The evolutionary recruitment of domains was highly modular, hierarchical and ongoing. Domain rearrangements elicited non-random and scale-free network structure. Comparative analyses of preferential attachment, randomness and modularity showed yin-and-yang complementary transition and biphasic patterns along the structural chronology. Remarkably, the evolving networks highlighted a central evolutionary role of cofactor-supporting structures of non-ribosomal peptide synthesis pathways, likely crucial to the early development of the genetic code. Some highly modular domains featured dual response regulation in two-component signal transduction systems with DNA-binding activity linked to transcriptional regulation of responses to environmental change. Interestingly, hub domains across the evolving networks shared the historical role of DNA binding and editing, an ancient protein function in molecular evolution. Our investigation unfolds historical source-sink patterns of evolutionary recruitment that further our understanding of protein architectures and functions.Domains are the structural, functional and evolutionary units of proteins. They combine to form multidomain proteins. The evolutionary history of this molecular combinatorics has been studied with phylogenomic methods. Here, we construct networks of domain organization and explore their evolution. A time series of networks revealed two ancient waves of structural novelty arising from ancient ‘ Protein domains are structural and functional units of evolution that make up proteins5, sometimes in unusually complex arrangements7. They fold into compact 3-dimensional (3D) atomic structures that arrange alpha-helical and beta-sheet structure elements into tightly packed conformations of the polypeptide chain8. The Structural Classification of Proteins (SCOP)9 and its extended version SCOPe10 are popular taxonomy gold standards of domain structure. SCOP definitions can be used to scan genome sequences for motifs of domains and study how they combine in proteins6. In SCOP, the structure of domains exhibiting similar 3D arrangements of secondary structures and thus identical topologies have been classified as folds (F)9. Within folds, protein domains whose structure and functional features indicate a common evolutionary origin are further grouped into fold superfamilies (FSF). These FSFs sometimes hold multiple evolutionarily related families, which unify domains with pairwise amino acid identities of more than 30%  of linear sequence analysis. For example, SCOP uses HMMs of structural recognition to recurrently enrich the database16 in a framework that increases alignment-quality and stability of family and superfamily relationships. A similar framework drives the Pfam database but focuses exclusively on sequence information. One difficulty is that not all domains fold into discrete structural entities within the space of possible folds17. Some popular domains overlap within a continuum. This ‘gregariousness’ complicates domain classification, demanding the exploration of super-secondary structural motifs as candidate lower-level units of structure, function and evolution18.The biological functions of genes manifest through the proteins or functional RNA molecules they encode. In evolution, novel functions appear when genes produce new genes by duplication, mutation, recombination, fusion and fission, or when genes are generated de novo. Research has attempted to quantitatively describe the origins of these processes of molecular diversification and how they increase molecular complexity over the course of evolution, for instance through pathways of protein domain organization7. More than two-thirds of protein sequences are longer than an average domain length, a vast majority of which are multidomain proteins19. A study of protein structures in 749 genomes showed that the lengths of orthologous protein families in Eukarya were almost double the lengths found in Bacteria and Archaea20. This variance among lengths results from shorter prokaryotic nondomain sequences that link domains to each other in proteins and have evolved reductively in prokaryotes but not in eukaryotes. The arrangement of domains along the sequence of multimeric proteins is referred to as ‘domain organization.’ Both the structure and organization of domains, which have been collectively termed protein domain ‘architecture’, are considered far more evolutionarily conserved than protein sequence23. In addition, some domain combinations make up functional units that recur in different protein contexts24. They have been termed supradomains  of domain building blocks depend on both domain size and abundance. Smaller domains are more likely to be used in multidomain proteins and are therefore more mobile28, an observation supported by a Menzerath-Altmann’s law of domain organization driven by an economy of scale29. Similarly, highly abundant domains appear more versatile, prompting abundance-based normalization of domain versatility measurements when studying intrinsic combinatorial properties and variation across lineages and biological functions32.Comparative genomic approaches allow to study the modular landscape of domain organization. For example, the evolutionary placement of domains in multiple architectural contexts can be quantified by counting distinct neighbors23. Furthermore, while structure is conserved over longer evolutionary timescales, a general metric for global pairwise comparison of structures does not yet exist33. Thus, the systematic classification of protein structure has been unable to unify the widely divergent folded structures at any level of abstraction , likely because different neighborhoods in protein sequence space contain different structures and functions35. Construction of a ‘periodic table’ of idealized structural representations of folds36 has not alleviated this difficulty due to an absence of rules of structural transformation that would explain the comparative framework. Numerous efforts to dissect the evolution of domain architectures have recently been reviewed37. To overcome limitations and produce global evolutionary views of the protein world, the focus shifted from molecules to proteomes. Trees of proteomes were first reconstructed from a proteomic census of structural domains (beginning with ref.38), and were later used to trace character-state changes along their branches to establish possible domain origins40. This approach, however, was restricted to domain structures and architectures appearing after the common ancestor of the proteomes surveyed in the trees. A much more effective way to create truly global chronologies of the protein world was the reconstruction of phylogenomic trees of domain organization (beginning with ref.41). These phylogenies take advantage of powerful serial homologies defined by the proteomic abundance of domains42 or architectures43 defined at F and FSF levels. Phylogenomic trees of domain structures helped uncover the natural history of biocatalysis by tracing chemical mechanisms in enzymatic reactions44, analyze the optimization and increase of protein folding speed derived from a flexibility-correlated factor known as contact order (the average relative distance of amino acid contacts in the tertiary structure of proteins)45, and study the history of an ‘elementary functionome’ with a bipartite network of elementary functional loop sequences and structural domains of proteins46. This last study revealed two initial waves of functional innovation involving founder ‘p-loop’ and ‘winged helix’ domain structures, and the emergence of hierarchical modularity and power law behavior in network evolution. Phylogenomic trees of domain architectures and their associated chronologies of molecular accretion showed that architectural diversification evolved through gradual accumulation of domains (singly occurring domains), domain pairs (two different domains), multidomains  and domain repeats (domains of one type that are repeated)7. The diversification began with a few single-domain architectures earlier in the timeline, followed by an increasing rate of accretion that culminated in a massive “big bang” of domain organization. The accumulation of architectures continued to date but with a decreasing rate46.In order to retrace past events in architectural evolution, statements of history (phylogenies) proposed directly from genomic data must be used to build chronologies of first evolutionary appearance of domains and domain architectures. Unfortunately, protein sequence has limited power in deep retrodictive exploration6 of time-varying (evolving) network structure. The chronology was calibrated with a molecular clock of protein structures, which transforms times of origin of domain architectures into geological timescales of billions of years (Gy)47. Five distinct composition- and topology-based ‘operative’ criteria of connectivity defined nodes and links of the evolving networks. This strategy identified connectivity distributions in a series of 169 growing networks, hubs of evolutionary recruitment acting as donors and acceptors, and structural adaptations of evolving networks to modular, random and scale-free properties. In particular, we discover a pattern of connectivity driven by fusions and fissions, respectively, with densely linked older and younger architectures from the evolutionary timeline sandwiching a period of sparse connectivity. This supports a biphasic or hourglass pattern previously observed in protein evolution48 and follows a model of module emergence49. We thus reveal remarkable patterns of emergence of hierarchy, modularity and structural cooption in evolving networks.Here, we explore the evolving interactome of protein domain organization. We generate a chronology that captures the historical development of domain and multidomain interactions with a graph theoretical approachconcise classification strings (ccs) describing SCOP domain constituents  of the molecular whole; (2) pairwise occurrence describes appearance of parts in sets of two; (3) adjacency refers to their geometrical or spatial arrangement (topology); and (4) splicing refers to the rearrangement of parts by operations of joining and excision that decompose structures   compositr whole;  pairwiseSect. 