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. 2010 Oct-Dec;1(4):328–334. doi: 10.4161/self.1.4.13315

No human protein is exempt from bacterial motifs, not even one

Brett Trost 1, Guglielmo Lucchese 2, Angela Stufano 2, Mik Bickis 3, Anthony Kusalik 1, Darja Kanduc 2,✉
PMCID: PMC3062388  PMID: 21487508

Abstract

The hypothesis that mimicry between a self and a microbial peptide antigen is strictly related to autoimmune pathology remains a debated concept in autoimmunity research. Clear evidence for a causal link between molecular mimicry and autoimmunity is still lacking. In recent studies we have demonstrated that viruses and bacteria share amino acid sequences with the human proteome at such a high extent that the molecular mimicry hypothesis becomes questionable as a causal factor in autoimmunity. Expanding upon our analysis, here we detail the bacterial peptide overlapping to the human proteome at the penta-, hexa-, hepta- and octapeptide levels by exact peptide matching analysis and demonstrate that there does not exist a single human protein that does not harbor a bacterial pentapeptide or hexapeptide motif. This finding suggests that molecular mimicry between a self and a microbial peptide antigen cannot be assumed as a basis for autoimmune pathologies. Moreover, the data are discussed in relation to the microbial immune escape phenomenon and the possible vaccine-related autoimmune effects.

Key words: bacterial versus human peptide overlap, molecular mimicry, microbial immune escape, autoimmunity, vaccines

Introduction

The sustained increase in the incidence of autoimmune diseases in the population1–4 and the continuously expanding list of autoimmune pathologies and autoantigens5 necessitate investigations into the role of molecular mimicry in the triggering of autoimmunity.6 Molecular mimicry, i.e., the sharing of a linear amino acid sequence or a conformational fit between a microbe and a host self determinant, has been and still is the predominant field of investigation in autoimmunity research.7–19 Recently, we reported that viral proteins overlap extensively with the human proteome,20,21 with only a limited number of viral pentamers not found in the human proteome. In conflict to the dominant tendency to causally associate viral infections and autoimmune diseases, these findings support the view that molecular mimicry is over-emphasized as a critical mechanism during autoimmune disease pathogenesis. We reasoned that, if there is a link between viral infections and autoimmune reactions, then the documented extent of viral peptide overlapping in the human proteome would suggest that the entire world human population would suffer from autoimmunity. In addition, the analysis of a number of bacterial proteomes for amino acid sequence similarity to the human proteome demonstrated the sharing of hundreds of nonamer sequences between bacterial and human proteomes.22 Again, the implications of these data appear of importance to define the current molecular mimicry model and, in general, to understand basic mechanisms in pathology and address research towards new directions. Here, as a further step in our studies on autoimmunity mechanisms, we detail the bacterial versus human peptide overlapping at the 5-, 6-, 7- and 8-mer levels, and demonstrate that no human proteins are exempt from the presence of bacterial motifs.

Results

Analyzing forty bacterial proteomes versus the Homo sapiens proteome: an overlap snapshot.

Forty bacterial proteomes, 20 pathogenic and 20 non-pathogenic, were analyzed for peptide sharing with the human proteome at the penta-, hexa-, heptaand octapeptide level to examine bacterial-versus-human similarity. Peptide similarity analysis of bacterial proteomes versus the human proteome was conducted as already described in detail20–22 and produced the data illustrated in Table 1.

Table 1.

Peptide sharing between bacterial proteomes and the human proteome at the 5-, 6-, 7- and 8-mer level

