Application of comprehensive molecular genetic profiling in precision cancer medicine, Hungarian experiences.

Recent developments in molecular genetic testing methods (e.g. next-generation sequencing [NGS]-panels) largely accelerated the process of finding the most appropriate targeted therapeutic intervention for cancer patients based on molecularly targetable genetic alterations. In Hungary, a centralized approval system following the recommendation of the National Molecular Tumor Board was launched for the coordination of all aspects of comprehensive genetic profiling (CGP) including patient selection and therapy reimbursement. AIM: The study aims to evaluate the clinical benefit of CGP in our Comprehensive Cancer Center Methods and patients: CGP was introduced into our routine clinical practice in 2021. An NGS-based large (> 500 genes) gene panel was used for cases where molecular genetic testing was approved by the National Molecular Tumor Board. From 2021 until August 2023 163 cases were tested. The majority of them were ECOG 0-1 patients with advanced-stage diseases, histologically rare cancer, or cancers with unknown primary tumours. RESULTS: Seventy-four cases (74 of 163, 45%) had clinically relevant genetic alterations. In 34 patients, the identified variants represented an indication for an approved therapy (approved by the Hungarian authorities, on-label indication), while in 40 cases the recommended therapy did not have an approved indication in Hungary for certain tumour types, but off-label indication could be recommended. Based on our CGP results, 24 patients (24/163; 14.7%) received targeted therapy. Treatment duration was between 1 and 60 months. In total 14 (14/163; 8.5% of the tested cases) patients had a positive clinical response (objective response or stable disease) and were treated for more than 16 weeks. INTERPRETATION: NGS-based CGP was successfully introduced in our institution and a significant number of patients benefited from comprehensive genetic tests. Our preliminary results can serve as the starting point of Drug Rediscovery Protocol (DRUP) studies.

Discovery and biocatalytic characterization of opine dehydrogenases by metagenome mining.

Enzymatic processes play an increasing role in synthetic organic chemistry which requires the access to a broad and diverse set of enzymes. Metagenome mining is a valuable and efficient way to discover novel enzymes with unique properties for biotechnological applications. Here, we report the discovery and biocatalytic characterization of six novel metagenomic opine dehydrogenases from a hot spring environment (mODHs) (EC 1.5.1.X). These enzymes catalyze the asymmetric reductive amination between an amino acid and a keto acid resulting in opines which have defined biochemical roles and represent promising building blocks for pharmaceutical applications. The newly identified enzymes exhibit unique substrate specificity and higher thermostability compared to known examples. The feature that they preferably utilize negatively charged polar amino acids is so far unprecedented for opine dehydrogenases. We have identified two spatially correlated positions in their active sites that govern this substrate specificity and demonstrated a switch of substrate preference by site-directed mutagenesis. While they still suffer from a relatively narrow substrate scope, their enhanced thermostability and the orthogonality of their substrate preference make them a valuable addition to the toolbox of enzymes for reductive aminations. Importantly, enzymatic reductive aminations with highly polar amines are very rare in the literature. Thus, the preparative-scale enzymatic production, purification, and characterization of three highly functionalized chiral secondary amines lend a special significance to our work in filling this gap. KEY POINTS: • Six new opine dehydrogenases have been discovered from a hot spring metagenome • The newly identified enzymes display a unique substrate scope • Substrate specificity is governed by two correlated active-site residues.

Gluing GAP to RAS Mutants: A New Approach to an Old Problem in Cancer Drug Development.