1 of Supplementary Text. Networks showcased time directionality, connectivity distributions, and network layouts:Time Directionality Mapping ages onto networks helped follow their evolutionary growth, as nodes and links accumulated over time since the origin of proteins to the present. The timeline of networks imposed a time directionality on network links, making them arcs (directed edges with arrows pointing from older to younger nodes) of directed graphs  nodes. Furthermore, outdegree describes the number of outward links and indegree the number of inward links from a node. As the timeline progresses, older nodes gain higher outdegrees as compared to the higher indegrees of recent nodes  tools53. Borrowing the DES rationale, we modeled the evolution of directed networks of domain organization with time flowing from one event to another as discrete evolutionary ‘time steps’, typical of a step function. The progression of events was visualized with two types of layouts, a vertical representation we coined ‘waterfall’ layout that had nodes arranged top-down by age and a concentric ‘radial’ representation of growing networks that unfolded time-events of protein evolution from center to periphery  of individual architectures onto the nodes of networks built using these five operative criteria . Networks grew in time and became increasingly complicated tangles, massively expanding after a “big bang” of domain combinations during the organismal diversification epoch. Movies described the evolutionary growth of these networks  subnetwork visualization of the SPX network  and hence was fully developed relatively recently in evolution. The ‘p-loop’ and ‘winged helix’ waves embedded the major gateways of enzymatic recruitment we previously reported for metabolism54. The first gateway was mediated by the c.37 P-loop hydrolase fold and originated in the energy interconversion pathways of the purine metabolism subnetwork. The second pathway was mediated by the a.4 winged helix fold and originated in the biosynthesis of cofactors and the metabolic subnetwork of porphyrin and chlorophyll57. The congruent realization of these evolutionary patterns with data sources of different types is remarkable . The first donor and acceptor pair occurred in the pairwise PX and SPX networks ~ 3.12 Gya (nd = 0.179), ~ 0.42 Gy later (Δnd = 0.11). The pairing event involved domains c.37.1 and d.14.1. The first adjacent donor and acceptor pair of the adjacency-based PAX and SPAX networks appeared ~ 2.90 Gya (nd = 0.237), ~ 0.22 Gy later (Δnd = 0.06). The adjacently paired nodes were domains c.37.1 and c.23.16. These observations highlight a remarkable tendency of domain organization to gradually but recurrently constrain pairwise occurrences in multidomain proteins. The evolutionary history of donors and acceptors of domain organization is hence associated with a highly optimized process of cooption. To explore this combinatorics, we first dissected the network connectivity with bar plots that describe the chronological accumulation of links along the evolutionary timeline . However, the pairwise cooption events of the SPX domain network, e.g., doubled in number and reached relatively comparable levels in evolution only after delays of ~ 0.6 Gy (Δnd = 0.15) and ~ 2.1 Gya (nd = 0.75), respectively. Moreover, the number of cooption events yielding sink domains in SPX almost tripled by the beginning of the organismal diversification epoch. In contrast, the number of CX sinks reached that level only halfway along that evolutionary epoch. These divergent patterns indicate a frustrated dynamics of network growth. The early adoption of composability of domains and supradomains in multidomains seems to have preceded the pairwise cooption of domains in protein history, leading to the numerous recruitment pathways of the modern protein world. A discussion on the source-sink relationships impacted by domain fusion and fission processes can be found in Sect. 3 of Supplementary Text.The networks of domains (SPX and SPAX) elicited 161 unique time-events along the evolutionary timeline, out of a total 169 events expected for networks of domains, supradomains and domain combinations   catalytic domains and NRPS modules. NRPS even preceded the emergence of the ribosome, acting as scaffold for nucleic acids and the modern translation function. In particular, the PX and PAX networks highlight the central evolutionary role of these novel emerging cofactor structures in the NRPS pathways. Thus, our findings made explicit that our connectivity criteria of generating networks of domain organization were at the cornerstone of the early development of genetic code and supported the evolutionary model of early biochemistry based on phylogenomic information and network structure.The most notable donor hubs for all networks types were the carrier protein domains e.23.1, a.28.1 and c.69.1, which are involved in Non-Ribosomal Peptide Synthesis (NRPS), whether directly or indirectly through other pathways (Table nd < 0.977) were the most prominent acceptor hubs (Table th percentile in other networks) were Nucleotide cyclase (d.58.29), Spermadhesin, CUB domain (b.23.1), and Fibronectin type III (b.1.2) (nd = 0.723–0.809). See Sect. 4 of Supplementary Text for additional donor/acceptor hub information, and Sect. 5 for cooption events occurring during the ‘big bang’ of domain organization.Domains c.30.1, b.1.1, d.142.1 and g.3.11  of nodes connecting with neighboring k nodes (i.e. the ratio of nodes with k links) decays as a power law, P(k) ~ k–γ, with γ defined as the exponent of power law decay. The frequency distributions of node-connectivity in biomolecular networks have γ typically ranging 2.1–2.463. Thus, scale-free properties drive degree distributions with heavy tails, where very few nodes have high degree values.Genomic-centric processes such as duplication, recombination, fusion and fission shape patterns of molecular complexityp value > 0.05). While power law behavior generally declined as the networks evolved , it somewhat sustained after the ‘big bang’ but only in CX and not in the pairwise networks . A log linear regression model of CX produced the highest absolute value for γ of 3.81 among the five networks, which was achieved early along the evolutionary timeline (nd ~ 0.25). This value of γ was much higher than values reported for metabolic networks (γ ~ 2.2)60. Remarkably, the γ was maintained at ~ 3 before and after the ‘big bang’, while remaining at ~ 2 until modern times with a minimum value of 1.7. The other four networks generated primarily with the pairwise criterion apparently deviated from the power-law behavior, especially after the ‘big bang’. For instance, the γ of PX and PAX peaked at 2.4 (nd ~ 0.35) and 3.2 (nd ~ 0.38), respectively, slightly later than CX. We also noted a transition in γ from 2.1 in PX and 2.7 in PAX prior to the ‘big bang’ to 1.6 in both after the big bang, plateauing at ~ 1 until the present. In the SPX and SPAX networks, γ reached a peak even later in time than PX and PAX with values of 2.8 (nd ~ 0.54) and 3.4 (nd ~ 0.66), respectively. These values transitioned from 2.4 in SPX and 2.8 in SPAX from before the big bang to 1.6 and 1.7 after the big bang, respectively, plateauing at ~ 1 in both the networks. As expected, the average γ based on less representative outdegree of each of the five networks remained low (1 ± 0.05).Our statistical analyses of the featured indegree distributions along the timeline of growing networks uncovered interesting patterns of power law dynamics Fig. . The scand ~ 0.37 and ~ 0.67. Moreover, the scale-free tendency of adjacency networks seemed comparatively higher than that of networks lacking the adjacency restriction. For instance, the average values of γ for the PAX and SPAX networks  were relatively higher than those for the corresponding parent PX and SPX networks . This suggests that proximity in amino acid sequence plays a major role in rendering the power-law behavior of evolving networks of domain organization. Overall, the average γ of CX (2.56 ± 0.06) remained the highest along the evolutionary timeline, indicating that composition strongly elicits the preferential attachment property. A complementary transition from random to non-random behavior  in ancient networks (nd ~ 0.3) implies deviation from randomness as biological networks evolve. Remarkably, this transition event coincides with the origin of a processive ribosome. Such biphasic patterns are common in biology and have explained the emergence of biological modules49 in metabolic networks of Escherichia coli50, networks of elementary functionomes46, and molecular ancestry networks of enzymes64. Section 6 of Supplementary Text further discusses scale-freeness and randomness of networks.We noticed biphasic patterns when -γ was plotted over network connectivity, with two minima at 65. Network modularity usually offsets the power-law behavior of biological networks by distributing node degrees within communities68. However, both scale-free properties and modular structure may co-exist in a network when modules coalesce hierarchically60. A primary index of modularity is the average clustering coefficient (C), defined as a node-averaged ratio of triangles (graph cycles of length 3) to triads (the connected graph triples) of the network, not taking into account the weights or direction of the node-links70  with a value of 0.09 ± 0.009. The composition CX network had a relatively higher C of 0.2 ± 0.009. However, the