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The continuity of life and its evolution, we proposed, emerge from an interactive group process manifested in networks of interaction. We term this process \textit{survival-of-the-fitted}. Here, we reason that survival of the fitted results from a natural computational process we term \textit{natural autoencoding}. Natural autoencoding works by retaining repeating biological interactions while non-repeatable interactions disappear. (1) We define a species by its \textit{species interaction code}, which consists of a compact description of the repeating interactions of species organisms with their external and internal environments. Species interaction codes are descriptions recorded in the biological infrastructure that enables repeating interactions. Encoding and decoding are interwoven. (2) Evolution proceeds by natural autoencoding of sustained changes in species interaction codes. DNA is only one element in natural autoencoding. (3) Natural autoencoding accounts for the paradox of genome randomization in sexual reproduction -- recombined genomes are analogous to the diversified inputs required for artificial autoencoding. The increase in entropy generated by genome randomization compensates for the decrease in entropy generated by organized life. (4) Natural autoencoding and artificial autoencoding algorithms manifest defined similarities and differences. Recognition of the importance of fittedness could well serve the future of a humanly livable biosphere., Comment: In version 7 we added various clarifications including to terminology, definitions, and to the reference to the second law of thermodynamics

COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg).

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires., Author summary More than 1,100 cancer treatments are currently in clinical testing. Of these, 295 are immune-oncology medicines and vaccines. For Checkpoint Immunotherapy alone, 52,539 patients were enrolled in 2017. Of these treatments, 108 are for breast cancer, the leading cancer diagnosed in women. Although checkpoint immunotherapy has yet to be proven beneficial in most types of breast cancer, current observations of which specific T cell go into the tumor, and how these cells are connected to the response to treatment, have lead to a new understating of the role of the immune system. Yet, and in spite of this intense research into the role of T cells in tumors, very little is known about how clones of T cells behave during tumor formation. The paper we present here studies this clonal behavior over time. We study it during mammary tumor development in mice. We see that a set of T cell clones, which comes from a diversity of different nucleotide sequences, stands out in its behavior compared with other T cell clones. Importantly, we show that these mouse T cell clones have a strong connection to human breast cancer. The specific identification of a subset of T cell clones, together with the behavior of these clones over time, is critical to our understanding of the effect of modifications to the immune system, such as the modifications made by immunotherapy treatments, and to our understanding of how to better control the behavior of the immune system.

An association between T‐cell lymphopenia and autoimmunity has long been proposed, but it remains to be elucidated whether T‐cell lymphopenia affects B‐cell responses to autoantigens. Human neonatal thymectomy (Tx) results in a decrease in T‐cell numbers and we used this model to study the development of autoreactivity. Two cohorts of neonatally thymectomized individuals were examined, a cohort of young (1–5 years post‐Tx, n = 10–27) and older children (>10 years, n = 26), and compared to healthy age‐matched controls. T‐cell and B‐cell subsets were assessed and autoantibody profiling performed. Early post‐Tx, a decrease in T‐cell numbers (2.75 × 109/L vs. 0.71 × 109/L) and an increased proportion of memory T cells (19.72 vs. 57.43%) were observed. The presence of autoantibodies was correlated with an increased proportion of memory T cells in thymectomized children. No differences were seen in percentages of different B‐cell subsets between the groups. The autoantigen microarray showed a skewed autoantibody response after Tx. In the cohort of older individuals, autoantibodies were present in 62% of the thymectomized children, while they were found in only 33% of the healthy controls. Overall, our data suggest that neonatal Tx skews the autoantibody profile. Preferential expansion and preservation of Treg (regulatory T) cell stability and function, may contribute to preventing autoimmune disease development after Tx.