Mutated genes may lead to cancer development in numerous tissues. While more than 600 cancer-causing genes are known today, some of the most widespread mutations are connected to the RAS gene; RAS mutations are found in approximately 25% of all human tumors. Specifically, KRAS mutations are involved in the three most lethal cancers in the U.S., namely pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and lung adenocarcinoma. These cancers are among the most difficult to treat, and they are frequently excluded from chemotherapeutic attacks as hopeless cases. The mutated KRAS proteins have specific three-dimensional conformations, which perturb functional interaction with the GAP protein on the GAP-RAS complex surface, leading to a signaling cascade and uncontrolled cell growth. Here, we describe a gluing docking method for finding small molecules that bind to both the GAP and the mutated KRAS molecules. These small molecules glue together the GAP and the mutated KRAS molecules and may serve as new cancer drugs for the most lethal, most difficult-to-treat, carcinomas. As a proof of concept, we identify two new, drug-like small molecules with the new method; these compounds specifically inhibit the growth of the PANC-1 cell line with KRAS mutation G12D in vitro and in vivo. Importantly, the two new compounds show significantly lower IC50 and higher specificity against the G12D KRAS mutant human pancreatic cancer cell line PANC-1, as compared to the recently described selective G12D KRAS inhibitor MRTX-1133.

Is HLA type a possible cancer risk modifier in Lynch syndrome?

Lynch syndrome (LS) is the most common inherited cancer syndrome. It is inherited via a monoallelic germline variant in one of the DNA mismatch repair (MMR) genes. LS carriers have a broad 30% to 80% risk of developing various malignancies, and more precise, individual risk estimations would be of high clinical value, allowing tailored cancer prevention and surveillance. Due to MMR deficiency, LS cancers are characterized by the accumulation of frameshift mutations leading to highly immunogenic frameshift peptides (FSPs). Thus, immune surveillance is proposed to inhibit the outgrowth of MMR-deficient cell clones. Recent studies have shown that immunoediting during the evolution of MMR-deficient cancers leads to a counter-selection of highly immunogenic antigens. The immunogenicity of FSPs is dependent on the antigen presentation. One crucial factor determining antigen presentation is the HLA genotype. Hence, a LS carrier's HLA genotype plays an important role in the presentation of FSP antigens to the immune system, and may influence the likelihood of progression from precancerous lesions to cancer. To address the challenge of clarifying this possibility including diverse populations with different HLA types, we have established the INDICATE initiative (Individual cancer risk by HLA type, http://indicate-lynch.org/), an international network aiming at a systematic evaluation of the HLA genotype as a possible cancer risk modifier in LS. Here we summarize the current knowledge on the role of HLA type in cancer risk and outline future research directions to delineate possible association in the scenario of LS with genetically defined risk population and highly immunogenic tumors.

Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model.

BACKGROUND: CDK4/6 inhibitors such as ribociclib are becoming widely used targeted therapies in hormone-receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer. However, cancers can advance due to drug resistance, a problem in which tumor heterogeneity and evolution are key features. METHODS: Ribociclib-resistant HR+/HER2- CAMA-1 breast cancer cells were generated through long-term ribociclib treatment. Characterization of sensitive and resistant cells were performed using RNA sequencing and whole exome sequencing. Lentiviral labeling with different fluorescent proteins enabled us to track the proliferation of sensitive and resistant cells under different treatments in a heterogeneous, 3D spheroid coculture system using imaging microscopy and flow cytometry. RESULTS: Transcriptional profiling of sensitive and resistant cells revealed the downregulation of the G2/M checkpoint in the resistant cells. Exploiting this acquired vulnerability; resistant cells exhibited collateral sensitivity for the Wee-1 inhibitor, adavosertib (AZD1775). The combination of ribociclib and adavosertib achieved additional antiproliferative effect exclusively in the cocultures compared to monocultures, while decreasing the selection for resistant cells. CONCLUSIONS: Our results suggest that optimal antiproliferative effects in heterogeneous cancers can be achieved via an integrative therapeutic approach targeting sensitive and resistant cancer cell populations within a tumor, respectively.

MetaHMM: A webserver for identifying novel genes with specified functions in metagenomic samples.