non-adjacency pairwise PX and SPX networks had the highest C values of 0.5 ± 0.02 and 0.32 ± 0.014, respectively. These values were still lower than those reported for metabolic networks (C =  ~ 0.6)71. Hence, the networks supposedly evolved more random smaller modules connected by various inter-modular links, rather than stronger larger modules with few interconnections. Also, the evolution of modular structure appeared better consolidated by pairwise (PX and SPX) and to a lesser degree composability (CX) constraints rather than by adjacency (PAX and SPAX). Comparing patterns of modularity of evolving networks to those of randomness  indicated complementary transitions between the two behaviors over the evolutionary timeline  that establish links preferentially within themselves and do so sparsely with the restC against network size N and evolutionary age nd. For typical scale-free models, C declines sharply with increasing N (C ~ N-coefficient), while the coefficients are as high as 0.7572. Instead, highly modular networks are typically independent of N60. In our networks, C regressed by N with very low coefficients . In contrast, the regression of C with age (C ~ nd-coefficient) produced significantly higher coefficients   networks that were used as control showed a C of zero. Our data strongly suggests the existence of a highly modular structure that is independent of network growth but is strongly constrained by history, especially when considering the pairwise interactions of the PX network. The rise of the modularity index with emerging power-law degree distribution during certain periods of network evolution indicated a parallel formation of complex hierarchical module clusters with scale-free properties, not distinct from those present in metabolic networks60. Our networks of domain organization showed a slight lag between an onset of scale-free organization (measured with KS fit and γ indegree statistics) and a delayed emergence of modular behavior (measured with C), occurring during early protein evolution. This was followed by intermittent periods of hierarchical modularity spanning across the middle of the evolutionary timeline. Remarkably, the evolving networks showed again a prominent biphasic pattern of hierarchical modularity involving two peaks of modularity (higher statistic C) coinciding with increased power-law behavior , at nd ~ 0.37 and nd ~ 0.67, respectively  . The tree was generated using maximum parsimony as the optimality criterion in PAUP* following the parsimony ratchet search strategy described in ref.7. Data matrix and tree files are provided as Supplementary Files https://github.com/gcalab/SciRep. The phylogeny represents reconstruction of the “natural history” of proteins that is supported by a model of protein structural growth75 and is carefully indexed with various evolutionary epochs of the protein world7.We explore the evolution of networks describing how structural domains combine and split to form single domain and multidomain proteins, i.e. the domain organization of proteins. The definition of protein domain structures followed the FSF level of SCOP version 1.75nd) values, which were derived directly from the rooted phylogenomic tree of protein domain organization7. nd values describe relative ages  of first appearances of 6,162 domains and domain combinations (multidomains) defined at SCOP FSF level  used to calculate geological age in Gy through calibration points of FSF domains associated with microfossil, fossil and biogeochemical evidence, biomarkers, and first-appearance of clade-specific domains47. The RSCB–PDB count was determined by following the hyperlink associated to the number of entries or structures (which is updated weekly) and selecting “Customizable Table” from the ’Reports’ menu above the results section. Subsequently, SCOP, CATH, and PFAM ID options were selected as domain information under the ’Domain Details’ section and domain counts data were exported as a comma separated value (.csv) file report. Supplementary Tables The timeline was calibrated with a molecular clock of FSF structures (t = –3.8317. SCOP concise classification strings (ccs) of domain descriptions9 were downloaded from http://scop.mrc-lmb.cam.ac.uk/scop/parse/index.html for SCOP version 1.75 as the file dir_des_scop_txt_1_75.txt. Available descriptions for 2,223 single domains were obtained from SCOP unique identifiers (sunID). The Gene Ontology (GO) specifications were recorded from the Superfamily Database (SUPFAM) available at http://supfam.cs.bris.ac.uk/SUPERFAMILY/GO.html. High-coverage domain-centric GO annotations that were supported only by all UniProts (including multidomain UniProts) were downloaded as the file Domain2GO_supported_only_by_all.txt. High-quality truly domain-centric GO annotations that were supported by both single domain UniProts and all UniProts (including multidomain UniProts) were downloaded as the file Domain2GO_supported_by_both.txt. We reported only the GO annotations ‘by all’ to capture higher coverage. Also, the GO terms were reported only for the 2223 single domains with descriptions available. Specialized GO annotations from two levels of hierarchy downstream were taken from files Domain2GO-Hie-Dist1.csv and Domain2GO-Hie-Dist2.csv. Structural domains functional ontology (SDFO) that mapped information from a theoretic analysis of Domain2GO annotation profiles were reported from the file SDFO.txt.Domain ages and assignment of fusional/fissional properties followed ref.76 and the statistical test bench R’s igraph package77 were used to visualize and analyze the networks, respectively. The collective impact of events was made explicit by Pajek’s Visualization of Similarity (VOS) clustering method79. VOS helped reveal communities and design layouts of networks with nodes separated into network modules, where high modularity indices ranged from 94–95%. Number of clusters varied over networks . Network clusters were visually compacted to hubs and their cohesive neighbors with the energy-optimizing Kamada-Kawai ‘separate components’ algorithm80. Pajek allowed to proportionally reduce the size of highly connected nodes by some scaling factor for optimally uncluttered visualization. Waterfall and radial network layouts were designed with node-size scaled down by factors of 0.1 and 0.25, respectively. R packages equipped with specialized code constructs to draw graphs and derive statistics were used to analyze network properties82. We also used Pre-Hypertext Processing language (PHP) to write custom scripts that generated radial visualizations of the networks and helped conduct housekeeping data management83. The PHP scripts were executed in the command line. Results of these scripts were input into Pajek’s and R’s analytical procedures. We used the open-source software ImageMagick (www.imagemagick.org) for batch conversion, captioning, and appending of network images . A detailed description of partition and data files, list of network data analysis functions, charting and graphing procedures, methods to generate power law statistics, modularity indices and randomness checks, and the method pipeline used to achieve waterfall diagrams can be found in Supplementary Materials and Methods.Mathematical definitions for construction of networks can be found in Supplementary Materials and Methods. The social network analysis tool PajekP(k) given k (i.e. the probability of having k-neighbors) were used to derive the γ coefficient of the power law distribution and the determination coefficient, R2. The value of γ represents an absolute slope of the log linear model of P(k) versus k. The slope is usually ≤ 0. γ >> 1 indicates strong tendency towards preferential attachment. R2 indicates the percentage of data that fits the linear model. High values of both γ and R2 suggest strong scale-free behavior. Additional power law statistics were calculated as: (1) the exponent of the fitted power law distribution, α, with an assumption that P(X = x) is proportional to x–α; (2) KS fit statistic to compare the input degree distribution with that of fitted power-law; and (3) the KS p-value of a statistical test, with the null hypothesis that data is being drawn from a power law distribution85. α >> 1, 0 < KS fit scores << 1, and KS p-values ≥ 0.05 suggest that degree data was derived from a fitted power law distribution. Maximum log likelihood of the fitted scale-free parameters was also determined. Control networks were included for reference that were generated with ‘Barabási’ methods58 of the igraph package from R77. These controls simulated basic and extended age-dependent power law graph models given varying sizes of the evolving networks.Linear regression models of VOS Quality index (VQ) was determined using the Pajek VOS algorithm by considering the number or weights of the links (arcs) between the nodes as similarities. Clusters or communities that were deemed ‘similar’ were iteratively drawn closer to each other until a final layout was achieved with least crossings and closest clusters. The quality index VQ was thus calculated for this final layout as ∑i=1 c, j=i+1 c (eij − ai2), where c is the number of communities; eij is the fraction of edges with one node v in the community i (ci) and the other node w in the community j (cj), defined as ∑vw (Avw/2 m) where v ϵ ci, w ϵ cj, m is the sum of weights in the graph and Avw is the weighted value or 0, indicating presence or absence of edge between nodes v and w in the adjacency matrix A of the network; and ai is the fraction of weighted k neighbors attached to the nodes in community i, i.e. ki/2m79. (2) The Clustering Ratio (C-ratio) is the ratio of the number of network clusters to the count of the connected nodes in the network. (3) The average Clustering Coefficient (C) is defined as the ratio of the triangles impingent on a node to the connected triples, determined as a global