Taxa Id Bacterium name 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
299768 Streptococcus thermophilus 450402 3863966 35961 92.8 448812 345526 33679 31.8 447222 29009 13533 3.5 445632 5821 2258 0.5
367928 Bifidobacterium adolescentis 593219 4720571 35984 91.6 591592 464558 34565 31.8 589965 41915 16817 3.6 588338 7312 3025 0.4
206672 Bifidobacterium longum 631639 4930744 35983 91.7 629916 507033 34673 32.5 628193 46282 17784 3.7 626470 7796 3266 0.4
257314 Lactobacillus johnsonii 580247 4373499 35966 91.6 578438 394509 34122 29.9 576629 32622 14862 3.2 574820 5814 2354 0.4
272621 Lactobacillus acidophilus 576525 4285917 35956 91.4 574666 378992 33885 29.0 572807 31372 14409 3.0 570948 5704 2190 0.4
203120 Leuconostoc mesenteroides 598355 4551589 35963 92.0 596353 419771 34303 30.4 594351 34970 15491 3.3 592349 6662 2432 0.5
416870 Lactococcus lactis 672683 4980061 35981 92.1 670299 495503 34595 31.5 667915 41383 17246 3.5 665531 6919 2831 0.4
393595 Alcanivorax borkumensis 896972 6304479 35999 91.5 894220 736390 35320 33.5 891468 62704 21456 4.1 888716 9487 4176 0.5
220668 Lactobacillus plantarum 904305 5835444 35989 90.8 901306 623037 35071 30.0 898307 52498 19685 3.3 895308 8195 3575 0.4
226185 Enterococcus faecalis 940332 6145880 35988 91.6 937095 668944 35158 31.1 933858 57666 20168 3.3 930621 9470 3796 0.4
420662 Methylibium petroleiphilum 1363510 7268505 36004 91.3 1359155 1153424 35558 35.9 1354800 123368 26348 5.1 1350445 19643 7555 0.7
251221 Gloeobacter violaceus 1359892 7559021 36004 91.7 1355488 1155772 35593 35.8 1351084 114279 26125 4.7 1346680 18514 7090 0.5
369723 Salinispora tropica 1499556 7020220 35995 91.6 1495034 1268110 35537 37.5 1490512 142761 27415 5.5 1485990 20474 8513 0.6
78245 Xanthobacter autotrophicus 1598054 7692783 36005 91.6 1593085 1285165 35609 35.8 1588116 136687 27146 4.9 1583147 21199 7980 0.6
138119 Desulfuobacterium hafniense 1580893 8351354 36000 90.5 1575878 1125244 35636 31.4 1570863 97706 25570 3.5 1565848 14124 6018 0.4
318586 Paracoccus denitrificans 1541153 7483126 35998 90.8 1536137 1192734 35649 34.5 1531121 122240 26532 4.7 1526105 17463 7217 0.6
351746 Pseudomonas putida 1734619 8415989 36006 90.5 1729375 1327148 35661 33.9 1724131 126059 27425 4.3 1718887 16807 7352 0.5
222523 Bacillus cereus 1505863 7776742 36005 90.0 1499864 981068 35501 29.7 1493866 82158 23724 3.2 1487873 10991 4881 0.4
366394 Sinorhizobium medicae 1896855 8634389 36006 90.6 1890710 1395567 35700 33.6 1884565 133621 27663 4.2 1878420 20378 7549 0.5
224911 Bradyrhizobium japonicum 2582736 9728471 36007 89.8 2574488 1816906 35804 32.9 2566240 183683 29933 4.1 2557992 27201 9643 0.5
471472 Chlamydia trachomatis 306365 3176212 35959 93.3 305482 276387 33051 34.4 304599 22712 11834 4.1 303716 3440 1592 0.5
455434 Treponema pallidum 345928 3356569 35952 93.0 344863 299569 33510 33.8 343798 26743 12789 3.9 342733 5561 2064 0.5
392021 Rickettsia rickeitsii 313106 2762374 35941 92.6 311761 226202 31809 30.7 310416 21699 10218 3.6 309071 5559 1605 0.7
458234 Frunciselia tularensis 450826 3551140 35966 91.8 449318 298668 33231 29.6 447810 24243 12135 3.2 446302 4463 1687 0.5
85962 Helicobacter pylori 485536 3780814 35963 92.2 483971 359383 33668 31.3 482406 29876 13919 3.5 480841 4281 1925 0.4
224326 Borrelia burgdorferi 412046 2867951 35918 93.1 410464 257661 32030 30.8 408882 20885 11291 3.3 407300 3026 1426 0.4
195099 Campylobacter jejuni 531256 3866438 35955 92.2 529420 374675 33686 30.7 527584 30421 14358 3.4 525748 5188 2141 0.4
