Arabidopsis genome revealed that Pseudoviridae insert randomly along the chromosome and have been recently active whereas Metaviridae were more active in the past and preferentially target heterochromatin.An analysis of the distribution and age of LTR retrotransposons in the  Genome evolution and size variation in multicellular organisms are profoundly influenced by the activity of retrotransposons. In higher eukaryotes with compact genomes retrotransposons are found in lower copy numbers than in larger genomes, which could be due to either suppression of transposition or to elimination of insertions, and are non-randomly distributed along the chromosomes. The evolutionary mechanisms constraining retrotransposon copy number and chromosomal distribution are still poorly understood.Arabidopsis thaliana genome, using an automated method for obtaining genome-wide, age and physical distribution profiles for different groups of elements, and then comparing the distributions of young and old insertions. Elements of the Pseudoviridae family insert randomly along the chromosomes and have been recently active, but insertions tend to be lost from euchromatic regions where they are less likely to fix, with a half-life estimated at approximately 470,000 years. In contrast, members of the Metaviridae (particularly Athila) preferentially target heterochromatin, and were more active in the past.I investigated the evolutionary dynamics of long terminal repeat (LTR)-retrotransposons in the compact A. thaliana, their non-random genomic distribution is due to both selection against insertions in euchromatin and preferential targeting of heterochromatin. Constant turnover of euchromatic insertions and a decline in activity for the elements that target heterochromatin have both limited the contribution of retrotransposon DNA to genome size expansion in A. thaliana.Diverse evolutionary mechanisms have constrained both the copy number and chromosomal distribution of retrotransposons within a single genome. In  Drosophila melanogaster [Ty1/Copia elements) and Metaviridae (Ty3/Gypsy).It has become increasingly clear that the activity of transposable elements (TEs) is a major cause of genome evolution. TEs are ubiquitous components of eukaryotic genomes. For example, 22% of the nogaster , 45% of nogaster  genomes nogaster . In plannogaster  and/or anogaster ,8-10. LTThe evolutionary forces that control copy number and shape the chromosomal distribution of different kinds of TEs in eukaryotic genomes are still poorly understood. Some large plant and animal genomes have expanded owing to an ability to tolerate massive amplification of retrotransposons, whereas in more compact genomes these elements are found in lower copy numbers, non-randomly distributed and mainly confined to heterochromatic regions -14. TEs It is often hypothesized that the non-random genomic distribution of TEs in some species reflects the action of purifying selection on the host against the deleterious effects of TE insertions in certain regions. Models differ in the kind of deleterious effects they propose: chromosomal rearrangements due to 'ectopic'  recombination ; disruptArabidopsis thaliana genome. The total TE content of the compact genome of A. thaliana, with a haploid size of approximately 150 Mbp (million base-pairs), has been previously estimated as around 10%, and is known to cluster around the pericentromeric heterochromatin [A. thaliana [A. thaliana. By comparing the age and chromosomal distributions of young and old insertions it is possible to distinguish between preferential targeting and passive accumulation of elements into heterochromatin. I show that members of the Pseudoviridae have recently been active, that they integrate randomly into the genome (relative to centromere location) and only passively accumulate in proximal regions, as purifying selection eliminates euchromatic insertions. In contrast, the Metaviridae (particularly members of the Athila group) preferentially insert into the pericentromeric heterochromatin, and their transpositional activity has declined in the last million years.To evaluate these hypotheses, I investigated the evolutionary history of different groups of LTR-RTs in the hromatin . Despitethaliana ,23. I haAthila superfamily, though the core centromere sequences themselves were not available. In fact, the size of the A. thaliana genome has been recently estimated as approximately 157 Mbp (around 20% larger than the estimate published with the genome sequence), and the additional size appears to be due to (unsequenced) heterochromatic repetitive DNA in the centromeres, telomeres and nucleolar-organizing regions [Athila is the most abundant superfamily, followed by the Copia-like, Gypsy-like, and TRIM . The ratio of solo-LTRs to complete elements is around 2:1. In addition to solo-LTR formation, deletion and fragmentation of retrotransposon DNA in A. thaliana also occur via other mechanisms: 36% of the DNA in the Athila, 38% in the Gypsy-like, 32% in the Copia-like, and 21% in the TRIM superfamilies correspond to degraded insertions that are neither 'complete' elements nor solo-LTRs.Most of the retrieved elements are fragmented and truncated, and nested insertions are common particularly among pericentromeric elements belonging to the  regions .  