Objective To address whether a targeted modulation of the abnormal expression of Hsp70 and autoantibodies against this molecule in systemic lupus erythematosus can influence disease. Methods Lupus-prone (NZB × NZW)F1 mice that had been DNA-vaccinated with plasmids encoding Hsp70 and controls were monitored for lupus disease parameters including anti-double stranded DNA (anti-dsDNA) autoantibodies and cytokines using enzyme-linked immunosorbent assay, and for kidney function and pathology. The phenotypic and numerical changes in relevant immune cells were evaluated by flow cytometry, and cell function was assessed. Results Mice that had been DNA-vaccinated with Hsp70 displayed marked suppression of anti-dsDNA antibody production, reduced renal disease, and antiinflammatory responses that are associated with a significantly extended survival, compared to controls. These protective effects in Hsp70-vaccinated mice were associated with an induction of tolerogenic immune responses and an expansion of functional Treg cells. Conclusion DNA vaccination with Hsp70 suppresses murine lupus by inducing tolerogenic immune responses and antiinflammatory immune responses associated with reduced disease manifestations and increased mouse survival.

Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points: The immune system classifies healthy and pathologic states of the body-including both self and foreign elements-by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions.Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom.Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions-such collective inflammatory responses are dynamically responsive.Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms. We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions.

Living organisms are composed of cells and all living cells contain a genome, the organism's stock of deoxyribonucleic acid (DNA). The role of the genome has been likened to a program that encodes the organism's development and its subsequent response to the environment. This computer metaphor teaches that the organism – the hardware – is controlled by the software – the genome. Thus, the organism and its fate can be explained by genetics, the plans written into the sequence of genomic DNA; the Genome Project was devised to decipher this program. However, it is now clear that the genome does not directly program the organism; the computer program metaphor has misled us. The genome is only one class of vital information that serves the organism. Metaphorically, the genome can be likened to a vocabulary of words that can be deployed to make meaningful sentences, or to a toolbox for accomplishing specific tasks. Key concepts: The genome does not function as a master plan or computer program for controlling the organism; the genome is the organism's servant, not its master. The genome is a reservoir of DNA sequence information and a vehicle for transmitting this information; the meaning of DNA emerges from the cellular processing of this raw information into proteins and other functional molecules. DNA is only one class of vital information that serves the organism; the organism epigenetically uses, manipulates, regulates and, in the case of the immune system, creates genes. The effects of a gene vary with the organism's environment; the interactions between genes and environment are not linear and, in most cases, not additive. Therefore, one cannot compute with certainty the relative contributions of genes and environment to an organism's observed features – its phenotype. Metaphorically, we can think of the genome as akin to a list of words, a vocabulary, which can be used to build and express a meaningful language; like a vocabulary, a genome by itself has no functional meaning. The genome is thus akin to a toolbox of DNA sequences that provide molecular tools as requested by the internal state of the organism and the state of the environment. One's genes cannot explain one's being: an organism is the expression of a dynamic and ongoing interaction between the state of its environment and its internal state, which includes its past history and its toolbox of DNA sequences. Keywords: genetics; computer program; evolution; information; complexity

We describe here the development, verification and validation of the SLE-key ® rule-out test for a definitive rule-out of a diagnosis of systemic lupus erythematosus (SLE). The test uses the proprietary iCHIP ® micro-array technology platform (Fattal et al., 2010) to identify discriminating patterns of circulating autoantibodies among SLE patients compared with self-declared healthy individuals. Given the challenges associated with the diagnosis of SLE and the healthcare costs of delayed diagnosis and misdiagnosis, a definitive rule-out test can provide significant clinical benefits to patients and potentially major cost savings to healthcare systems.

Alzheimer's disease (AD) is a neurodegenerative disorder which is the most prevalent cause of dementia in the western world. Currently, it is the most expensive disease in America, costing more than heart diseases and cancer and as the world population is getting older it is expected to become the most expensive medical disorder in the world. AD is characterized by three core pathologies: accumulation of amyloid β (Aβ) plaques, neurofibrillary tangles (NFT) and sustained inflammation. It is now believed that inflammation provides the link between Aβ and NFT. The immune system is therefore, a major player in the pathogenesis of AD. Here we propose that Bacillus Calmette-Guerin (BCG) could affect the incidence of AD. Bacillus Calmette-Guerin (BCG) is a live attenuated Mycobacterium bovis preparation first developed as a vaccine against M. tuberculosis. It has been shown to be moderately effective in preventing tuberculosis, while noted to induce modifications in inflammatory response and to regulate the immune system. Intra-vesical administration of BCG is used successfully in the past four decades to prevent recurrence of non-muscle invasive bladder cancer. In this manuscript we investigate the hypothesis that exposure to BCG decreases the prevalence of AD in elderly population and that this occurs through modulation of the immune system. Our hypothesis is based on several lines of evidence: lower prevalence of AD in countries with high BCG coverage, ability of BCG to ameliorate several conditions involving the immune system like type 1 diabetes mellitus and multiple sclerosis, animal models of AD in which BCG shows therapeutic potential and a plausible molecular mechanism which may be the basis for this hypothesis. Namely, elevated systemic levels of IL-2 (as found when BCG is given intra-vesically) that amplify Treg cells that inhibit AD associated inflammation, decreased plaque formation and restore cognitive function. To test this hypothesis one may study cognition in the large available "natural adult population" exposed to high dose of BCG through the bladder. Bladder cancer survivors not given BCG can serve as control group. This population can be used without adding any medical intervention.

Cancer immunotherapy by checkpoint blockade proves that an effective immune response to a tumor can be induced clinically. However, little is known about the evolution of tumor-associated T-cell receptor (TCR) repertoires without intervention. Here we studied TCR repertoire evolution in mice spontaneously developing mammary tumors; we sequenced peripheral blood alpha and beta TCRs of CD4+CD62L+CD44− T cells monthly for 8 months in 10 FVB/NJ mice transgenic at the Erbb2 locus, all developing tumors; 5 FVB/NJ mice without the transgene were age-matched controls. Sequences were either private (restricted to one mouse) or public (shared among mice); public sequences were either exclusive to the tumor group or inclusive among different groups. We now report that 1), public AA sequences were each encoded by many different nucleotide sequences (NT) recombinations (convergent recombination; CR); 2) mice developing tumors evolved tumor-exclusive public sequences, derived initially from private or from inclusive public sequences; and 3) tumor-exclusive public sequences in mice were also present among published public TCR sequences from human breast cancer patients. These cross-species tumor-exclusive TCR sequences manifested high CR; but the AA sequences shared by mice and humans did not share NT sequences. Thus, tumor-exclusive TCR AA sequences across species are selected from different NT recombination events. The roles of tumor-exclusive TCR repertoires in advancing or inhibiting tumor development and the effects of tumor immunotherapy on these T cells remain to be seen.

OBJECTIVE: We previously described the multiplex autoantibody SLE-key Rule-Out test, which detects a signature of autoantibody reactivity that distinguishes healthy subjects from SLE patients with 94% sensitivity, 75% specificity and 93% negative predictive value; thus, an individual manifesting a positive Rule-Out test score is unlikely to have SLE (e.g. lupus is excluded). The objective of this current study was to evaluate the stability of the lupus-associated signature over time. METHODS: We used banked serum samples from healthy subjects (n = 51) and lupus patients (n = 50 individual samples and n = 181 paired samples, for a total of n = 412 serum samples). The samples were drawn at different times after diagnosis to analyse the impact on the SLE-key Rule-Out test of time elapsed since diagnosis and any changes in disease activity (as reflected by the SLEDAI score). RESULTS: The SLE signature remains stable for the first 10 years after diagnosis; in this time frame

To enable microbial colonization of the gut mucosa, the intestinal immune system must not only react to danger signals but also recognize cues that indicate safety. Recognition of safety, paradoxically, is mediated by the same environmental sensors that are involved in signaling danger. Indeed, in addition to their well-established role in inducing inflammation in response to stress signals, pattern recognition receptors and a variety of metabolic sensors also promote gut-microbiota symbiosis by responding to "microbial symbiosis factors", "resolution-associated molecular patterns", markers of energy extraction and other signals indicating the absence of pathogenic infection and tissue damage. Here we focus on how the paradoxical roles of immune receptors and other environmental sensors define the microbiota signature of an individual.