374833 Neisseria meningitidis 559567 4473458 35977 92.2 557569 433384 34379 32.4 555571 38343 16154 3.8 553573 6201 2582 0.6
516950 Streptococcus pneumoniae 624663 4783135 35976 92.2 622474 467024 34432 31.7 620285 38722 16550 3.5 618096 6560 2702 0.4
257309 Corynebacterium diphtheriae 716248 5461966 35986 92.2 713983 601188 34952 34.0 711718 51983 19542 4.0 709453 7482 3527 0.5
212717 Clostridium tetani 799625 4942017 35972 91.3 797211 519578 34461 29.5 794797 42607 17049 3.1 792383 6520 2876 0.4
273036 Staphylococcus aureus 725020 4995196 35982 91.2 722512 465501 34460 28.8 720004 36783 16275 3.0 717496 5780 2508 0.4
262698 Brucella abortus 874969 6056419 35991 92.0 871895 714382 35165 33.8 868821 65769 21087 4.2 865747 12203 4314 0.7
400673 Legionella pneumophila 1021398 6572966 35999 90.7 1018194 703289 35287 30.2 1014990 57003 20586 3.3 1011786 9586 3736 0.4
520 Bordetella pertussis 1051997 6271766 35998 91.6 1048737 884648 35324 35.3 1045477 91024 23660 4.9 1042217 14756 5916 0.7
243277 Vibrio cholerae 1138797 7077244 36000 90.7 1134997 833801 35408 31.2 1131197 70647 22617 3.5 1127398 12337 4680 0.4
349746 Yersinia pestis 1123279 7065400 36001 90.8 1119462 846102 35433 31.9 1115645 73246 22780 3.7 1111828 11446 4709 0.5
83331 Mycobacterium tuberculosis 1307195 6877511 35993 91.5 1302949 1082521 35477 35.8 1298704 115970 25679 4.9 1294460 19203 7141 0.6
99287 Salmonella typhimurium 1401436 7838576 36001 90.0 1396908 1015124 35570 31.4 1392380 89038 24671 3.6 1387852 12184 5316 0.4
261594 Bacillus anthracis 1439159 7615447 36005 90.1 1433570 944793 35483 29.7 1427981 76954 23306 3.2 1422392 11356 4851 0.4
Total bacterial overlap*: 15260383 36014 9133718 36014 1643139 35906 200708 31170
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The level of peptide overlap between 40 bacterial proteomes and the human proteome is shown. The filtered bacterial proteomes consisted of 128,248 unique proteins, while the human proteome contained 36,014 proteins at the time of the analysis. Bacteria that are pathogenic to humans are shown in bold. Information for each bacterium can be found at ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi. Column details are as follows: (1) Number of 5-mer occurrences in the bacterial proteome (including duplicate instances of same unique 5-mer); (2) Observed bacterial 5-mer occurrences in the human proteome (including multiple occurrences); (3) Number of human proteins involved in the pentapeptide overlap; (4) Percent of unique bacterial 5-mers which occur in the human proteome; (5) Number of 6-mer occurrences in the bacterial proteome (including duplicate instances of same unique 6-mer); (6) Observed bacterial 6-mer occurrences in the human proteome (including multiple occurrences); (7) Number of human proteins involved in the hexapeptide overlap; (8) Percent of unique bacterial 6-mers which occur in the human proteome; (9) Number of 7-mer occurrences in the bacterial proteome (including duplicate instances of same unique7-mer); (10) Observed bacterial 7-mer occurrences in the human proteome (including multiple occurrences); (11) Number of human proteins involved in the heptapeptide overlap; (12) Percent of unique bacterial 7-mers which occur in the human proteome; (13) Number of 8-mer occurrences in the bacterial proteome (including duplicate instances of same unique 8-mer); (14) Observed bacterial 8-mer occurrences in the human proteome (including multiple occurrences); (15) Number of human proteins involved in the octapeptide overlap; (16) Percent of unique bacterial 8-mers which occur in the human proteome.