average over all nodes in a simplified (undirected/unweighted) network70. C is not meaningful for strictly bipartite or scale-free graphs73. We also report coefficients of linear regression of C over the age and size of the networks of domain organization. (4) The Fast-Greedy Community (FGC) agglomerative hierarchical algorithm detects community structure for networks with m edges, n nodes, and a depth d of the dendrogram describing the community structure, given an optimized linear running time of O(m × d × logn) ~ O(n × log2n)86. The Newman-Girvan algorithm index (NG) was computed with two different input partitions, the first (5) defined by age (NGage) and the second (6) defined by VOS clustering (NGvos). NG calculates the modularity of a network given a predefined division or partition to measure the influence of the partition in separating the different node types. This indicates either assortative (positive) or disassortative (negative) mixing across modules65. The NG algorithm computes an index as 1/(2 m)∑ij(Aij − 1/(2m)kikj × ∆), where m is the sum total of weights in the graph and Aij are weighted entries in the adjacency matrix of the network; ki | kj and ci | cj are the weighted degrees and the components (numeric partitions) of the nodes i and j, respectively; finally, ∆ equals 1 if x = y and 0 otherwise65. The VQ, C-ratio, C and FGC indices each range from 0 to 1, while the NG indices range from − 1 to 1. In all cases, higher values represent strong modularity of the network at an event of evolutionary history. Heatmaps of modularity were constructed using log10-scaled modularity matrices, with each map element given as (Aij − kikj/(2m))Mnd, where Aij, ki, kj and m were the same as defined for NG65, while Mnd was the network’s modularity index at event nd. Cladistic representations of modularity were visualized with dendrograms whose metrics were calculated from squared Euclidean distance matrices, which indicate dissimilarities between cluster means87. The dissimilarity or distance matrices were clustered hierarchically using the Ward's minimum variance method that seeks compact and spherical clusters88.We investigated modularity using six indices: (1) The 89, was used to measure random network behavior. The resultant test statistic RVN is defined as ∑i=1→n−1 (Ri−Ri+1)2/∑i=1→n (Ri – (n + 1)/2)2, where Ri = rank (Xi) with i = 1…n, (RVN − 2)/σ is the asymptotically standard normal, and σ2 = [4(n − 2)(5n2 − 2n − 9)]/[5n(n + 1)(n − 1)2]. The null hypothesis of this method was randomness, which was tested against the alternate hypothesis of non-randomness, given a trend of RVN values. A p value is computed from a two-sided beta distribution approximation test. Random graph controls were created by following the Erdős–Rényi graph model6190.The Bartels rank test of randomness, which primarily offers a rank version of von Neumann's Ratio Test for RandomnessSupplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Figure S7.Supplementary Figure S8.Supplementary Figure S9.Supplementary Figure S10.Supplementary File 1.Supplementary File 2.Supplementary File 3.Supplementary Information.Supplementary Video 1.Supplementary Video 2.Supplementary Video 3.


Human African trypanosomiasis is a neglected parasitic disease for which the current treatment options are quite limited. Trypanosomes are not able to synthesize purines de novo and thus solely depend on purine salvage from the host environment. This characteristic makes players of the purine salvage pathway putative drug targets. The activity of known nucleoside analogues such as tubercidin and cordycepin led to the development of a series of C7-substituted nucleoside analogues. Here, we use RNA interference (RNAi) libraries to gain insight into the mode-of-action of these novel nucleoside analogues. Whole-genome RNAi screening revealed the involvement of adenosine kinase and 4E interacting protein into the mode-of-action of certain antitrypanosomal nucleoside analogues. Using RNAi lines and gene-deficient parasites, 4E interacting protein was found to be essential for parasite growth and infectivity in the vertebrate host. The essential nature of this gene product and involvement in the activity of certain nucleoside analogues indicates that it represents a potential novel drug target. Trypanosoma brucei gambiense and T. b. rhodesiense, which are transmitted by tsetse flies .,43.50, I5 cells/mL and exposed to various concentrations of 5 and 3′-deoxytubercidin [50, IC50/2 and IC50/5) for 48 h. Cells were resuspended in Laemmli sample buffer  containing β-mercaptoethanol. Genomic DNA was sheared by sonication and samples were stored at −80 °C until further analysis; 10 µL of protein sample was loaded on 10% Mini-PROTEAN® TGX Stain-Free™ Protein Gel (Bio-Rad) at 100 V. Proteins were transferred to a polyvinylidene difluoride (PVDF) membrane at 100 V. Primary antibodies for PAD1 and EF1ɑ  were used at 1:1000 and 1:7000, respectively [Tetracycline-induced 4EIP-AB parasites were seeded at a concentration of 5 × 10bercidin   were pre-treated for 48 h with 1 mg/mL of doxycycline and 5% sucrose in the drinking water. Doxycycline exposure was maintained throughout the entire experiment and fresh doxycycline solutions were prepared every 2–3 days. The tetracycline-negative groups received water with 5% sucrose. On day 0, mice were infected intraperitoneally with 5000 parasites. Each group was infected with a different strain: NY-SM + (pre-induced with tetracycline in vitro), NY-SM—(not induced with tetracycline), ADKIN A + (NY-SM cells transfected with RNAi insert Tb927.6.2300 and pre-induced with tetracycline in vitro), and ADKIN A—(NY-SM cells transfected with RNAi insert Tb927.6.2300 and not induced with tetracycline). Parasitaemia and anaemia were determined using a Neubauer improved haemocytometer. To confirm adequate knock-down of the genes of interest, 100 µL of blood was collected at day 4 post-infection (dpi). RNA was extracted using the RNA blood mini kit (QIAGEN) and subjected to RT-qPCR to determine the percentage of gene knock-down.The role of 4EIP RNAi-mediated knockdown (NY-SM cells transfected with RNAi insert Tb927.9.11050) and knockout (4EIP-KO and 4EIP-AB strains) in in vivo infectivity was evaluated in a similar manner as described above.T.b.b. AnTat1.1EPpyRE9 parasites were used to infect teneral tsetse flies (Glossina morsitans morsitans) through a parasitized blood meal supplemented with 10 mM reduced l-glutathione. The infected blood meal consisted of a mixture of infected mouse blood (5–7 dpi) and defibrinated horse blood to obtain a final concentration of >106 BSF parasites/mL. Newly emerged tsetse flies were divided into three groups and offered an infected blood meal with or without 5 or 3′-deoxytubercidin at a concentration corresponding to the IC50. After the infected blood meal, tsetse flies were fed every 2–3 days with uninfected defibrinated horse blood. At 7 dpi, tsetse flies were dissected to evaluate midgut infection ratios.All graphs and statistical analyses were prepared using GraphPad Prism 7 software.2–9 showed sub-micromolar activity against T. brucei parasites in vitro. Tubercidin was cytotoxic at low concentrations, as described in literature [4 and 9 showed a modest increase in selectivity, whereas the remaining analogues showed a significantly improved selectivity . Uptake of APOL1 by the parasite causes lysosomal and mitochondrial membrane permeabilization and the subsequent translocation of mitochondrial EndoG to the parasite nucleus causing DNA damage and cell death . This ta5 suggests the occurrence of compound phosphorylation upon uptake by the parasite. This phenomenon has already been described for other nucleoside analogues [5 remained active against the ADKIN knockdown strains at sub-micromolar levels indicates that this phosphorylation may not be essential or only partially contributes to the overall activity. It is clear from a broader compound evaluation on the developed ADKIN RNAi strains that various analogues exhibit different degrees of dependency on ADKIN or may be subject to different rates of enzymatic conversion by ADKIN. ADKIN plays an important role in trypanosomal purine salvage [ADKIN is a known enzyme in the purine salvage pathway of the parasite and is responsible for the direct phosphorylation of adenosine to AMP following uptake of the former ,49. The nalogues . However salvage ,49, but  salvage ,51 revea salvage .Leishmania major. 4EIP binds with its N-terminal domain to the mRNA cap-binding protein LeishIF4E, leading to dissociation from the cap and a suppression of active translation [5 can be partially attributed to 4EIP as confirmed by the use of knock-out and add-back control parasites. However, the cap-binding protein 4E1 was not influenced by 5, indicating that the inhibition of 4EIP works independently of 4E1. The suppression of translation in T. brucei, as regulated by 4EIP, is required for successful differentiation into short stumpy parasites in the bloodstream. This is an essential adaptation of the parasite for successful transmission to the tsetse fly vector [The second identified target, 4EIP, has been extensively studied in nslation . Similarnslation . The MoAy vector  and subsIn summary, we here describe the acquired insights in the MoA of novel 7-deazaadenosine nucleoside analogues for the treatment of HAT. RNAi screening in combination with confirmatory in vitro and in vivo experiments using knockout strains identified the involvement of 4EIP in the activity of some antitrypanosomal nucleoside analogues. The reliance of some compounds on this gene product and the essential nature for in vivo infectivity indicate that 4EIP represents a potential drug target. The available RNAi and knockout clones will allow a more in depth analysis of the structural requirements for metabolic activation and 4EIP interaction of newly synthetized nucleoside analogues.