In this work, we studied autoantibody repertoires and Ig isotypes in 71 mothers and their 104 healthy newborns (including twins and triplets delivered term or premature). Newborns receive maternal IgG Abs via the placenta before birth, but developing infants must produce their own IgM and IgA Abs. We used an Ag microarray analysis to detect binding to a selection of 295 self-Ags, compared with 27 standard foreign Ags. The magnitude of binding to specific self-Ags was found to be not less than that to the foreign Ags. As expected, each newborn shared with its mother a similar IgG repertoire—manifest as early as the 24th week of gestation. IgM and IgA autoantibody repertoires in cord sera were highly correlated among the newborns and differed from their mothers’ repertoires; the latter differed in sera and milk. The autoantibodies bound to self-Ags known to be associated with tumors and to autoimmune diseases. Thus, autoantibody repertoires in healthy humans—the immunological homunculus—arise congenitally, differ in maternal milk and sera, and mark the potential of the immune system to attack tumors, beneficially, or healthy tissues, harmfully; regulation of the tissue site, the dynamics, and the response phenotype of homuncular autoimmunity very likely affects health.

Background The SLE-key® RuleOut iCHIP® antigen microarray-based test rules out a diagnosis of SLE with a sensitivity of 94%1. Objectives Here we report the use of the iCHIP® platform and a set of synthetic oligonucleotide antigens to distinguish between SLE subjects and those with a diagnosis of Rheumatoid Arthritis (RA), Scleroderma (SSc), Sjogren9s syndrome (SS), or healthy individuals (HC). Methods We examined IgM and IgG antibody binding to 22 synthetic oligonucleotides (44 features) in the sera of HC subjects (N=40); SLE (N=30); SSc (N=40); SS (N=20); or RA (N=30) patients. Univariate analysis (FDR adjusted p-values) was used to determine the ability of each feature to separate between SLE and the different classes of subjects. Results Table 1 shows that multiple oligonucleotides successfully distinguished SLE patients from all other groups. All significant features were IgG antibodies, except for 1 IgM. Table 2 shows the impact of single nucleotide change on autoantibody binding. PolyG (G17) separates SLE from all but SS. T1G16 separates SLE from HC subjects, while G16T1 gave no significant separation. The addition of a G to the 59 and 39 end of T16 enhanced IgG antibody binding and improved separation between SLE and other autoimmune diseases with at least 10-fold improved significance as compared to T20. PolyG sequence length impacts the ability of the oligonucleotides to separate between SLE and the other groups (Fig. 1A). Unexpectedly, sequences either shorter or longer than G14 were effective in separating SLE from HC, RA, and SSc, while G14 was not effective. Furthermore, none of the polyG homopolymers could separate SLE from SS. Sequences rich in C or T were more effective at separating between SLE and SS patients (Fig. 1B). Conclusions Autoantibody binding to oligonucleotides can be used to differentiate SLE from other autoimmune conditions and healthy subjects. The structural basis for the differences in binding of antibodies from disease sera to the various oligonucleotides is not yet understood, but may be due to immunologically unique conformations and secondary structures of oligonucleotides of defined length and sequence. SSc can be differentiated from SLE based on particular antibody binding to epitopes of oligonucleotides containing C and T. RA can be differentiated from SLE more significantly than the other autoimmune conditions. The iCHIP® microarray technology is being further developed to generate a clinically useful test to rule in a diagnosis of SLE relative to other related autoimmune diseases. References Putterman et al., J Immunol Methods 2016. Fattal et al., Immunology, 2010. Fattal et al., Immunology, 2015. Acknowledgements Authors wish to acknowledge Cohen-Gindi O, Lerner M, Tarnapolski O, Blumenstein Y, Javaherian A, Pitts J, Barton M and Wong E. and the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115308 BIOVACSAFE. Disclosure of Interest C. Putterman Consultant for: ImmunArray, A. Gabrielli: None declared, A. Balbir-Gurman: None declared, P. Safer Employee of: ImmunArray, K. Jakobi-Brook: None declared, R. Sorek Employee of: ImmunArray, I. Gluzman Employee of: ImmunArray, S. Wallace Employee of: ImmunArray, I. Cohen Shareholder of: ImmunArray, Consultant for: ImmunArray