The fast and affordable sequencing of large clinical and environmental metagenomic datasets opens up new horizons in medical and biotechnological applications. It is believed that today we have described only about 1% of the microorganisms on the Earth, therefore, metagenomic analysis mostly deals with unknown species in the samples. Microbial communities in extreme environments may contain genes with high biotechnological potential, and clinical metagenomes, related to diseases, may uncover still unknown pathogens and pathological mechanisms in known diseases. While the species-level identification and description of the taxa in the samples do not seem to be possible today, we can search for novel genes with known functions in these samples, using numerous techniques, including artificial intelligence tools, like the hidden Markov models (HMMs). Here we describe a simple-to-use webserver, the MetaHMM, which is capable of homology-based automatic model-building for the genes to be searched for, and it also finds the closest matches in the metagenome. The webserver uses already highly successful building blocks: it performs multiple alignments by applying Clustal Omega, builds a hidden Markov model with HMMER components of hmmbuild and uses hmmsearch for finding similar sequences to the specified model in the metagenomes. The webserver is publicly available at https://metahmm.pitgroup.org.

SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification.

Summary: Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here, we introduce SECLAF, a webserver that uses deep neural networks for hierarchical biological sequence classification. By applying SECLAF for residue-sequences, we have reported [Methods (2018), https://doi.org/10.1016/j.ymeth.2017.06.034] the most accurate multi-label protein classifier to date (UniProt-into 698 classes-AUC 99.99%; Gene Ontology-into 983 classes-AUC 99.45%). Our framework SECLAF can be applied for other sequence classification tasks, as we describe in the present contribution. Availability and implementation: The program SECLAF is implemented in Python, and is available for download, with example datasets at the website https://pitgroup.org/seclaf/. For Gene Ontology and UniProt based classifications a webserver is also available at the address above.

Near perfect protein multi-label classification with deep neural networks.

Biological sequences can be considered as data items of high-, non-fixed dimensions, corresponding to the length of those sequences. The comparison and the classification of biological sequences in their relations to large databases are important areas of research today. Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here we present two new ANNs with multi-label classification ability, showing impressive accuracy when classifying protein sequences into 698 UniProt families (AUC=99.99%) and 983 Gene Ontology classes (AUC=99.45%).

[Evolution of molecular genetic methods in the clinical diagnosis of hereditary endocrine tumour syndromes]. / Molekuláris genetikai vizsgálatok az örökletes endokrinológiai tumor szindrómák klinikai diagnosztikájában.

The common features of hereditary endocrine tumour syndromes or multiple endocrine neoplasias (MEN) are the association of various tumours of different endocrine organs in one patient or within the same family. Different types can be distinguished from among which type 1 and type 2 are the most common. The mode of inheritance is autosomal dominant, meaning that there is a 50% chance to inherit the pathogenic alteration. The pathogenic variants of genes responsible for MEN syndromes have also been identified in sporadic endocrine tumours and many cases initially referred to as sporadic have been later categorized as familiar based on genetic analysis. The main role of the molecular genetic analysis in these syndromes is to identify the pathogenic variant, then, after appropriate genetic counseling, to perform the genetic screening of first-degree relatives. Following molecular genetic analysis, the state-of-the-art clinical follow-up of the clinically healthy mutation carriers may decrease or even prevent the morbidity and mortality. Due to technological developments in recent years, the molecular genetic analysis of hereditary tumour syndromes has also been changed. Using next generation based sequencing methods in routine clinical diagnostics, the number of pathogenic genes in endocrine tumours has also increased. The present review focuses on the genetic background of hereditary endocrine tumour syndromes and the recently used molecular biological methods will also be presented. Orv Hetil. 2018; 159(7): 285-292.

Significant differences found in short nucleotide sequences of human intestinal metagenomes of Northern-European and Chinese Origin.

BACKGROUND: Metagenomic analysis of environmental and clinical samples is gaining considerable importance in today's literature. Changes in the composition of the intestinal microbial communities, relative to the healthy control, are reported in numerous conditions. METHODS: We have carefully analyzed the frequencies of the short nucleotide sequences in the metagenomes of two different enterotypes; namely of Chinese and European origins. RESULTS: We have identified 255 nucleotide sequences of length up to 9, such that their frequencies significantly differ in the two enterotypes examined. CONCLUSIONS: We have demonstrated that short nucleotide sequences are capable of differentiating enterotypes, and not only metagenomes, originating from healthy and diseased subjects. GENERAL SIGNIFICANCE: Our results may imply that the frequency-differences of certain short nucleotides have diagnostical value if properly applied for different clusters of metagenomes. "This article is part of a Special Issue entitled "Science for Life" Guest Editor: Dr. Austen Angell, Dr. Salvatore Magazù and Dr. Federica Migliardo".