Penicillium, while metabarcoding suggest a higher abundance of Alternaria. This discrepancy might reside on the targeted portion of the community  or in the specific features of each technique. Thus, this study shows that commercial wheat flour hosts a wide fungal diversity with several taxa potentially representing concerns for consumers, aspects that need more attention throughout the food production chain.Consumers are increasingly demanding higher quality and safety standards for the products they consume, and one of this is wheat flour, the basis of a wide variety of processed products. This major component in the diet of many communities can be contaminated by microorganisms before the grain harvest, or during the grain storage right before processing. These microorganisms include several fungal species, many of which produce mycotoxins, secondary metabolites that can cause severe acute and chronic disorders. Yet, we still know little about the overall composition of fungal communities associated with wheat flour. In this study, we contribute to fill this gap by characterizing the fungal microbiome of different types of wheat flour using culture-dependent and -independent techniques. Qualitatively, these approaches suggested similar results, highlighting the presence of several fungal taxa able to produce mycotoxins. In-vitro isolation of fungal species suggest a higher frequency of  Alternaria, Cladosporium, Fusarium, and Helmintosporium have been reported as contaminants of grains in the field (moisture content of 18–30%), while Aspergillus, Penicillium, Eurotium, and Mucor are mainly reported to contaminate grains in storage conditions (moisture content of 14–16%) [Wheat flour is an essential ingredient for the human diet on a global scale. However, the final quality and safety of flour-based products can be greatly influenced by fungal contaminations that can occur before and/or after harvest ,2,3. For 14–16%) ,6 and, m 14–16%) . Althoug 14–16%) ,10, few The presence of mycotoxin-producing fungi in grains influences all the downstream production chain as the milling process does not destroy fungi, and wheat flour may carry a significant mycological and mycotoxigenic load that can contaminate the food products at the end of the production chain ,12. FurtIn this study, we explored the fungal diversity associated with different types of wheat flour  using both a culture-dependent (in vitro isolation of fungi) and a culture-independent method , showing that these food products host a wide diversity of fungi, several of which potentially producing mycotoxins.We analyzed the diversity of the fungal microbiome associated with different types of wheat flours using two different methods. Wheat flour samples were collected from commercial mills located in Calabria  during 2018. Samples included flour of type “00”, “0”, and wholemeal (3 samples each). All samples were collected as three subsamples of ∼1 kg from uniform bulks of flour. Flour samples were kept in sterile plastic bags at 5 °C and analysed within 5 days after sampling. Samples were then used to isolate fungi in vitro (culture-dependent method) and processed to prepare ITS-amplicon libraries for metabarcoding analyses (culture-independent method).Alive fungal contaminants were isolated from our flour samples by plating them on potato dextrose agar (PDA) plates . From each individual sample, we collected three sub-samples of ∼10 g, and suspended them in 20 mL of sterile water:agar solution (0.1%). Each suspension was serially diluted up to a 1:1000 ratio in sterile water:agar solution (0.1%). Then, we plated 0.1 mL from each suspension on PDA plates amended with ampicillin and streptomycin  to prevent bacterial growth. Plates were incubated at 20 ± 2 °C for 5 days, and then inspected to count the number of CFU/g of sample. Each colony was then isolated on PDA plate, and isolates were grouped according to the morphology of colonies on PDA and according to microscopic features , yielding 34 representative isolates.These representative isolates were identified using molecular barcoding. Each isolate was grown on Potato Dextrose Broth (PDB)  at 22 °C for 5 to 7 days according to the growth speed of each different isolate. The mycelium was collected by centrifugation, washed twice with sterile distilled water and lyophilized. DNA was then extracted from 20 mg of lyophilized tissue using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions, and concentration and quality were measured using Nanodrop 2000 spectrophotometer .2) using a Mastercycler Ep Gradient S  set at 94 °C for 3 min; 94 °C for 30 s, 56 °C for 30 s, and 72 °C for 45 s repeated 35 times; and ending with 10 min of extension at 72 °C. PCR products were purified with Amicon Ultra-0.5 mL 100 k 96 PK centrifugal filters  and prepared for Sanger sequencing using a BigDye Terminator v3.1 Cycle Sequencing Kit  according to the manufacturer’s instructions. Libraries were then sequenced on a 3500 Genetic Analyzer  in both directions.PCR reactions were performed using primers ITS4 and ITS5  in 50 µLSequences were merged and manually curated using ChromasPro version 1.7.6, and grouped in bins containing identical sequences defined as sequence types (STs) . Each STWe characterized the composition of the whole fungal community in our flour samples using metabarcoding. DNA was extracted from ∼25 mg of flour using the DNeasy Plant Mini Kit  according to the manufacturer’s instructions, quantified using a Nanodrop 2000 spectrophotometer , and normalized to 50 ng/µL using ultrapure water. The ITS2 region from the fungal rRNA was amplified using the primers ITS3_KYO2 and ITS4  modifiedDemultiplexed forward and reverse reads were merged using the PEAR 0.9.1 . Data haPenicillium was the most diverse of all investigated samples  or to two or more reference species with identical or very similar ITS sequences , followed by type “00” (80–173 CFU/g) and type “0” (53–80 CFU/g). The genus  samples  with 17 equences . FurtherAspergillus was isolated from all samples of flour type “00” with a concentration ranging from 7 and 20 CFU/g. The analysis of sequences enabled the identification of two STs associated with five different Aspergillus species  in all investigated samples (Mycosphaerella (8.4%), and Fusarium (3.6%). Overall, all genera detected using the culturing method were also detected in metabarcoding analyses although data about the relative abundance were not consistent between the two methods , 417 (type “0”), and 301 OTUs . Members of the phylum Ascomycota dominated in all samples and accounted for 94.8% of the total number of detected sequences followed by Basidiomycota (3.3%) and unidentified fungi (1.4%). Within the phylum Ascomycota, Dothideomycetes (52.5%), and Sordariomycetes (39.4%) were found to be the most representative classes. At the level of genus, more than 130 different taxa were identified across all investigated samples. Among these, 17 had a relative abundance ≥1%. The genus  samples . This ge methods . In partanalyses , with thIn this study, we used two different approaches (culture-dependent and -independent) to characterize the fungal microbiome associated with different types of wheat flours. Our results suggest the presence of a wide diversity of fungal species that can be isolated from these products, with several taxa being potential producers of mycotoxins. In addition, we were able to isolate a higher number of CFU from wholemeal flour (527–1840 CFU/g) compared to “00” (80–240 CFU/g) and “0” (53–80 CFU/g) types. This might be the result of the product transformation, as wholemeal flour contains grain elements that are removed in the other two flour types, and this might reduce the whole microbial load.Aspergillus were the dominant members of the community, followed by Penicillium spp. Similar results were observed in wheat flour [Alternaria, Fusarium and Epicoccum resulted the most common members of the fungal community [Fusarium was the most abundant fungal genus in grains of durum wheat. These examples show that the fungal microbiome associated with grains and flours is quite diverse and variable, even when we look only at the portion that can be isolated and cultivated in vitro.While in our study we focused on studying the whole fungal community of different types of wheat flour, previous studies mostly focused on identifying mycotoxigenic fungal species ,38. For at flour , cerealsat flour , maize fat flour , and peaat flour . On fresommunity , while Community  found thPenicillium was the most abundant genus within the flour-associated mycobiome, followed by Cladosporium, Aspergillus and Alternaria. Similar results were obtained on whole wheat and corn flour [Alternaria, Cladosporium, Fusarium, and Helmintosporium are more common to contaminate grains in the field, while Aspergillus, Penicillium, Mucor, and Eurotium, are more common contaminants during the post-harvest phase [Penicillium in our samples may indicate that the flour products