Background We previously described the SLE-key® RuleOut test1,2 to rule out the presence of systemic lupus erythematosus (SLE) with 94% sensitivity, 75% specificity, and 93% negative predictive value. We also reported that the SLE-key® signature appeared to be independent of disease activity or duration3 suggesting4 that that the SLE-key® signature might persist over time in the same subject. Objectives Here we report that the SLE-key® signature remains stable over time in paired samples drawn from most individual subjects, regardless of disease activity. Methods We determined the SLE-key® RuleOut scores for 113 paired serum samples submitted by clinics specializing in SLE. SLEDAI scores at the time of the blood draw ranged from 0 to 22. The mean SLEDAI difference within the pairs was 2.7±6.3. Samples were collected from subjects with a T1-T2 time difference that ranged from 0 to 11.5 years (mean =2±2.6 years). Results The SLE-Key® RuleOut test identifies an SLE-specific signature based on a profile of autoantibodies to a combination of nucleic acids (complex ssDNA and a defined oligonucleotide) and protein biomarkers. Patients with an SLE-key® score of >0.18 are considered not ruled out for a diagnosis of SLE. In 84% of paired samples, patients9 SLE-key® scores remained essentially the same (Figure 1, closed circles). The scores for these subjects were stable, persistent, and independent of SLEDAI score or time between sampling. Significant changes in the SLE-key® scores of 18/113 patient pairs (open circles) appear to be independent of time between blood draws and change in SLEDAI score. In 7 cases there was a change in Rule Out status of the patients. In 3 cases, both scores were close to the 0.18 threshold and the change was deemed not significant. In 4 cases, patients9 status changed from RuledOut to Not Ruled Out, but with no correlation to change in SLEDAI score or time between sampling dates. Records of patients with changing SLE-key® scores are being studied to determine the reasons and the clinical implications of the change. Conclusions The SLE-key® RuleOut test detects a serologic signature which remains stable between sampling dates and over a long period of time after diagnosis in 84% of subjects. Subjects who were ruled out at T1 were generally ruled out at T2. Patients not ruled out at T1 remained not ruled out at T2. The clinical implications of a changing SLE-key® RuleOut score in the remaining 16% of patients may be meaningful, and are currently being carefully investigated. References Fattal et al; Immunology 2010. Putterman et al., J Immunol Methods, 2016. Putterman et al., Lupus 2016. Cohen IR, Lupus Open Access 2016. Acknowledgements The authors wish to acknowledge the invaluable contributions of Cohen-Gindi O, Lerner M, Tarnapolski O, Blumenstein Y, Javaherian A, Pitts J, Barton M and Wong E and Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115308 BIOVACSAFE. Disclosure of Interest C. Putterman Consultant for: ImmunArray, M. Petri: None declared, R. Caricchio: None declared, P. Safer Employee of: ImmunArray, K. Jakobi Employee of: ImmunArray, R. Sorek Employee of: ImmunArray, I. Gluzman Employee of: ImmunArray, S. Wallace Employee of: ImmunArray, I. Cohen Shareholder of: ImmunArray, Consultant for: ImmunArray

Myeloid differentiation factor 88 (MyD88) recruits signaling proteins to the intracellular domain of receptors belonging to the toll-like/interleukin-1 (IL-1) receptor superfamily. Mice lacking MyD88 are highly susceptible to infectious diseases, but tend to resist experimentally induced autoimmune diseases such as experimental autoimmune encephalomyelitis (EAE) and manifest diminished allograft rejection. We reasoned that inhibition of MyD88 should influence the cytokine profile of responding T cells by blocking costimulatory molecule expression by antigen-presenting cells (APCs) and by inhibiting T-cell responses to IL-18. We now report that inhibition of MyD88 in human APCs led to decreased IFNγ and IL-17 production and a shift to IL-4 production by responding T cells in a mixed lymphocyte reaction. Direct inhibition of Myd88 in mouse and human T cells also reduced their production of IFNγ in response to IL-12/IL-18 stimulation. Finally, systemic MyD88 antagonism significantly reduced the clinical manifestations of EAE in mice. Thus, MyD88 appears to be a key factor in determining T cell phenotype and represents a potential target for therapeutic intervention.