analyses showed that eSTRs are enriched in conserved regions, colocalize with regulatory elements and may modulate certain histone modifications. By analyzing known genome-wide association study (GWAS) signals and searching for new associations in 1,685 whole genomes from deeply phenotyped individuals, we found that eSTRs are enriched in various clinically relevant conditions. These results highlight the contribution of STRs to the genetic architecture of quantitative human traits.

The landscape of human STR variation.

Short tandem repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across more than 1000 individuals in Phase 1 of the 1000 Genomes Project. Extensive quality controls show that reliable allelic spectra can be obtained for close to 90% of the STR loci in the genome. We utilize this call set to analyze determinants of STR variation, assess the human reference genome's representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations.

Similarity-driven discovery of zeolite materials for adsorption-based separations.

Crystalline porous materials can be exploited in many applications. Discovery of materials with optimum adsorption properties typically involves expensive brute-force characterization of large sets of materials. An alternative approach based on similarity searching that enables discovery of materials with optimum adsorption for CO(2) and other molecules at a fraction of the cost of brute-force characterization is demonstrated.

Molecular simulation of the effect of cholesterol on lipid-mediated protein-protein interactions.

Experiments and molecular simulations have shown that the hydrophobic mismatch between proteins and membranes contributes significantly to lipid-mediated protein-protein interactions. In this article, we discuss the effect of cholesterol on lipid-mediated protein-protein interactions as function of hydrophobic mismatch, protein diameter and protein cluster size, lipid tail length, and temperature. To do so, we study a mesoscopic model of a hydrated bilayer containing lipids and cholesterol in which proteins are embedded, with a hybrid dissipative particle dynamics-Monte Carlo method. We propose a mechanism by which cholesterol affects protein interactions: protein-induced, cholesterol-enriched, or cholesterol-depleted lipid shells surrounding the proteins affect the lipid-mediated protein-protein interactions. Our calculations of the potential of mean force between proteins and protein clusters show that the addition of cholesterol dramatically reduces repulsive lipid-mediated interactions between proteins (protein clusters) with positive mismatch, but does not affect attractive interactions between proteins with negative mismatch. Cholesterol has only a modest effect on the repulsive interactions between proteins with different mismatch.