The "Giant Virus Finder" discovers an abundance of giant viruses in the Antarctic dry valleys.

Mimivirus was identified in 2003 from a biofilm of an industrial water-cooling tower in England. Later, numerous new giant viruses were found in oceans and freshwater habitats, some of them having 2,500 genes. We have demonstrated their likely presence in four soil samples taken from the Kutch Desert (Gujarat, India). Here we describe a bioinformatics work-flow, called the "Giant Virus Finder" that is capable of discovering the likely presence of the genomes of giant viruses in metagenomic shotgun-sequenced datasets. The new workflow is applied to numerous hot and cold desert soil samples as well as some tundra- and forest soils. We show that most of these samples contain giant viruses, especially in the Antarctic dry valleys. The results imply that giant viruses could be frequent not only in aqueous habitats, but in a wide spectrum of soils on our planet.

Nucleotide 9-mers characterize the type II diabetic gut metagenome.

Discoveries of new biomarkers for frequently occurring diseases are of special importance in today's medicine. While fully developed type II diabetes (T2D) can be detected easily, the early identification of high risk individuals is an area of interest in T2D, too. Metagenomic analysis of the human bacterial flora has shown subtle changes in diabetic patients, but no specific microbes are known to cause or promote the disease. Moderate changes were also detected in the microbial gene composition of the metagenomes of diabetic patients, but again, no specific gene was found that is present in disease-related and missing in healthy metagenome. However, these fine differences in microbial taxon- and gene composition are difficult to apply as quantitative biomarkers for diagnosing or predicting type II diabetes. In the present work we report some nucleotide 9-mers with significantly differing frequencies in diabetic and healthy intestinal flora. To our knowledge, it is the first time such short DNA fragments have been associated with T2D. The automated, quantitative analysis of the frequencies of short nucleotide sequences seems to be more feasible than accurate phylogenetic and functional analysis, and thus it might be a promising direction of diagnostic research.

Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression.

BACKGROUND: Previously, drug-based synchronization procedures were used for characterizing the cell cycle dependent transcriptional program. However, these synchronization methods result in growth imbalance and alteration of the cell cycle machinery. DNA content-based fluorescence activated cell sorting (FACS) is able to sort the different cell cycle phases without perturbing the cell cycle. MiRNAs are key transcriptional regulators of the cell cycle, however, their expression dynamics during cell cycle has not been explored. METHODS: Following an optimized FACS, a complex initiative of high throughput platforms (microarray, Taqman Low Density Array, small RNA sequencing) were performed to study gene and miRNA expression profiles of cell cycle sorted human cells originating from different tissues. Validation of high throughput data was performed using quantitative real time PCR. Protein expression was detected by Western blot. Complex statistics and pathway analysis were also applied. RESULTS: Beyond confirming the previously described cell cycle transcriptional program, cell cycle dependently expressed genes showed a higher expression independently from the cell cycle phase and a lower amplitude of dynamic changes in cancer cells as compared to untransformed fibroblasts. Contrary to mRNA changes, miRNA expression was stable throughout the cell cycle. CONCLUSIONS: Cell cycle sorting is a synchronization-free method for the proper analysis of cell cycle dynamics. Altered dynamic expression of universal cell cycle genes in cancer cells reflects the transformed cell cycle machinery. Stable miRNA expression during cell cycle progression may suggest that dynamical miRNA-dependent regulation may be of less importance in short term regulations during the cell cycle.

Giant viruses of the Kutch Desert.

