Decoding semiotic minimal genome: a non-genocentric approach.

The search for the minimum information required for an organism to sustain a cellular system network has rendered both the identification of a fixed number of known genes and those genes whose function remains to be identified. The approaches used in such search generally focus their analysis on coding genomic regions, based on the genome to proteic-product perspective. Such approaches leave other fundamental processes aside, mainly those that include higher-level information management. To cope with this limitation, a non-genocentric approach based on genomic sequence analysis using language processing tools and gene ontology may prove an effective strategy for the identification of those fundamental genomic elements for life autonomy. Additionally, this approach will provide us with an integrative analysis of the information value present in all genomic elements, regardless of their coding status.

Preliminary Evidence for a Positive Relation Between the COMT rs4680 Met/Met Genotype and Math Achievement.

To identify if COMT polymorphisms interact with executive functions as predictors of math skills, we assessed 38 adolescents (mean age = 16.4 ± 0.80 years, IQ > 80) from a larger study of high-school students screened for their mathematical abilities. Adolescents were genotyped for the COMT Val158Met polymorphism (grouped as Met/Met or Val-carriers) and completed the WRAT math achievement test, working-memory, inhibitory-control, and shifting tasks. Met/Met-carriers achieved higher WRAT scores than the Val-carriers (W = 229, p = .009). Genotype group was a moderate-to-strong predictor of WRAT scores (ß = 0.56 to 0.74). No genotype/executive-function interaction was detected. Our findings suggest that the rs4680 Met/Met genotype is positively associated with math achievement.

The calculus of codes - From entropy, complexity, and information to life.

Exploring the core components that define living systems and their operational mechanisms within emerging biological entities is a complex endeavor. In the realm of biological systems literature, the terms matter, energy, information, complexity, and entropy are frequently referenced. However, possessing these concepts alone does not guarantee a comprehensive understanding or the ability to reconstruct the intricate nature of life. This study aims to illuminate the trajectory of these organic attributes, presenting a theoretical framework that delves into the integrated role of these concepts in biology. We assert that Code Biology serves as a pivotal steppingstone for unraveling the mechanisms underlying life. Biological codes (BCs) emerge not only from the interplay of matter and energy but also from Information. Contrary to deriving information from the former elements, we propose that information holds its place as a fundamental physical aspect. Consequently, we propose a continuum perspective called Calculus of Fundamentals involving three fundamentals: Matter, Energy, and Information, to depict the dynamics of BCs. To achieve this, we emphasize the necessity of studying Entropy and Complexity as integral organic descriptors. This perspective also facilitates the introduction of a mathematical theoretical framework that aids in comprehending continuous changes, the driving dynamics of biological fundamentals. We posit that Energy, Matter, and Information constitute the essential building blocks of living systems, and their interactions are governed by Entropy and Complexity analyses, redefined as biological descriptors. This interdisciplinary perspective of Code Biology sheds light on the intricate interplay between the controversial phenomenon of life and advances the idea of constructing a theory rooted in information as an organic fundamental.

The Enzymatic Core of Scorpion Venoms.

Enzymes are an integral part of animal venoms. Unlike snakes, in which enzymes play a primary role in envenomation, in scorpions, their function appears to be ancillary in most species. Due to this, studies on the diversity of scorpion venom components have focused primarily on the peptides responsible for envenomation (toxins) and a few others (e.g., antimicrobials), while enzymes have been overlooked. In this work, a comprehensive study on enzyme diversity in scorpion venoms was performed by transcriptomic and proteomic techniques. Enzymes of 63 different EC types were found, belonging to 330 orthogroups. Of them, 24 ECs conform the scorpion venom enzymatic core, since they were determined to be present in all the studied scorpion species. Transferases and lyases are reported for the first time. Novel enzymes, which can play different roles in the venom, including direct toxicity, as venom spreading factors, activators of venom components, venom preservatives, or in prey pre-digestion, were described and annotated. The expression profile for transcripts coding for venom enzymes was analyzed, and shown to be similar among the studied species, while being significantly different from their expression pattern outside the telson.