we used were contaminated by potential mycotoxigenic fungi during the postharvest storage.Our results suggest that rn flour , althougrn flour ,43,44,45st phase . Thus, tPenicillium, for example, was the most abundant in our samples with also the highest diversity of STs, and species of this genus have been previously reported to produce the mycotoxins patulin and ochratoxin A [Penicillium verrucosum, which is one of the most important ochratoxigenic species [Penicillium griseofulvum known to produce patulin [Penicillium citrinum, Penicillium commune, and Penicillium chrysogenum, potentially producers of citrinin, cyclopiazonic acid and roquefortine C, respectively, [Aspergillus are known to produce mycotoxins such as aflatoxins, ochratoxin A and fumonisins [Aspergillus, but the resolution of the marker we used did not allow to distinguish between Aspergillus oryzae, Aspergillus kamarensis and Aspergillus fasciculatus. Although these species are not known to produce mycotoxins [Aspergillus flavus that is an important aflatoxin producer [Alternaria  found in our study in low abundance can produce mycotoxins like alternariol or tenuazonic acid [Fusarium, Cladosporium, and Mycosphaerella, which are known pathogens/endophytes of wheat plants, and none of them has been previously reported to produce mycotoxins. Thus, the fact we were able to isolate them can be just the result of plant-microbe interaction occurring in the field, with no influence on the quality/safety of food products.Interestingly, most of the fungal genera isolated in our study contain species that can produce mycotoxins . Penicilatoxin A . Indeed, species , Penicil patulin . We alsoctively, . Similarmonisins . In our cotoxins , the ideproducer . Additionic acid ,52, but nic acid ,54. ThusThese results obtained using classic methods for fungal isolation and cultivation in vitro largely matched those obtained using metabarcoding. Qualitatively, all the fungal genera identified through in vitro cultivation were also found in the metabarcoding dataset. However, the region amplified during metabarcoding library preparation is much shorter compared to the one we used to identify fungal isolates. Thus, we were not able to accurately identify all the OTUs to species level, and we preferred to group them at the genus level to avoid providing incorrect information on their identity. In addition, the metabarcoding dataset uncovered a wider diversity of fungal taxa associated with wheat flours, and this can be the results of two main factors. First, the in vitro isolation has the limit that not all organisms can be cultivated, either because they are difficult to cultivate using standard media or they are not cultivable at all ,56, whilThis study provides a comprehensive picture of the fungal diversity of the three most used typologies of wheat flour. Using both culture-dependent and -independent techniques, we found the presence of several fungal taxa that can cause harm to consumers. Future research can further expand our results, by testing a wider range of producers, sampling timeframes, production areas, and comparing the mycobiome of wheat grains and flour to understand the source of potential mycotoxigenic fungi. A better understanding of the mycobiome of wheat flours increases our knowledge on the frequency and distribution of mycotoxin producers, with a positive impact on food safety. Consumers are increasingly demanding food with higher quality and safety standards, so it is essential to integrate these state-of-the-art tools into the quality control procedures so that unsuitable food products are quickly identified and removed from the production chain.


Addition of edible insects to food products may improve the nutritional status but can also influence their techno-functional properties. This study investigates the impact of supplementing wheat flour by cricket flour or paste at different levels (5–15%) on the rheological and textural properties of flour, dough, and baked chapatti. Addition of freeze-dried cricket flour resulted in the highest water absorption. The storage modulus increased at higher level (10–15%) of supplementation to wheat flour indicating an increased dough consistency. Similarly, biaxial extension of the dough showed an increased resistance to extension and decreased extensibility at higher level of supplementation due to a reduced strength of the gluten network. Uniaxial extension of baked chapatti showed less extensible and harder chapatti with the addition of a higher amount of cricket flour or paste. At lower level (5%), incorporation of cricket flour resulted in chapatti with textural properties comparable to the reference. Oven dried cricket powder is suggested as the best option for incorporating in chapatti dough to improve food security in Asian Countries. Chapatti, also known as “roti”, is a single-layered flatbread prepared from whole wheat flour which is a typical staple diet in South Asian countries. Freshly prepared chapatti with soft, pliable texture and slightly chewiness is desirable, and serves as a major source of energy and protein for millions of people ,2. BeingAcheta domesticus Linnaeus 1758) or mealworm (Tenebrio molitor) powder was observed by Cappelli et al. (2020) [A. domesticus) and cockroach (Nauphoeta cinerea) powder, respectively [Hermetia illucens or larval Tenebrio molitor flour had lower specific volumes than wheat flour bread, while A. domesticus did not affect the specific volume [T. molitor) was added to wheat flour dough at 5–10% [Edible insects are rich sources of dietary protein, fat, amino acids, fatty acids and fiber, and can be considered as an interesting alternative to the conventional animal sources ,9,10,11.. 2020) . A negatectively . Gonzále20 . A neat 5–10% .A. domesticus) using small and large deformation mechanical tests.As such, although extensive investigation of bread has been conducted, reports on the rheological properties of chapatti dough and baked chapatti are quite limited. In addition, there are no published data on the incorporation of edible insects into a chapatti. It was reported that flour with higher water absorption capacity resulted in improved textural properties . For thiAcheta domesticus; Insecta; Orthoptera; Gryllidae)  [Commercial (All bake) white wheat flour  was purchased from a local market . Adult house crickets  and placed in a freeze drier  for 40 h. The obtained powder was further ground  and sieved (mesh size < 1.18 mm) to discard coarse particles. To obtain oven-dried powder, thawed crickets were placed in a forced-air oven  at 65 °C for 17 h. Dried crickets were ground  to powder and sieved to discard larger fractions. The paste sample was obtained by grinding thawed crickets to a paste using a kitchen grinder . Freeze-dried (AFD) and oven-dried (AOD) powders were stored in a sealed polythene bag at 4 °C temperature until further analysis while paste (APT) was prepared instantly before each experiment.Wheat flour and cricket flour blends were formulated by substituting wheat flour at 5%, 10% and 15% level with freeze dried and oven dried cricket flour, coded respectively as AFD 5%, AFD 10%, AFD 15% and AOD 5%, AOD 10% AOD 15%. Cricket paste was used at the same levels . Batches with 100% wheat flour were used as control.Particle size distribution of wheat flour and cricket flour was measured by a laser diffraction particle size analyzer  using the Tornado Dry Powder System. The wavelength of the main illumination source was 750 nm with a minimum obscuration of 4%. The intensity of the scattered laser light was detected and transformed to particle sizes by the Fraunhofer model.Dough mixing characteristics of wheat flour (WF) and wheat flour–cricket flour (CF) blends were analyzed in a Farinograph (constant flour weight procedure) with a 50-g mixer bowl  according to the AACC International method 54-21.02. The parameters determined were water absorption (WA) to reach a dough consistency of 500 BU, dough development time and stability.Pasting properties of wheat flour and cricket flour blends were analyzed using a rheometer  equipped with a starch cell (C-ETD 160) and stirrer probe (ST 24-2D/2V/2V-30). The flour suspension was prepared by dissolving 1.8 g of flour (wheat flour or cricket flour blends) in 15.0 mL water. Suspensions were first mixed for 10 s and held at 50 °C for 50 s then heated from 50 to 95 °C at 0.1 °C/s, held at 95 °C for 5.0 min, after which the paste was cooled from 95 to 50 °C, and held at 50 °C for 2 min. Parameters obtained were peak viscosity, breakdown viscosity, setback viscosity and pasting temperature.To obtain a dough with good handling properties 52.4 g tap water and 2.0 g table salt (NaCl) were added to 100.0 g flour (either WF or CF blends) and mixed to dough by a kneading machine  using a dough hook for 5.0 min at speed 3. From each batch, the dynamic properties and biaxial extensibility of the dough and the texture measurement of baked chapatti were determined. To prepare the dough with cricket paste, the moisture content of the paste was used to calculate the water addition. However, the reference amount of 52.4 g water was insufficient to obtain a homogeneous dough probably because of the inclusion of water in the paste structure. Therefore, for the three paste samples 62.4 g water was used.Dynamic rheology of the dough sample was measured by a rheometer  using parallel serrated plates (25 mm diameter) with a strain sweep from 0.01 to 100% at a frequency of 1 Hz. After resting for 12 min an aliquot of dough was taken from the center and placed between the plates conditioned previously at 25 °C with 1.5 mm gap. Excess sample was trimmed, and the edge of the sample was covered with silicone oil to avoid sample dehydration during the test. The test started after a resting time of 2 min between the plates. Each formulation was prepared three times and each preparation was analyzed once.Biaxial extensibility measurement of the dough was carried out using a texture analyzer  equipped with a tortilla/pastry rig (HDP/TPB) on a heavy duty platform with a 5 kg load cell. After resting for 5 min, the dough was sheeted to approx. 