*

Obtained by combining all bacterial proteomes into one protein set, and computing the overlap of this set with the human proteome. The human proteome was downloaded from UniProtKB23 and analyzed by custom programs written in C24 (see under Methods).

Table 1, last line, shows that combining all bacterial proteomes into one protein set and then computing the overlap of this set with the human proteome gives 15,260,383 perfect pentapeptide matches distributed through 36,014 human proteins; 9,133,718 perfect hexapeptide matches distributed through 36,014 human proteins; 1,643,139 perfect heptapeptide matches distributed throughout 35,906 human proteins; and 200,708 perfect octapeptide matches distributed throughout 31,170 human proteins. That is, the bacterial-versus-human overlap at the penta- and hexapeptide levels spans the entire human proteome: there does not exist one human protein that does not host a bacterial hexapeptide. Only 104 of the 36,104 human proteins (i.e., about 0.3% of the human proteome) are exempt from bacterial heptapeptide motifs. The human proteins that do not harbor bacterial motifs at the heptapeptide level are listed in Box 1.

Actually, the heptapeptide sharing between bacteria and human proteome is extensive and massive as schematized in the circle graph of Figure 1, illustrating the percentage distribution of bacterial heptapeptides throughout the human proteome.

Figure 1.

Figure 1

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Distribution of bacterial heptapeptides throughout the human proteome: only 0.3% of the human proteome is exempt from bacterial heptapeptide motifs. The area corresponding to the human proteins containing bacterial heptapeptide(s) is reported in red. The area corresponding to the human proteins with no bacterial heptapeptide(s) is in black. The pie chart is a schematic representation of the peptide sharing between the forty bacteria under analysis in Table 1 and Homo sapiens.

The bacterial motif distribution through the human proteome decreases at the octamer level, but is still impressive: only 4,844 human proteins (just 13.44% of the human proteome) are exempt from bacterial 8-mers.

In addition, Table 1 shows that the microbe's pathogenicity does not affect the level of bacterial overlaps through the human proteins (see the percent of unique bacterial n-mers which occur in the human proteome, columns 4, 8, 12 and 16 and in Table 1). As a further confirmation, log-log plotting the bacterial 5-mer occurrences in the human proteome as a function of the bacterial proteome length produces the graph illustrated in Figure 2. It can be seen that the bacterial versus human overlap is independent of the microbe's pathogenicity and expectedly depends almost exclusively on the size of the bacterial proteome.

Figure 2.

Figure 2

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Bacterial 5-mer occurrences in the human proteome as a function of the bacterial proteome length. Symbol plus in black: non-pathogenic bacteria. Symbol plus in red: pathogenic bacteria. Information for bacteria can be found in Table 1.

Analyzing bacterial proteins versus the Homo sapiens proteome for heptapeptide sharing: the Klebsiella pneumoniae and Proteus mirabilis paradigmatic examples.

Table 1 suggests that molecular mimicry between a self and a microbial peptide antigen cannot be assumed as a single or exclusive basis for autoimmune pathologies. Also, it has to be underlined that the data reported above analyze only 40 bacterial proteomes. Therefore, taking into account that the human organism hosts hundreds of bacterial organisms amounting to trillions of bacterial cells,25,26 this study greatly understates the level of overlap between bacterial and human proteomes. But even considering one bacterial protein only, we are presented with a marked bacterial-versus-human peptide commonality. In this regard, scientific relevant models are offered by Klebsiella pneumoniae and Proteus mirabilis. In the past decades, there has been an intensive scientific debate because of a consecutive sequence of six amino acids, Gln-Thr-Asp-Arg-Glu-Asp (QTDRED) shared between HLA B27.1 and the nitrogenase reductase enzyme of K. pneumoniae.27 This sequence commonality was invoked as a possible structural basis for cross-reactivity to occur and cause of ankylosing spondylitis. Following a profusion of inconclusive papers on the issue,27–33 the attention successively shifted on the molecular mimicry between human motifs (EQRRAA and LRREI) and P. mirabilis peptide sequences (ESRRAL and IRRET) as a possible aetiological basis for autoimmune rheumatoid arthritis.34