Diploid genome assembly of Kluyveromyces marxianus NRRL Y-50883 (SLP1).

The yeast Kluyveromyces marxianus SLP1 has the potential for application in biotechnological processes because it can metabolize several sugars and produce high-value metabolites. K. marxianus SLP1 is a thermotolerant yeast isolated from the mezcal process, and it is tolerant to several cell growth inhibitors such as saponins, furan aldehydes, weak acids, and phenolics compounds. The genomic differences between dairy and nondairy strains related to K. marxianus variability are a focus of research attention, particularly the pathways leading this species toward polyploidy. We report the diploid genome assembly of K. marxianus SLP1 nonlactide strain into 32 contigs to reach a size of ∼12 Mb (N50 = 1.3 Mb) and a ∼39% GC content. Genome size is consistent with the k-mer frequency results. Genome annotation by Funannotate estimated 5000 genes in haplotype A and 4910 in haplotype B. The enriched annotated genes by ontology show differences between alleles in biological processes and cellular component. The analysis of variants related to DMKU3 and between haplotypes shows changes in LAC12 and INU1, which we hypothesize can impact carbon source performance. This report presents the first polyploid K. marxianus strain recovered from nonlactic fermenting medium.

Unraveling the hCoV-19 Informational Architecture.

The hCoV-19 virus is continuously evolving to highly infectious and lethal variants. There is a latent risk that current vaccines will not be effective over these novel variants. This entails comprehending the genome-wide viral information to unveil mutagenic mechanisms of hCoV-19. To date, this virus is studied as a collection of non-related variants, making it challenging to forecast hotspots and their upcoming effects. In this work, we explore genome-wide information to disentangle informational mechanisms that lead to insights into viral mutagenicity. Towards this aim, we modeled informational compartments based on a topic-free-alignment workflow. These compartments illustrate that hCoV-19 has a complex informational architecture that addresses high-level virus phenomena, i.e., mutagenicity. This new framework represents the first step towards identifying the virus mutagenicity leading to the development of all-variants-effective vaccines.

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome.

Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.

Metacode: One code to rule them all.

The code of codes or metacode is a microcosm where biological layers, as well as their codes, interact together allowing the continuity of information flow in organisms by increasing biological entities' complexity. Through this novel organic code, biological systems scale towards niches with higher informatic freedom building structures that increase the entropy in the universe. Code biology has developed a novel informational framework where biological entities strive themselves through the information flow carried out through organic codes consisting of two molecular or functional landscapes intertwined through arbitrary linkages via an adaptor whose nature is autonomous from molecular determinism. Here we will integrate genomic and epigenomic codes according to the evidence released in ENCODE (phase 3), psychENCODE and GTEx project, outlining the principles of the metacode, to address the continuous nature of biological systems and their inter-layered information flow. This novel complex metacode maps from very constrained sets of elements (i.e., regulation sites modulating gene expression) to new ones with greater freedom of decoding (i.e., a continuous cell phenotypic space). This leads to a new domain in code biology where biological systems are informatic attractors that navigate an energy metaspace through a complexity-noise balance, stalling in emergent niches where organic codes take meaning.

Frequency spectra characterization of noncoding human genomic sequences.

BACKGROUND: Noncoding sequences have been demonstrated to possess regulatory functions. Its classification is challenging because they do not show well-defined nucleotide patterns that can correlate with their biological functions. Genomic signal processing techniques like Fourier transform have been employed to characterize coding and noncoding sequences. This transformation in a systematic whole-genome noncoding library, such as the ENCODE database, can provide evidence of a periodic behaviour in the noncoding sequences that correlates with their regulatory functions. OBJECTIVE: The objective of this study was to classify different noncoding regulatory regions through their frequency spectra. METHODS: We computed machine learning algorithms to classify the noncoding regulatory sequences frequency spectra. RESULTS: The sequences from different regulatory regions, cell lines, and chromosomes possessed distinct frequency spectra, and that machine learning classifiers (such as those of the support vector machine type) could successfully discriminate among regulatory regions, thus correlating the frequency spectra with their biological functions CONCLUSION: Our work supports the idea that there are patterns in the noncoding sequences of the genome.

