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1.
Into the wild: new yeast genomes from natural environments and new tools for their analysis.
Libkind, D, Peris, D, Cubillos, FA, Steenwyk, JL, Opulente, DA, Langdon, QK, Rokas, A, Hittinger, CT
FEMS yeast research. 2020;(2)
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Abstract
Genomic studies of yeasts from the wild have increased considerably in the past few years. This revolution has been fueled by advances in high-throughput sequencing technologies and a better understanding of yeast ecology and phylogeography, especially for biotechnologically important species. The present review aims to first introduce new bioinformatic tools available for the generation and analysis of yeast genomes. We also assess the accumulated genomic data of wild isolates of industrially relevant species, such as Saccharomyces spp., which provide unique opportunities to further investigate the domestication processes associated with the fermentation industry and opportunistic pathogenesis. The availability of genome sequences of other less conventional yeasts obtained from the wild has also increased substantially, including representatives of the phyla Ascomycota (e.g. Hanseniaspora) and Basidiomycota (e.g. Phaffia). Here, we review salient examples of both fundamental and applied research that demonstrate the importance of continuing to sequence and analyze genomes of wild yeasts.
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The power of cooperation: Experimental and computational approaches in the functional characterization of bacterial sRNAs.
Georg, J, Lalaouna, D, Hou, S, Lott, SC, Caldelari, I, Marzi, S, Hess, WR, Romby, P
Molecular microbiology. 2020;(3):603-612
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Abstract
Trans-acting small regulatory RNAs (sRNAs) are key players in the regulation of gene expression in bacteria. There are hundreds of different sRNAs in a typical bacterium, which in contrast to eukaryotic microRNAs are more heterogeneous in length, sequence composition, and secondary structure. The vast majority of sRNAs function post-transcriptionally by binding to other RNAs (mRNAs, sRNAs) through rather short regions of imperfect sequence complementarity. Besides, every single sRNA may interact with dozens of different target RNAs and impact gene expression either negatively or positively. These facts contributed to the view that the entirety of the regulatory targets of a given sRNA, its targetome, is challenging to identify. However, recent developments show that a more comprehensive sRNAs targetome can be achieved through the combination of experimental and computational approaches. Here, we give a short introduction into these methods followed by a description of two sRNAs, RyhB, and RsaA, to illustrate the particular strengths and weaknesses of these approaches in more details. RyhB is an sRNA involved in iron homeostasis in Enterobacteriaceae, while RsaA is a modulator of virulence in Staphylococcus aureus. Using such a combined strategy, a better appreciation of the sRNA-dependent regulatory networks is now attainable.
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Essential Oil Phytocomplex Activity, a Review with a Focus on Multivariate Analysis for a Network Pharmacology-Informed Phytogenomic Approach.
Buriani, A, Fortinguerra, S, Sorrenti, V, Caudullo, G, Carrara, M
Molecules (Basel, Switzerland). 2020;(8)
Abstract
Thanks to omic disciplines and a systems biology approach, the study of essential oils and phytocomplexes has been lately rolling on a faster track. While metabolomic fingerprinting can provide an effective strategy to characterize essential oil contents, network pharmacology is revealing itself as an adequate, holistic platform to study the collective effects of herbal products and their multi-component and multi-target mediated mechanisms. Multivariate analysis can be applied to analyze the effects of essential oils, possibly overcoming the reductionist limits of bioactivity-guided fractionation and purification of single components. Thanks to the fast evolution of bioinformatics and database availability, disease-target networks relevant to a growing number of phytocomplexes are being developed. With the same potential actionability of pharmacogenomic data, phytogenomics could be performed based on relevant disease-target networks to inform and personalize phytocomplex therapeutic application.
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Automatic Gene Function Prediction in the 2020's.
Makrodimitris, S, van Ham, RCHJ, Reinders, MJT
Genes. 2020;(11)
Abstract
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.
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Human microbiome: an academic update on human body site specific surveillance and its possible role.
Dekaboruah, E, Suryavanshi, MV, Chettri, D, Verma, AK
Archives of microbiology. 2020;(8):2147-2167
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Abstract
Human body is inhabited by vast number of microorganisms which form a complex ecological community and influence the human physiology, in the aspect of both health and diseases. These microbes show a relationship with the human immune system based on coevolution and, therefore, have a tremendous potential to contribute to the metabolic function, protection against the pathogen and in providing nutrients and energy. However, of these microbes, many carry out some functions that play a crucial role in the host physiology and may even cause diseases. The introduction of new molecular technologies such as transcriptomics, metagenomics and metabolomics has contributed to the upliftment on the findings of the microbiome linked to the humans in the recent past. These rapidly developing technologies are boosting our capacity to understand about the human body-associated microbiome and its association with the human health. The highlights of this review are inclusion of how to derive microbiome data and the interaction between human and associated microbiome to provide an insight on the role played by the microbiome in biological processes of the human body as well as the development of major human diseases.
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Practically useful protein-design methods combining phylogenetic and atomistic calculations.
Weinstein, J, Khersonsky, O, Fleishman, SJ
Current opinion in structural biology. 2020;:58-64
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Abstract
Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.
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Phytopathogenic oomycetes: a review focusing on Phytophthora cinnamomi and biotechnological approaches.
de Andrade Lourenço, D, Branco, I, Choupina, A
Molecular biology reports. 2020;(11):9179-9188
Abstract
The Phytophthora genus is composed, mainly, of plant pathogens. This genus belongs to the Oomycete class, also known as "pseudo-fungi", within the Chromista Kingdom. Phytophthora spp. is highlighted due to the significant plant diseases that they cause, which represents some of the most economically and cultural losses, such as European chestnut ink disease, which is caused by P. cinnamomi. Currently, there have been four genome assemblies placed at the National Center for Biotechnology Information (NCBI), although the progress to understand and elucidate the pathogenic process of P. cinnamomi by its genome is progressing slowly. In this review paper, we aim to report and discuss the recent findings related to P. cinnamomi and its genomic information. Our research is based on paper databases that reported probable functions to P. cinnamomi proteins using sequence alignments, bioinformatics, and biotechnology approaches. Some of these proteins studied have functions that are proposed to be involved in the asexual sporulation and zoosporogenesis leading to the host colonization and consequently associated with pathogenicity. Some remarkable genes and proteins discussed here are related to oospore development, inhibition of sporangium formation and cleavage, inhibition of flagellar assembly, blockage of cyst germination and hyphal extension, and biofilm proteins. Lastly, we report some biotechnological approaches using biological control, studies with genome sequencing of P. cinnamomi resistant plants, and gene silencing through RNA interference (iRNA).
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Amino Acid Encoding Methods for Protein Sequences: A Comprehensive Review and Assessment.
Jing, X, Dong, Q, Hong, D, Lu, R
IEEE/ACM transactions on computational biology and bioinformatics. 2020;(6):1918-1931
Abstract
As the first step of machine-learning based protein structure and function prediction, the amino acid encoding play a fundamental role in the final success of those methods. Different from the protein sequence encoding, the amino acid encoding can be used in both residue-level and sequence-level prediction of protein properties by combining them with different algorithms. However, it has not attracted enough attention in the past decades, and there are no comprehensive reviews and assessments about encoding methods so far. In this article, we make a systematic classification and propose a comprehensive review and assessment for various amino acid encoding methods. Those methods are grouped into five categories according to their information sources and information extraction methodologies, including binary encoding, physicochemical properties encoding, evolution-based encoding, structure-based encoding, and machine-learning encoding. Then, 16 representative methods from five categories are selected and compared on protein secondary structure prediction and protein fold recognition tasks by using large-scale benchmark datasets. The results show that the evolution-based position-dependent encoding method PSSM achieved the best performance, and the structure-based and machine-learning encoding methods also show some potential for further application, the neural network based distributed representation of amino acids in particular may bring new light to this area. We hope that the review and assessment are useful for future studies in amino acid encoding.
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Proteomic and Bioinformatic Profiling of Transporters in Higher Plant Mitochondria.
Møller, IM, Rao, RSP, Jiang, Y, Thelen, JJ, Xu, D
Biomolecules. 2020;(8)
Abstract
To function as a metabolic hub, plant mitochondria have to exchange a wide variety of metabolic intermediates as well as inorganic ions with the cytosol. As identified by proteomic profiling or as predicted by MU-LOC, a newly developed bioinformatics tool, Arabidopsis thaliana mitochondria contain 128 or 143 different transporters, respectively. The largest group is the mitochondrial carrier family, which consists of symporters and antiporters catalyzing secondary active transport of organic acids, amino acids, and nucleotides across the inner mitochondrial membrane. An impressive 97% (58 out of 60) of all the known mitochondrial carrier family members in Arabidopsis have been experimentally identified in isolated mitochondria. In addition to many other secondary transporters, Arabidopsis mitochondria contain the ATP synthase transporters, the mitochondria protein translocase complexes (responsible for protein uptake across the outer and inner membrane), ATP-binding cassette (ABC) transporters, and a number of transporters and channels responsible for allowing water and inorganic ions to move across the inner membrane driven by their transmembrane electrochemical gradient. A few mitochondrial transporters are tissue-specific, development-specific, or stress-response specific, but this is a relatively unexplored area in proteomics that merits much more attention.
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Complexities of Understanding Function from CKD-Associated DNA Variants.
Lin, J, Susztak, K
Clinical journal of the American Society of Nephrology : CJASN. 2020;(7):1028-1040
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Abstract
Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type-specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype-phenotype association.