Peptide overlap analysis shows that K. pneumoniae and P. mirabilis proteomes have a peptide platform in common with the human proteome. As examples, Table 2 shows the heptapeptide sharing between the human proteome and K. pneumoniae ATP-binding protein and P. mirabilis ATP-dependent RNA helicase protein. The K. pneumoniae ATP-binding protein (UniProt accession number: B5XMS5_KLEP3, aa 1–233) is formed by 227 heptamers, 36 of which are present in the human proteome. Analogously, P. mirabilis RNA helicase protein (UniProtKB: B4ESW0_PROMH, aa 1–465, formed by a total of 459 heptamers) has 190 perfect heptapeptide sequences in common with the human proteome (Table 2). In conclusion, data from Table 2 further demonstrate that molecular mimicry cannot be considered as a single or exclusive causal factor in the genesis of autoimmune phenomena.

Table 2.

Heptapeptides are described by their amino acid position in the bacterial protein, amino acid sequence, and number of exact matches to the human proteome. The human proteins hosting bacterial heptapeptides are indicated by accession number (www.uniprot.org)

Aa Pos Sequence Matches Human proteins hosting bacterial heptapeptides
K. pneumoniae ATP-binding protein:
41 VGTSGSG 1 Q8WXI7
44 SGSGKST 4 ABCB7; ABCBA; Q5T6J7; Q5T6J8
45 GSGKST 4 CAR15; CFTR; CN37; PE X1
46 SGKSTLL 4 CAR15; CFTR; Q9BX10; Q9P1K2
47 GKSTLLH 1 DIRA3
51 LLHLLGG 1 Q6IF12
103 SALENVA 1 CNR2
112 LLIGKKK 1 SEM4D
113 LIGKKKP 1 SEM4D
114 IGKKKPA 1 Q8N6H7
130 LQAVGLE 1 Q6JQN1
152 QRVAIAR 3 ABCB7; ABCBB; Q9HAQ7
153 RVAIARA 2 ABCB7; ABCBB
154 VAIARAL 2 ABCBB; CCR10
155 AIARALV 5 MDR1; MDR3; Q4G0Q4; Q6KG50; TAP2
164 PRLVLAD 1 TIE1
193 VAQRTAF 1 TJAP1
222 RLTADLT 1 RNF39
223 LTADLTL 1 RNF39
P. mirabilis ATP-dependent RNA helicase protein:
4 FTSLGLS 1 SYQ
5 TSLGLSE 1 SYQ
7 LGLSEAL 1 Q6ZMC8
8 GLSEALL 1 SNX7
9 LSEALLR 1 Q6W0C5
11 EALLRAI 1 ITIH2
25 PTPIQQQ 1 NGAP
32 AIEPILA 1 ANC5
46 AQTGTGK 2 DDX43; DDX53
47 QTGTGKT 7 DDX43; DDX53; KIF11; KIF3A; KIF3B; KIF3C; KIFC2
48 TGTGKTA 1 DDX27
49 GTGKTAA 4 DD19A; DD19B; DDX25; DDX27
50 TGKTAAF 4 DD19A; DD19B; DDX25; DDX27
59 PILEKLA 1 Q6VMQ6
79 ALILTPT 1 Q5T1V6
80 LILTPTR 1 Q5T1V6
81 ILTPTRE 1 Q5T1V6
82 LTPTREL 6 DDX24; DDX43; DDX47; DDX49; DDX53; Q5T1V6
83 TPTRELA 9 DDX24; DDX43; DDX46; DDX47; DDX49; DDX53; Q5T1V6; Q8NHQ9; Q8TE C9
86 RELAAQI 1 MA1C1
102 YLPIRSL 1 CN103
126 GVDVLIA 1 Q6ZND7
127 VDVLIAT 1 Q6ZND7
128 DVLIATP 1 Q6ZND7
129 VLIATPG 1 Q6ZND7
130 LIATPGR 2 DDX27; Q6ZND7
131 IATPGRL 11 DDX17; DDX23; DDX27; DDX43; DDX47; DDX49; DDX53; DDX54; DDX5; Q5T1V6; Q9UI98
132 ATPGRLL 2 DDX18; Q5T1V6
133 TPGRLLD 2 DDX18; Q5T1V6
153 VLVLDEA 3 Q8NAM8; Q8NHQ9; Q8TE C9
154 LVLDEAD 11 DDX10; DDX17; DDX1; DDX28; DDX3Y; DDX43; DDX48; DDX4; DDX5; Q8NHQ9; Q8TE C9
155 VLDEADR 9 DDX10; DDX17; DDX23; DDX3Y; DDX46; DDX4; DDX5; Q8NHQ9 Q8TE C9
156 LDEADRM 8 DDX17; DDX23; DDX27; DDX3Y; DDX41; DDX46; DDX4; DDX5