Whole-Genome k-mer Topic Modeling AssociatesBacterial Families.

Alignment-free k-mer-based algorithms in whole genome sequence comparisons remainan ongoing challenge. Here, we explore the possibility to use Topic Modeling for organismwhole-genome comparisons. We analyzed 30 complete genomes from three bacterial families bytopic modeling. For this, each genome was considered as a document and 13-mer nucleotiderepresentations as words. Latent Dirichlet allocation was used as the probabilistic modeling of thecorpus. We where able to identify the topic distribution among analyzed genomes, which is highlyconsistent with traditional hierarchical classification. It is possible that topic modeling may be appliedto establish relationships between genome's composition and biological phenomena.

Modeling Quorum Sensing Dynamics and Interference on Escherichia coli.

Bacteria control the expression of specific genes by Quorum Sensing (QS). This works using small signaling molecules called Autoinducers (AIs), for example, the Autoinducer-2 (AI-2). In this work, we present a mathematical model that represents the AI-2 dynamics on Escherichia coli, which is linked to the cell growth and the lsr operon expression. The model is adjusted using experimental data. Our results suggest that the extracellular AI-2 activity level depends on the cell growth rate, and this activity depends on the cell exponential growth phase. The model was adapted to simulate the interference of QS mechanisms in a co-culture of two E. coli strains: a wild type strain and a knock out strain that detects AI-2 but does not produce it. Co-culture simulations unveiled two conditions to avoid the QS on the wild strain: when the knock out takes control of the growth medium and overcomes the wild strain, or when is pre-cultured to its mid-exponential phase and then added to the wild strain culture. Model simulations unveiled new insights about the interference of bacterial communication and offer new tools for QS control.

From Probiotics to Psychobiotics: Live Beneficial Bacteria Which Act on the Brain-Gut Axis.

There is an important relationship between probiotics, psychobiotics and cognitive and behavioral processes, which include neurological, metabolic, hormonal and immunological signaling pathways; the alteration in these systems may cause alterations in behavior (mood) and cognitive level (learning and memory). Psychobiotics have been considered key elements in affective disorders and the immune system, in addition to their effect encompassing the regulation of neuroimmune regulation and control axes (the hypothalamic-pituitary-adrenal axis or HPA, the sympathetic-adrenal-medullary axis or SAM and the inflammatory reflex) in diseases of the nervous system. The aim of this review is to summarize the recent findings about psychobiotics, the brain-gut axis and the immune system. The review focuses on a very new and interesting field that relates the microbiota of the intestine with diseases of the nervous system and its possible treatment, in neuroimmunomodulation area. Indeed, although probiotic bacteria will be concentrated after ingestion, mainly in the intestinal epithelium (where they provide the host with essential nutrients and modulation of the immune system), they may also produce neuroactive substances which act on the brain-gut axis.

An Ontology Systems Approach on Human Brain Expression and Metaproteomics.

Research in the last decade has shown growing evidence of the gut microbiota influence on brain physiology. While many mechanisms of this influence have been proposed in animal models, most studies in humans are the result of a pathology-dysbiosis association and very few have related the presence of certain taxa with brain substructures or molecular pathways. In this paper, we associated the functional ontologies in the differential expression of brain substructures from the Allen Brain Atlas database, with those of the metaproteome from the Human Microbiome Project. Our results showed several coherent clustered ontologies where many taxa could influence brain expression and physiology. A detailed analysis of psychobiotics showed specific slim ontologies functionally associated with substructures in the basal ganglia and cerebellar cortex. Some of the most relevant slim ontology groups are related to Ion transport, Membrane potential, Synapse, DNA and RNA metabolism, and Antigen processing, while the most relevant neuropathology found was Parkinson disease. In some of these cases, new hypothetical gut microbiota-brain interaction pathways are proposed.

Genomic signal processing for DNA sequence clustering.

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

On DNA numerical representations for genomic similarity computation.

Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

Genomic signal processing methods for computation of alignment-free distances from DNA sequences.

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments.