Advancements in bioinformatics and computational biology: 15th annual Polish Bioinformatic Society Symposium.

The Polish Bioinformatic Society (PTBI) Symposium convenes annually at leading Polish Universities, and in 2023, the Silesian University of Technology hosted participants from all over the world. The 15th PTBI Symposium, spanning a 3-day duration and divided into four scientific sessions, gathered around 100 participants and centered on research related to machine learning in biomedicine, RNA structure algorithms, next-generation sequencing methods, and microbiome analysis but was not limited to only those topics. The meeting also recognized outstanding research conducted by young scientists by awarding the best poster and best talk. Finally, the awards for the best PhD, MSc, and BSc thesis in bioinformatics defended in Poland were given. This report summarizes the key highlights and outcomes of the meeting.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Corrigendum: Ni2+-assisted hydrolysis may affect the human proteome; filaggrin degradation ex vivo as an example of possible consequences.

[This corrects the article DOI: 10.3389/fmolb.2022.828674.].

Insights from analyses of low complexity regions with canonical methods for protein sequence comparison.

Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.

Ni2+-Assisted Hydrolysis May Affect the Human Proteome; Filaggrin Degradation Ex Vivo as an Example of Possible Consequences.

Deficiency in a principal epidermal barrier protein, filaggrin (FLG), is associated with multiple allergic manifestations, including atopic dermatitis and contact allergy to nickel. Toxicity caused by dermal and respiratory exposures of the general population to nickel-containing objects and particles is a deleterious side effect of modern technologies. Its molecular mechanism may include the peptide bond hydrolysis in X1-S/T-c/p-H-c-X2 motifs by released Ni2+ ions. The goal of the study was to analyse the distribution of such cleavable motifs in the human proteome and examine FLG vulnerability of nickel hydrolysis. We performed a general bioinformatic study followed by biochemical and biological analysis of a single case, the FLG protein. FLG model peptides, the recombinant monomer domain human keratinocytes in vitro and human epidermis ex vivo were used. We also investigated if the products of filaggrin Ni2+-hydrolysis affect the activation profile of Langerhans cells. We found X1-S/T-c/p-H-c-X2 motifs in 40% of human proteins, with the highest abundance in those involved in the epidermal barrier function, including FLG. We confirmed the hydrolytic vulnerability and pH-dependent Ni2+-assisted cleavage of FLG-derived peptides and FLG monomer, using in vitro cell culture and ex-vivo epidermal sheets; the hydrolysis contributed to the pronounced reduction in FLG in all of the models studied. We also postulated that Ni-hydrolysis might dysregulate important immune responses. Ni2+-assisted cleavage of barrier proteins, including FLG, may contribute to clinical disease associated with nickel exposure.

MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

SUMMARY: Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. AVAILABILITY AND IMPLEMENTATION: MAINE is freely available at maine.ibemag.pl as an online web application. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections-Upper Silesia pilot study.

In the DECODE project, data were collected from 3,114 surveys filled by symptomatic patients RT-qPCR tested for SARS-CoV-2 in a single university centre in March-September 2020. The population demonstrated balanced sex and age with 759 SARS-CoV-2( +) patients. The most discriminative symptoms in SARS-CoV-2( +) patients at early infection stage were loss of taste/smell (OR = 3.33, p < 0.0001), body temperature above 38℃ (OR = 1.67, p < 0.0001), muscle aches (OR = 1.30, p = 0.0242), headache (OR = 1.27, p = 0.0405), cough (OR = 1.26, p = 0.0477). Dyspnea was more often reported among SARS-CoV-2(-) (OR = 0.55, p < 0.0001). Cough and dyspnea were 3.5 times more frequent among SARS-CoV-2(-) (OR = 0.28, p < 0.0001). Co-occurrence of cough, muscle aches, headache, loss of taste/smell (OR = 4.72, p = 0.0015) appeared significant, although co-occurrence of two symptoms only, cough and loss of smell or taste, means OR = 2.49 (p < 0.0001). Temperature > 38℃ with cough was most frequent in men (20%), while loss of taste/smell with cough in women (17%). For younger people, taste/smell impairment is sufficient to characterise infection, whereas in older patients co-occurrence of fever and cough is necessary. The presented study objectifies the single symptoms and interactions significance in COVID-19 diagnoses and demonstrates diverse symptomatology in patient groups.

Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development.

BACKGROUND: The rapid spread of the COVID-19 demands immediate response from the scientific communities. Appropriate countermeasures mean thoughtful and educated choice of viral targets (epitopes). There are several articles that discuss such choices in the SARS-CoV-2 proteome, other focus on phylogenetic traits and history of the Coronaviridae genome/proteome. However none consider viral protein low complexity regions (LCRs). Recently we created the first methods that are able to compare such fragments. RESULTS: We show that five low complexity regions (LCRs) in three proteins (nsp3, S and N) encoded by the SARS-CoV-2 genome are highly similar to regions from human proteome. As many as 21 predicted T-cell epitopes and 27 predicted B-cell epitopes overlap with the five SARS-CoV-2 LCRs similar to human proteins. Interestingly, replication proteins encoded in the central part of viral RNA are devoid of LCRs. CONCLUSIONS: Similarity of SARS-CoV-2 LCRs to human proteins may have implications on the ability of the virus to counteract immune defenses. The vaccine targeted LCRs may potentially be ineffective or alternatively lead to autoimmune diseases development. These findings are crucial to the process of selection of new epitopes for drugs or vaccines which should omit such regions.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

The Discovery of a Novel Antimetastatic Bcl3 Inhibitor.

The development of antimetastatic drugs is an urgent healthcare priority for patients with cancer, because metastasis is thought to account for around 90% of cancer deaths. Current antimetastatic treatment options are limited and often associated with poor long-term survival and systemic toxicities. Bcl3, a facilitator protein of the NF-κB family, is associated with poor prognosis in a range of tumor types. Bcl3 has been directly implicated in the metastasis of tumor cells, yet is well tolerated when constitutively deleted in murine models, making it a promising therapeutic target. Here, we describe the identification and characterization of the first small-molecule Bcl3 inhibitor, by using a virtual drug design and screening approach against a computational model of the Bcl3-NF-kB1(p50) protein-protein interaction. From selected virtual screening hits, one compound (JS6) showed potent intracellular Bcl3-inhibitory activity. JS6 treatment led to reductions in Bcl3-NF-kB1 binding, tumor colony formation, and cancer cell migration in vitro; and tumor stasis and antimetastatic activity in vivo, while being devoid of overt systemic toxicity. These results represent a successful application of in silico screening in the identification of protein-protein inhibitors for novel intracellular targets, and confirm Bcl3 as a potential antimetastatic target.

PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins.

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity-a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

The need for standardisation in life science research - an approach to excellence and trust.

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However  for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

Disentangling the complexity of low complexity proteins.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. SHORT ABSTRACT: There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

Tandem repeats lead to sequence assembly errors and impose multi-level challenges for genome and protein databases.

The widespread occurrence of repetitive stretches of DNA in genomes of organisms across the tree of life imposes fundamental challenges for sequencing, genome assembly, and automated annotation of genes and proteins. This multi-level problem can lead to errors in genome and protein databases that are often not recognized or acknowledged. As a consequence, end users working with sequences with repetitive regions are faced with 'ready-to-use' deposited data whose trustworthiness is difficult to determine, let alone to quantify. Here, we provide a review of the problems associated with tandem repeat sequences that originate from different stages during the sequencing-assembly-annotation-deposition workflow, and that may proliferate in public database repositories affecting all downstream analyses. As a case study, we provide examples of the Atlantic cod genome, whose sequencing and assembly were hindered by a particularly high prevalence of tandem repeats. We complement this case study with examples from other species, where mis-annotations and sequencing errors have propagated into protein databases. With this review, we aim to raise the awareness level within the community of database users, and alert scientists working in the underlying workflow of database creation that the data they omit or improperly assemble may well contain important biological information valuable to others.

Quantitative Conformational Analysis of Functionally Important Electrostatic Interactions in the Intrinsically Disordered Region of Delta Subunit of Bacterial RNA Polymerase.

Electrostatic interactions play important roles in the functional mechanisms exploited by intrinsically disordered proteins (IDPs). The atomic resolution description of long-range and local structural propensities that can both be crucial for the function of highly charged IDPs presents significant experimental challenges. Here, we investigate the conformational behavior of the δ subunit of RNA polymerase from Bacillus subtilis whose unfolded domain is highly charged, with 7 positively charged amino acids followed by 51 acidic amino acids. Using a specifically designed analytical strategy, we identify transient contacts between the two regions using a combination of NMR paramagnetic relaxation enhancements, residual dipolar couplings (RDCs), chemical shifts, and small-angle scattering. This strategy allows the resolution of long-range and local ensemble averaged structural contributions to the experimental RDCs, and reveals that the negatively charged segment folds back onto the positively charged strand, compacting the conformational sampling of the protein while remaining highly flexible in solution. Mutation of the positively charged region abrogates the long-range contact, leaving the disordered domain in an extended conformation, possibly due to local repulsion of like-charges along the chain. Remarkably, in vitro studies show that this mutation also has a significant effect on transcription activity, and results in diminished cell fitness of the mutated bacteria in vivo. This study highlights the importance of accurately describing electrostatic interactions for understanding the functional mechanisms of IDPs.

Proteome profiles of different types of thyroid cancers.

Proteomics profiling of tissue specimens representative for major types of thyroid cancers: papillary (classical and follicular variant), follicular, anaplastic and medullary, as well as benign follicular adenoma, was performed using shotgun LC-MS/MS approaches. A combination of Orbitrap and MALDI-TOF approach allowed to identify protein products of 3700 unique genes and revealed large differences between medullary, anaplastic and epithelium-derived differentiated cancers (papillary and follicular). Proteins characteristic for medullary and anaplastic cancers included factors associated with neuroendocrine functions and factors typically associated with advanced malignancies, respectively. Proteomes of different types of epithelium-derived differentiated cancers and follicular adenoma were compared using multi-enzyme LC-MS/MS approach, which revealed products of 4800 unique genes. A comparable overall similarity of follicular cancers to both variants of papillary cancers was found. Moreover, follicular adenoma showed higher overall similarity to follicular cancer than to either variant of papillary cancer. Proteins discriminating differentiated thyroid neoplasms included factors associated with lipid and hormone metabolism, regulation of gene expression and maintenance of DNA structure. Importantly, proteome data matched several features of transcriptome and metabolome profiles of thyroid cancers contributing to systems biology of this malignancy.

Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets.

BACKGROUND: High-throughput methods in molecular biology provided researchers with abundance of experimental data that need to be interpreted in order to understand the experimental results. Manual methods of functional gene/protein group interpretation are expensive and time-consuming; therefore, there is a need to develop new efficient data mining methods and bioinformatics tools that could support the expert in the process of functional analysis of experimental results. RESULTS: In this study, we propose a comprehensive framework for the induction of logical rules in the form of combinations of Gene Ontology (GO) terms for functional interpretation of gene sets. Within the framework, we present four approaches: the fully automated method of rule induction without filtering, rule induction method with filtering, expert-driven rule filtering method based on additive utility functions, and expert-driven rule induction method based on the so-called seed or expert terms - the GO terms of special interest which should be included into the description. These GO terms usually describe some processes or pathways of particular interest, which are related to the experiment that is being performed. During the rule induction and filtering processes such seed terms are used as a base on which the description is build. CONCLUSION: We compare the descriptions obtained with different algorithms of rule induction and filtering and show that a filtering step is required to reduce the number of rules in the output set so that they could be analyzed by a human expert. However, filtering may remove information from the output rule set which is potentially interesting for the expert. Therefore, in the study, we present two methods that involve interaction with the expert during the process of rule induction. Both of them are able to reduce the number of rules, but only in the case of the method based on seed terms, each of the created rule includes expert terms in combination with the other terms. Further analysis of such combinations may provide new knowledge about biological processes and their combination with other pathways related to genes described by the rules. A suite of Matlab scripts that provide the functionality of a comprehensive framework for the rule induction and filtering presented in this study is available free of charge at: http://rulego.polsl.pl/framework .

The proline-rich region of glyceraldehyde-3-phosphate dehydrogenase from human sperm may bind SH3 domains, as revealed by a bioinformatic study of low-complexity protein segments.

Glyceraldehyde-3-phosphate dehydrogenase from human sperm (GAPDHS) provides energy to the sperm flagellum, and is therefore essential for sperm motility and male fertility. This isoform is distinct from somatic GAPDH, not only in being specific for the testis but also because it contains an additional amino-terminal region that encodes a proline-rich motif that is known to bind to the fibrous sheath of the sperm tail. By conducting a large-scale sequence comparison on low-complexity sequences available in databases, we identified a strong similarity between the proline-rich motif from GAPDHS and the proline-rich sequence from Ena/vasodilator-stimulated phosphoprotein-like (EVL), which is known to bind an SH3 domain of dynamin-binding protein (DNMBP). The putative binding partners of the proline-rich GAPDHS motif include SH3 domain-binding protein 4 (SH3BP4) and the IL2-inducible T-cell kinase/tyrosine-protein kinase ITK/TSK (ITK). This result implies that GAPDHS participates in specific signal-transduction pathways. Gene Ontology category-enrichment analysis showed several functional classes shared by both proteins, of which the most interesting ones are related to signal transduction and regulation of hydrolysis. Furthermore, a mutation of one EVL proline to leucine is known to cause colorectal cancer, suggesting that mutation of homologous amino acid residue in the GAPDHS motif may be functionally deleterious.

Synthetic genistein glycosides inhibiting EGFR phosphorylation enhance the effect of radiation in HCT 116 colon cancer cells.

The need to find new EGFR inhibitors for use in combination with radiotherapy in the treatment of solid tumors has drawn our attention to compounds derived from genistein, a natural isoflavonoid. The antiproliferative potential of synthetic genistein derivatives used alone or in combination with ionizing radiation was evaluated in cancer cell lines using clonogenic assay. EGFR phosphorylation was assessed with western blotting. Genistein derivatives inhibited clonogenic growth of HCT 116 cancer cells additively or synergistically when used in combination with ionizing radiation, and decreased EGFR activation. Our preclinical evaluation of genistein-derived EGFR inhibitors suggests that these compounds are much more potent sensitizers of cells to radiation than the parent isoflavonoid, genistein and indicate that these compounds may be useful in the treatment of colon cancer with radiation therapy.

RuleGO: a logical rules-based tool for description of gene groups by means of Gene Ontology.

Genome-wide expression profiles obtained with the use of DNA microarray technology provide abundance of experimental data on biological and molecular processes. Such amount of data need to be further analyzed and interpreted in order to obtain biological conclusions on the basis of experimental results. The analysis requires a lot of experience and is usually time-consuming process. Thus, frequently various annotation databases are used to improve the whole process of analysis. Here, we present RuleGO--the web-based application that allows the user to describe gene groups on the basis of logical rules that include Gene Ontology (GO) terms in their premises. Presented application allows obtaining rules that reflect coappearance of GO-terms describing genes supported by the rules. The ontology level and number of coappearing GO-terms is adjusted in automatic manner. The user limits the space of possible solutions only. The RuleGO application is freely available at http://rulego.polsl.pl/.