157 DEADRML 5 DDX17; DDX27; DDX3Y; DDX4; DDX5
158 EADRMLD 5 DDX17; DDX27; DDX3Y; DDX4; DDX5
159 ADRMLDM 4 DDX17; DDX3Y; DDX4; DDX5
160 DRMLDMG 4 DDX17; DDX3Y DDX4; DDX5
161 RMLDMGF 4 DDX17; DDX3Y; DDX4; DDX5
185 LLFSATF 5 DD19A; DD19B; DDX25; IRK10; Q86XP3
213 PKNSAAE 1 ARFP1
232 RKTELLS 1 CASC5
292 FKDGKLK 1 PGH1
302 ATDIAAR 1 DDX10
303 TDIAARG 1 DDX10
304 DIAARGL 1 DDX10
305 IAARGLD 1 DDX10
306 AARGLDI 10 DDX18; DDX21; DDX24; DDX27; DDX3Y; DDX4; DDX50; DDX54; Q59FR7; Q86XP3
307 ARGLDID 1 Q6ZUM4
328 EDYVHRI 1 DDX17
329 DYVHRIG 1 DDX17
330 YVHRIGR 6 DDX17; DDX1 DDX3Y; DDX41; DDX43; DDX4
331 VHRIGRT 6 DDX17; DDX3Y; DDX41; DDX43; DDX4; Q5VZQ4
332 HRIGRTG 10 DD19A; DD19B; DDX23; DDX25; DDX3Y; DDX41; DDX43; DDX4; DDX52; Q86XP3
332 HRIGRTG 10 DD19A; DD19B; DDX23; DDX25; DDX3Y; DDX41; DDX43; DDX4; DDX52; Q86XP3
333 RIGRTGR 10 DD19A; DD19B; DDX23; DDX25; DDX3Y; DDX41; DDX43; DDX4; DDX52; Q86XP3
334 IGRTGRA 4 DDX23; DDX43; DDX52; Q86XP3
337 TGRAAAT 1 ABCBB
341 AATGKAI 1 SMC3
391 KPKNKAR 1 KI21A
402 GGHGRAD 1 Q92827
438 KSKPARR 1 OR9Q2
446 RKHDDDR 3 Q2QGD7; ZXDA; ZXDB
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Discussion

Using a set of forty bacterial proteomes, this study shows that there does not exist a human protein exempt from a bacterial peptide overlap at the penta- and hexapeptide level and that only 104 proteins out of 36,104 do not host a microbial heptapeptide overlap. This finding is remarkable from a biological point of view and further supports a non-stochastic nature of the peptide overlapping between microbial and human proteomes. Our considerations are the following. Taking heptamer motifs as an example, we calculate that there are 1,280,000,000 possible heptapeptides that theoretically are available and might be used to build a human proteome exempt of bacterial heptapeptides. On the other hand, we know that the human proteome is formed by 36,103 proteins and 15,697,964 occurrences of 10,431,975 unique 7-mers21 and, in the present study we find that only 104 human proteins out of 36,104 do not host a microbial heptapeptide overlap. That is, in face of the enormously high number of potential heptapeptides (1,280,000,000), the human proteome not only presents a high degree of repetitiveness in its 7-mer composition, but also utilizes heptapeptides common to bacteria so that almost no human protein is exempt from bacterial heptapeptide motifs. This peptide commonality has no mathematical justification. There is no shortage of possible heptapeptides, rather an incredibly huge number of heptapeptides are potentially available. Thus, we are forced to conclude that the redundancy present in the protein world is not stochastic (i.e., is not pure random chance), but reflects strong peptide usage bias.21,35,36

BOX 1: List of the human proteins that do no host a bacterial heptapeptide.