The Kutch Desert (Great Rann of Kutch, Gujarat, India) is a unique ecosystem: in the larger part of the year it is a hot, salty desert that is flooded regularly in the Indian monsoon season. In the dry season, the crystallized salt deposits form the "white desert" in large regions. The first metagenomic analysis of the soil samples of Kutch was published in 2013, and the data were deposited in the NCBI Sequence Read Archive. At the same time, the sequences were analyzed phylogenetically for prokaryotes, especially for bacteria. In the present work, we identified DNA sequences of recently discovered giant viruses in the soil samples from the Kutch Desert. Since most giant viruses have been discovered in biofilms in industrial cooling towers, ocean water, and freshwater ponds, we were surprised to find their DNA sequences in soil samples from a seasonally very hot and arid, salty environment.

Evaluating the Quantitative Capabilities of Metagenomic Analysis Software.

DNA sequencing technologies are applied widely and frequently today to describe metagenomes, i.e., microbial communities in environmental or clinical samples, without the need for culturing them. These technologies usually return short (100-300 base-pairs long) DNA reads, and these reads are processed by metagenomic analysis software that assign phylogenetic composition-information to the dataset. Here we evaluate three metagenomic analysis software (AmphoraNet--a webserver implementation of AMPHORA2--, MG-RAST, and MEGAN5) for their capabilities of assigning quantitative phylogenetic information for the data, describing the frequency of appearance of the microorganisms of the same taxa in the sample. The difficulties of the task arise from the fact that longer genomes produce more reads from the same organism than shorter genomes, and some software assign higher frequencies to species with longer genomes than to those with shorter ones. This phenomenon is called the "genome length bias." Dozens of complex artificial metagenome benchmarks can be found in the literature. Because of the complexity of those benchmarks, it is usually difficult to judge the resistance of a metagenomic software to this "genome length bias." Therefore, we have made a simple benchmark for the evaluation of the "taxon-counting" in a metagenomic sample: we have taken the same number of copies of three full bacterial genomes of different lengths, break them up randomly to short reads of average length of 150 bp, and mixed the reads, creating our simple benchmark. Because of its simplicity, the benchmark is not supposed to serve as a mock metagenome, but if a software fails on that simple task, it will surely fail on most real metagenomes. We applied three software for the benchmark. The ideal quantitative solution would assign the same proportion to the three bacterial taxa. We have found that AMPHORA2/AmphoraNet gave the most accurate results and the other two software were under-performers: they counted quite reliably each short read to their respective taxon, producing the typical genome length bias. The benchmark dataset is available at http://pitgroup.org/static/3RandomGenome-100kavg150bps.fna.

On dimension reduction of clustering results in structural bioinformatics.

OPTICS is a density-based clustering algorithm that performs well in a wide variety of applications. For a set of input objects, the algorithm creates a reachability plot that can either be used to produce cluster membership assignments, or interpreted itself as an expressive two-dimensional representation of the clustering structure of the input set, even if the input set is embedded in higher dimensions. The focus of this work is a visualization method that can be applied for comparing two, independent hierarchical clusterings by assigning colors to all entries of the input database. We give two applications related to macromolecular structural properties: the first is a sequence-based clustering of the SwissProt database that is evaluated using NCBI taxonomy identifiers, and the second application involves clustering locations of specific atoms in the serine protease enzyme family-and the clusters are evaluated using SCOP structural classifications.

Visual analysis of the quantitative composition of metagenomic communities: the AmphoraVizu webserver.