1.9 mm thickness by a pasta sheeter  and cut into a specific dimension of 10 cm × 10 cm. The ball probe (SMSP/1 SP) started at 80 mm above the platform and travelled at a test speed and post-test speed of 1.0 and 10.0 mm/s, respectively over a distance of 75 mm. Data acquisition rate was 200 pps. Three samples from separate batches were evaluated and the results are presented as the maximum force or tenacity, the force to extend 30 mm and extensibility at maximum force.Chapatti was prepared according to the method described by Thakur et al. (2017) with slight modification . After rThe texture of the baked chapatti was evaluated using a texture analyzer  fitted with a tensile grip (A/TG). Rectangular strips of 7.0 cm × 3.5 cm dimension were cut out near the central portion of chapatti. The cut strip was analyzed by placing it between two clamps of the texturometer at a distance of 22 mm. The chapatti strip was pulled apart until it ruptured by using a load cell of 5 kg at a travelling speed of 1 mm/sec. The extensibility test was conducted using the return to start option. The force to extend the chapatti strip 1 mm, force to rupture, and the distance of extension were recorded. The mean of six measurements from three batches was calculated.One-way ANOVA was used to check the impact of adding cricket flour and paste on all the rheological and textural properties using SPSS  while normality was tested by Shapiro–Wilk test and homogeneity was tested by Levene’s test. Tukey’s post-hoc test was used to separate the means at 5% level of significance. Kruskal Wallis test with Benferroni post-hoc test was used for unequal and non-homogenous samples.Particle size of flour is important as it influences the dough rheology and the texture of final products due to the variations of constituents in different fractions of particles and the different water binding capacity as function of particle size . Wheat fA. domesticus).Incorporation of oven-dried, and freeze-dried cricket powder showed significant effects on the dough mixing properties as measured by the farinograph . GeneralA. domesticus) [Substitution of freeze-dried cricket flour (AFD) had a higher effect on water absorption than that of oven dried cricket flour (AOD) resulting in the highest water absorption in the formulation AFD 15%. Lucas-González et al., (2019) also reported the highest water absorption for freeze dried cricket (esticus) . Higher esticus) . The smaesticus) .p < 0.05) ranging from 1.83 min for wheat flour to 5.27 min for AOD 15%. Similar effects on dough development time due to addition of fiber and protein rich ingredients were reported by several authors [A. domesticus powder, but further addition had a significant effect [A. domesticus flour used by the author than that of our study (67 g/100 g DM). This is supported by Cappelli et al. (2020) who showed a significant positive relationship between water absorption and protein content of A. domesticus [Cricket flour addition also increased the dough development time significantly  showed that peak viscosity and breakdown viscosity had a significantly positive correlation with chapatti extensibility .p < 0.05). Similarly, the breakdown viscosity and setback viscosity decreased with an increasing cricket powder substitution. These lower viscosities indicate less pronounced variations in the viscosity profile during pasting. The lower setback viscosity due to addition of cricket flour indicates a lower gel stability and lower sensitivity to retrogradation [Peak viscosity decreased with a higher substitution level . For 10%radation , probablPatanga succincta) flour to rice flour [The result is also in agreement with the findings reported by several authors when supplementing wheat flour with wheat bran as a source of insoluble fiber , chickpece flour . Khan etce flour . The incce flour ,36. The ce flour .A dynamic oscillatory test was conducted to study the viscoelastic properties of the dough prepared from different blends of wheat flour and cricket paste along with freeze-dried and oven-dried cricket powder. To facilitate direct comparisons between the effects of adding cricket flour to wheat flour dough, a constant water amount was added. However, more water was required to make a homogenous dough with cricket paste  compared to the loss modulus , which indicate a viscoelastic solid behaviour of all the doughs. As can be seen in The same increasing trend with increasing substitution is present for paste, but due to the higher water addition, these values cannot be compared with the results for cricket flour.Patanga succincta L.) powder and cricket (A. domesticus) protein hydrolysates addition to rice flour and corn masa dough were also reported by Indriani et al. (2020) and Luna et al. (2021), respectively [Several factors can contribute to the changes of storage module and loss module of the formulated dough. Firstly, the lower water content of cricket powder compared with wheat flour might contribute to the higher value of G’, as both the moduli are sensitive to water that acts as a plasticizer in the dough system . Secondlectively ,43.Dough resistance to deformation or tenacity could be used as a predictor of gas retention capacity of the dough during baking, while extensibility of dough is used as an indicator of the handling characteristics of the dough.The biaxial extensibility tests showed that both the tenacity and extensibility of the dough were significantly affected with the addition of cricket powder as shown in Dough extensibility is another parameter used to predict baked chapatti as dough with a higher extensibility gives better chapatti with better texture and mouth feel . Dough eT. molitor) and cricket (A. domesticus) powder resulted in an increased tenacity and decreased extensibility of the bread dough [Tenacity and extensibility of dough depends on gluten strength which could be disrupted by protein and fiber present in cricket powder resulting in a weak gluten network that could easily break down during textural analysis. Cappelli et al. (2020) reported that higher level (10% and 15%) supplementation of wheat flour with mealworm  crickets and cockroach flour was reported by several authors ,20,46. T% cricketPartial substitution of wheat flour with cricket flour improves the nutritional value of bakery products but can also have an impact on the functional properties. The incorporation of cricket flour in chapatti dough altered the Farinograph characteristics and the differences were more pronounced at the higher level of substitution. Freeze dried cricket flour had a more pronounced effect on the mixing properties compared with oven dried flour, with a higher water absorption, longer dough development time and lower dough stability. Unlike mixing properties, pasting properties did not differ between the types of cricket flour but the addition of cricket flour reduced all the pasting viscosities. The storage modulus increased sharply with a higher substitution of cricket flour and paste showing stiffer dough compared to wheat flour dough. This was also confirmed in the biaxial extensibility test of the dough, with a higher tenacity and reduced extensibility, especially at higher levels of substitution (10–15%). The textural properties of the baked chapatti did not differ significantly from the wheat flour reference at 5% supplementation, but higher addition (10–15%) of cricket flour and paste resulted in harder and less extensible chapatti. An addition of a smaller amount (5%) of cricket flour or paste is possible without significantly altering the texture of wheat flour chapatti. Higher amounts of crickets can only be used in combination with hydrocolloids, which will be part of further research, together with a detailed study of the consumer acceptability of the enriched chapatti by sensory analysis.