CATRl; COAS3; CT187; CU094; FA27L; KR124; KR192; KR211; KR410; KR412; KR413; KR414; KRA42; KRA44; KRA47; KRA81; LCE2B; MTIF; MTIG; MTIM; MT2; MT4; RL41; SPHAR; SPR2A; SPR2B; SPR2D; SPR2E; SPR2F; TRGll; Q2XP30; Q9MY73; Q05CR9; Q05CT7; Q0QVY9; Q6EHZ1; Q6PK85; Q7Z4Q0; Q86YX3; Q9HCX8; Q9P1F9; Q5FC06; Q86XP7; Q8IVI0; Q9NY32; Q9U153; Q6JTU6; Q6ZVA9; Q86TX6; Q81WU1; Q8NI73; Q96EQ2; Q9BXV1; Q9H325; Q5HYP9; Q6GZ88; Q7Z5A1; Q9H3A8; Q9P145; Q9P1I0; Q4VFV5; Q8IVH9; Q9NZ11; Q9P1F8; Q13254; Q6AWA8; Q7Z425; Q96S45; A0A4R1; A0MA52; Q07603; Q8WYR5; Q96Q13; Q9BZU2; Q9NYD4; Q9P1E0; Q9P1E9; Q9UI79; Q147W9; Q6JV79; Q6JV82; Q9HAZ7; A2RUG3; Q96IP2; Q31629; Q495H9; Q5JT78; Q5JVP1; Q5T7W9; Q5TAP0; Q68K28; Q6ZQP6; Q71M31; Q7LCP5; Q7Z4E0; Q86SX0; Q86YX6; Q8NG36; Q8WV73; MORN4; Q96IR5; Q96JR7; Q9BZU0; Q9UI80

Human proteins reported as accession numbers.

In addition, in light of the intensive research dedicated to understanding the function/effect of the presence of a single bacterial match in a human protein looking for pathological correlates,27–34 the present data are striking and seem to overturn our conceptualization of the relationship(s) between microbes and Homo sapiens. Actually, the data reported in this study are logical when analyzed in the light of the phylogenetic background linking bacteria and eukaryotes. Cells are of only two kinds: bacteria (or prokaryotes) and eukaryotes, which evolved from bacteria, possibly as recently as 800–850 My ago. As described in detail by Cavalier-Smith,37 eukaryogenesis involved radical changes in almost every metabolic and structural aspect of the bacterial cell with a reorganization of the membrane and cytoskeleton apparatus and new chromosomal relationships to originate the eukaryotic nucleus and mitotic cycle. In this cell re-organization, new eukaryotic proteins evolved from old bacterial ones. Therefore, we can conclude that the data from Tables 1 and 2 find a proper explanation in the evolutionary history of eukaryotes.

When analyzed from a pathological-clinical point of view, this report is of crucial importance in the study of autoimmune diseases for three reasons. As already discussed above, the data are of special relevance as regards the molecular mimicry hypothesis. Indeed, the molecular mimicry hypothesis suggests that, when bacterial/viral agents share epitopes with a host's protein, an immune response against the infectious agent may result in the formation of cross-reacting antibodies that bind the shared epitopes on the normal cell and result in the auto-destruction of the cell.6 However, the extensive sequence similarity between bacteria and human proteins documented in this study suggests that molecular mimicry between a self and a microbial peptide antigen is inadequate to explain autoimmune pathologies.