Low-cost DNA sequencing methods have given rise to an enormous development of metagenomics in the past few years. One basic--and difficult--task is the phylogenetic annotation of the metagenomic samples studied. The difficulty comes from the fact that the typical environmental sample contains hundreds of unknown and still uncharacterized microorganisms. There are several possible methods to assign at least partial phylogenetic information to these uncharacterized data. Originally, the 16S ribosomal RNA was used as phylogenetic marker, then genome sequence alignments and similarity measures between the unknown genome and the reference genomes were applied (e.g., in the MEGAN software), and more recently, phylogeny-based methods applying suitable sets of marker genes were suggested (AMPHORA, AMPHORA2, and the webserver implementation AmphoraNet). Here, we present a visual analysis tool that is capable of demonstrating the quantitative relations gained from the output of the AMPHORA2 program or the easy-to-use AmphoraNet webserver. Our web-based tool, the AmphoraVizu webserver, makes the phylogenetic distribution of the metagenomic sample clearly visible by using the native output format of AMPHORA2 or AmphoraNet. The user may set the phylogenetic resolution (i.e., superkingdom, phylum, class, order, family, genus, and species) along with the chart type and will receive the distribution data detailed for all relevant marker genes in the sample. For publication quality results, the chart labels can be customized by the user. The visualization webserver is available at the address http://amphoravizu.pitgroup.org. The AmphoraNet webserver is available at http://amphoranet.pitgroup.org. The open-source version of the AmphoraVizu program is available for download at http://pitgroup.org/apps/amphoravizu/AmphoraVizu.pl.

Catalytic mechanism of α-phosphate attack in dUTPase is revealed by X-ray crystallographic snapshots of distinct intermediates, 31P-NMR spectroscopy and reaction path modelling.

Enzymatic synthesis and hydrolysis of nucleoside phosphate compounds play a key role in various biological pathways, like signal transduction, DNA synthesis and metabolism. Although these processes have been studied extensively, numerous key issues regarding the chemical pathway and atomic movements remain open for many enzymatic reactions. Here, using the Mason-Pfizer monkey retrovirus dUTPase, we study the dUTPase-catalyzed hydrolysis of dUTP, an incorrect DNA building block, to elaborate the mechanistic details at high resolution. Combining mass spectrometry analysis of the dUTPase-catalyzed reaction carried out in and quantum mechanics/molecular mechanics (QM/MM) simulation, we show that the nucleophilic attack occurs at the α-phosphate site. Phosphorus-31 NMR spectroscopy ((31)P-NMR) analysis confirms the site of attack and shows the capability of dUTPase to cleave the dUTP analogue α,ß-imido-dUTP, containing the imido linkage usually regarded to be non-hydrolyzable. We present numerous X-ray crystal structures of distinct dUTPase and nucleoside phosphate complexes, which report on the progress of the chemical reaction along the reaction coordinate. The presently used combination of diverse structural methods reveals details of the nucleophilic attack and identifies a novel enzyme-product complex structure.

Genetic variants of the HSD11B1 gene promoter may be protective against polycystic ovary syndrome.

The HSD11B1 gene encodes the type 1 isoform of the 11-ß-hydroxysteroid dehydrogenase that is responsible for the regeneration of glucocorticoids from hormonally-inactive metabolites into active forms in a tissue-specific manner. Altered activity of the enzyme, and certain genetic variants of the HSD11B1 gene, has been associated with various metabolic morbidities. In this study, our aim was to systematically test the potential role of the HSD11B1's single nucleotide polymorphisms (SNPs) in polycystic ovary syndrome (PCOS). Nine HSD11B1 SNPs were selected and genotyped using Taqman SNP assays on real-time PCR in a group of PCOS patients (n = 58) and in age-matched healthy controls (n = 64). Genotype-phenotype correlations were determined and haplotype analysis was performed. An in silico prediction for potential transcription factor binding sites was also performed. Of the 5 promoter SNPs, 3 (rs760951; rs4844880; rs3753519) were less frequent in the PCOS group compared to healthy controls. SNPs rs4844880 and rs3753519 were in a complete linkage and the mutant haplotype (AA) was less frequent in the PCOS group. No association between HSD11B1 variants and clinical, pathological findings was observed in patients, but in healthy women the rs4844880 and the AA haplotype were associated with higher levels of homeostasis model assessment of beta cell function. The polymorphic form of the rs4844880 was predicted to bind Pbx-1. Promoter SNPs of the HSD11B1 gene might exert a potential genetic protective role against the development of PCOS, possibly via their beneficial effect on carbohydrate homeostasis due to facilitation of insulin efflux from pancreatic beta-cells.

Robust circuitry-based scores of structural importance of human brain areas.

We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.