Protein domains are functional and structural units of proteins. They are responsible fora particular function that contributes to protein’s overall role. Because of thisessential role, the majority of the genetic variants occur in the domains. In this study,the somatic mutations across 21 cancer types were mapped to the individual proteindomains. To map the mutations to the domains, we employed the whole human proteome topredict the domains in each protein sequence and recognized about 149 668 domains. A novelPerl-API program was developed to convert the protein domain positions into genomicpositions, and users can freely access them through GitHub. We determined the distributionof protein domains across 23 chromosomes with the help of these genomic positions.Interestingly, chromosome 19 has more number of protein domains in comparison with otherchromosomes. Then, we mapped the cancer mutations to all the protein domains. Around46–65% of mutations were mapped to their corresponding protein domains, and significantlymutated domains for all the cancer types were determined using the local false discoveryratio (locfdr). The chromosome positions for all the protein domains can be verified usingthe cross-reference ensemble database.Database URL: https://dcmp.vit.ac.in/ Key PointsDCMP is a web-based resource for protein domains, providing chromosome positions andcancer mutation counts.DCMP provides the protein domain distribution across 23 chromosomes.DCMP allows the user to explore significantly mutated domains across the 21 cancertypes.Cancers are triggered by collective changes in genetic and non-genetic materials, which areinduced by environmental factors that elicit inappropriate activation or inactivation ofspecific genes . It starBRCA1 andBRCA2 , bladder (3.6%), salivary (3.9%), vaginal(3.6%), endometrial (3.4%) and colorectal cancers (1.3%). Similarly, 6.02% of alteredEGFR is observed in several cancer types, including lung, breast andcolon. Hence, molecule inhibitors and therapeutic drugs are being developed forHER2 and EGFR with more excellent selectivity, specificto HER2 and EGFR mutations , INSDC Assembly GCA_000001405.28, December2013 . The proThe homo sapiens proteome containing 109 095 sequences was obtained from the Ensembldatabase using genome assembly GRCh38. The PfamScan search tool is locally installed,incorporating HMMER and BLAST to search against Pfam domain libraries. The individualprotein sequence of the target species was searched against Pfam libraries, and the totalestimate of 169 745 protein domains was predicted. We considered 149 668 domain hits withan e-value of ≤0.01, and To install the PfamScan program:http://ftp.ebi.ac.uk/pub/databases/Pfam/.First, download the tarball ‘PfamScan.tar.gz’ and unpack the script using ‘tar zxvfPfamScan.tar.gz’ command. The standalone Perl script ‘pfam_scan.pl’ is obtained fromhttp://hmmer.org/download.html and add HMMER3 binaries to your path.Second, compile the HMMER3 source code using the tarball of the HMMER3 beta 3 releasefrom the HMMER site Install non-standard Perl dependencies, the Moose framework and Bioperl 1.4 viaCPAN.Finally, add the Pfam Modules to your PERL5LIB using the ‘exportPERL5LIB=/path/to/pfam_scanDir:$PERL5LIB’ bash command.To run searches using ‘pfam_scan.pl’:http://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/.Download Pfam data files, Pfam-A.hmm, Pfam-A.hmm.dat, Pfam-B.hmm, Pfam-B.hmm.dat andactive_site.dat from the Pfam FTP site Generate the binary files for Pfam-A.hmm and Pfam-B.hmm by running the followingcommands: hmmpress Pfam-A.hmm and hmmpress Pfam-B.hmm.Input the protein sequences in a FASTA-format file containing your querysequence(s).Run the program using ‘pfam_scan.pl -fasta <fasta_file> -dir <directorylocation of Pfam files>’.The COSMIC database was used to download the mutations for 21 different cancers, usingthe GRCh38 genome version, as shown in https://github.com/iarnoldemerson/Protein-to-genome-position.git, and The domains predicted from the protein sequence are reported in peptide position, whereasthe cancer mutations are depicted in genomic locations. Before mapping the cancermutations to their corresponding protein domains, we should change either the mutation ordomain positions. In this study, we choose to change the domain positions to their genomicpositions. A Perl program was written using the ensemble Perl API module to convert theprotein domain positions into genomic positions, and the steps followed are shown in After converting the domain position into genomic position using the Perl API program,cancer mutations are now ready to map with their protein domains. WNK1,WNK2 and OXSR1. The accumulated SNP signifies the sumof mutations that occurred in the OSR1_C domains, whereas the cumulative domain length isaccomplished by summing their domain length in all those three genes.After mapping all the cancer mutations, the mutation count for each Pfam domain needs tobe normalized. In this study, we normalized the mutation counts by utilizing thecumulative length of all occurrences of the Pfam domain within the cancer set. et al. The relative frequency isutilized as the success probability (p). Then, it was normalized usingthe Bernoulli distribution signal to noise ratio, which results in the normalized score,z, as follows: To determine the SMDs, we adapted the method to estimate the local false discovery ratein microarray experiments by Efron The null distribution is estimated using the ‘locfdr’ package from R and employed thesestatistics to identify statistically significant domains with a local false discovery rateof <0.1. False Discovery Rate (FDR) controls the number of false positives that resultin a significant result, and it has a greater ability to find truly significant results.For example, an FDR of 0.1 implies that 10% of significant tests will result in falsepositives. In a gene expression study, when the FDR was fixed at 0.1, seven genes with asignificant difference were found. However, the number of significant differencesdecreases to 1, using a more stringent FDR of 0.05. Furthermore, it has been shown thatthe number of false positives recovered is considerably higher than the number expected. Thus weFrom The peptide to genome conversion program takes peptide start and end as an input . The next step is to map themutations into each protein domain, and this step requires more computation time since themutation position is compared with all the domain positions. Mapping of mutations wascarried out for all 21 cancer types, and http://dcmp.vit.ac.in/mutated_domains/). The distribution of SMDs variesacross cancer types, as depicted using heatmap in The locfdr was used to determine the statistically significant domains for all the cancertypes. The top 10 protein domains in each cancer type are shown in TP53 gene is a gene that is mutated in many cancers, and it isthe most common gene mutation found in cancer cells. A tumor-suppressor gene,TP53, codes for a protein that inhibits the development and growth oftumors. Since over 50% of human cancers carry loss of function mutations in thep53 gene, p53 has been considered one of the classicaltype tumor suppressors. There are three protein domains, namely, PI3Ka, Nebulin andzf-H2C2_2, which occur in >10 cancer subtypes. PI3Ka is believed to be one of thesignificant therapeutic targets for cancer treatment (NEBL(nebulette) gene is located on chromosome 10p12.31 and encodes the nebulin-like protein,and studies indicate the role of NEBL as an oncogene and tumor suppressorin cancer (ZF genes isexpressed at levels comparable to other cancer-relevant genes (Interestingly, the p53 protein domain has been found in the top 10 list of all cancertypes. The reatment . Hyperacn cancer . The ZF nt genes .http://dcmp.vit.ac.in/.The database facilitates users to explore mutated protein domains for different cancertypes, and it consists of the following three primary menus: domain genomic positions,SMDs and mutated domains. The initial one represents the chromosome positions for anygiven protein domain with references to the ensemble. The second menu displays the top 10SMDs with references to the Pfam database. The last menu provides a complete list ofmutated protein domains for any given cancer type. The front-end was designed using PHPscripting language with MySQL as the database, and the interactive graphs were plottedusing CanvasJS. The DCMP database can be reached through the weblink Mutation in the protein contributes specific information than a normal protein. It cancause cells to multiply uncontrollably and become cancerous. Identification of mutatedproteins in the cell is an essential part of developing novel therapeutic targets. The wholehuman proteome was used to determine the mutated domains in 21 different cancer types.Somatic mutations were mapped to the protein domains, and the SMDs were selected usingstatistical methods. Users can visualize the genomic positions of any protein domain and thelist of mutated domains in 21 cancer types using the DCMP database.baab066_SuppClick here for additional data file.