Second, this study might contribute to explaining the microbial immune escape phenomenon. Scientific and clinical literature have been and are intensively debating the escape of microbes from immune control. A number of hypotheses and possible mechanisms have been proposed,38–40 albeit with scarce results. Here, the quantitative analysis of n-peptide overlapping of bacterial versus human proteomes reported in Tables 1 and 2 offers a logical and rational explanation to the vexata quaestio of microbial escape from immune surveillance. Indeed, the present data and our past studies20,21 document that microbes are “a portion” of our human self and, consequently, presumably are subject to the same tolerance mechanisms that characterize human antigens and tissues. As a matter of fact, most chronic diseases, including pertussis,41 tuberculosis,42 leishmaniasis,43 periodontitis,44 gastritis,45 to cite only a few of them, occur because an appropriate immune response required for pathogen clearance is not established. This causes a long-term pathogen colonization favored and progressively auto-sustained by pathogen-encoded molecules that enable the suppression of host immune response. The progressively increasing bacterial burden then causes a vicious cycle of bacterial proliferation and host tissue inflammation that translates into tissue damage, impaired function and eventual disease.

The third and most crucial consequence of this study is related to current anti-infectious vaccine preparations. Possibly as a consequence of immunotolerance mechanisms towards repeatedly shared peptide sequences, in general active vaccines produce a weak immune response; also, autoimmune cross-reactions are extremely rare events.46–50 Under normal, non-stimulated conditions, the immune system fails to make immune responses to the infectious antigens present in the vaccines unless adjuvants are added.17,48,49 As a rule, the current active vaccine formulations contain adjuvants to enhance immunogenicity.50–52 The adjuvants serve to activate the immune system against microbial antigens that by themselves do not evoke immune responses, but rather are immunotolerated. However, as demonstrated in this and other studies of ours,20–22 microbial antigens contain a high number of motifs shared with human proteins. Therefore, using viral or bacterial antigens in adjuvanted active vaccines will possibly trigger the immune system to react against the shared motifs (i.e., not only against the microbial antigen(s), but also against human self-molecules) with the concrete risk of developing adverse events and autoimmune pathologies in the human population.53–56

Methods

The human proteome was downloaded from UniProtKB (www.ebi.ac.uk),23 and duplicated sequences and fragments were filtered out. After filtering, we were left with a human proteome consisting of 36,014 unique proteins, for a total of 15,806,702 amino acids. Bacterial proteomes were downloaded from Integr8,23 and each bacterial proteome was filtered in the same manner as the human proteome. The bacteria were chosen based on the following criteria: (1) known to be non-pathogenic or pathogenic; (2) phylogenetically different; (3) have proteomes established to a significant degree of completeness. In addition, the bacterial proteomes were chosen to span a range of proteome sizes, with the smallest bacterial proteome being 450,406 and 306,369 amino acids (for non-pathogenic and pathogenic bacteria, respectively), the largest being 2,582,740 and 1,439,163 amino acids (for non-pathogenic and pathogenic bacteria, respectively).

Sequence similarity analysis of each bacterial proteome to the human proteome was carried out using bacterial n-mers (with n from 5 to 8) sequentially overlapped by 4, 5, 6 and 7 residues, respectively. The scans were performed by custom programs written in C, which utilized suffix trees for efficiency.24 The bacterial proteomes were manipulated and analyzed as follows. Each bacterial proteome was decomposed in silico to a set of penta-, hexa-, hepta- or octamers (including all duplicates). A library of unique penta-, hexa-, hepta- or octamers for each microbial proteome was then created by removing duplicates. Next, for each n-mer in the library, the entire human proteome was searched for instances of the same n-mer. Any such occurrence was termed an overlap or match. Cursory analysis (e.g., identification of unique overlapping n-mers, counts of unique overlapping n-mers, counts of duplications) were performed using LINUX/UNIX shell scripts and standard LINUX/UNIX utilities.

Footnotes

Authors' Contributions

B.T., G.L. and A.S. performed the computational analysis. M.B. and A.K. performed the mathematical analysis and supervised the computational analysis. D.K. proposed the original idea, interpreted the data, developed the research project and wrote the manuscript. All authors discussed the results and revised and commented on the manuscript with a particular contribution from A.K.

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