Table Katydid-At1 and Katydid-At2, most intra-element LTR pairs did not cluster. In contrast, when trees were constructed for representatives of the Athila (athila2), Gypsy-like (atlantys2), and Copia-like  superfamilies, intra-element LTR pairs always clustered (data not shown), providing evidence for the lack of inter-element recombination in those 'families'.To obtain the genome-wide age distribution of each superfamily (except TRIM), 564 pairs of intra-element LTRs were (pairwise) aligned and their sequence divergence estimated. Many of the complete TRIM elements have highly divergent LTRs, and I suspect that extensive recombination between inter-element LTRs has occurred. In neighbor-joining trees of LTR sequences (of both complete and solo elements) from the TRIM families Athila insertions are significantly older than the Gypsy-like , Gypsy-like older than Copia-like (p < 0.0001). Age distributions are summarized in Figure The superfamilies differ significantly in their average age of insertions. p < 0.000. Age dis-8 substitutions per site per year [Copia-like elements are younger than 3 million years (Myr), 90% younger than 2 Myr, and only two insertions estimated to be older than 4 Myr. This shows that complete insertions from the known Copia-like families in the A. thaliana genome are younger than the species itself, whose time of divergence from its closest relatives, such as A. lyrata has been estimated (with the same rate of evolution) to be 5.1-5.4 Myr ago [Athila (and the Gypsy-like TEs), as 7% of 219 intra-element LTR pairs were estimated to be older than 5 Myr (3% of the Gypsy-like). Furthermore, the Athila and Gypsy-like superfamilies have an excess of degraded insertions relative to Copia-like  has been shown to occur in A. thaliana [Arabidopsis are not evolving in a completely neutral fashion [Chs) and alcohol dehydrogenase (Adh) loci, their synonymous sites would be evolving more slowly than LTR-RT fossils, and the dating method described above would systematically overestimate the ages of their insertion events.Using the rate of 1.5 × 10per year , 97% of  Myr ago . The sitthaliana , and the fashion . If thisCopia-like elements appears to show a recent burst of activity  in  regions . HoweverAthila elements are almost exclusively inserted in the pericentromeric regions, and the other superfamilies in significantly and progressively less proximal regions of the chromosome arms . Furthermore, except for TRIM, within each superfamily the solo-LTRs are significantly more distal than the complete elements , suggesting that formation of solo-LTRs is more likely to occur in distal regions. The distribution of complete TRIM elements relative to the centromere is not significantly different from random , although sample size is small, while their solo-LTRs are significantly clustered .A. thaliana would affect age comparisons among different groups of elements, as they show different degrees of clustering into the pericentromeric heterochromatin. However, older estimates for the age of heterochromatic elements are consistent with the hypothesis that heterochromatin is a 'safe haven' where TE insertions persist for longer periods of time. Here I show that the mechanisms that led to the accumulation of LTR-RTs in proximal regions are distinct for different groups: elements of the youngest superfamily (Copia-like) insert randomly into the genome (relative to the location of the pericentromeric heterochromatin), but there is negative selection (on the host genome) against their insertions in euchromatin; elements of the older superfamilies  preferentially insert into the pericentromeric regions. These distinct mechanisms become apparent when temporal and spatial data are combined  regions are more likely to persist for longer periods of time. This interpretation assumes that the neutral mutation rate is the same for both the distal (euchromatic) and proximal (heterochromatic) portions of the genome. Intra-genomic variation in the per-replication mutation rate has been reported between the two sex chromosomes of a flowering plant  . Furthermore, the distribution along the chromosome arms of 34 Copia-like insertions with no divergence between their intra-element LTRs is not significantly different from random . This is evidence that Copia-like elements integrate randomly relative to the location of the centromeres, but tend to get eliminated from distal, and passively accumulate in proximal regions.For complete t) for a neutral allele is given by t = 4Ne, where Ne is the effective population size. For A. thaliana t can be estimated using an average of estimates of nucleotide diversity (θ) for 8 different A. thaliana genes, θ = 9 × 10-3 [μ = 1.5 × 10-8 [t = 2θ/μ, yielding an estimate of t ≈ 1.2 Myr. This value for t is consistent with an independent estimate that placed the time since the divergence between A. thaliana and A. lyrata between 3.45t and 5.6t [Copia-like insertions are younger than 1.2 Myr, most of them are likely to be polymorphic. Taken together with the highly significant negative correlation between age and distance from the pericentromeric regions, these results indicate that complete Copia-like insertions are less likely to get fixed in the distal, euchromatic portions of the chromosome arms than in the pericentromeric heterochromatin.The average time to fixation , as both young and old insertions are found only in proximal regions . If the arm is excluded from the analysis there is no significant correlation . This suggests that for the Gypsy-like also there is an insertional bias towards proximal regions. This bias is not as strong as for Athila, as complete Gypsy-like insertions are not exclusively found around the centromeres, and they cluster (to a much lesser extent) in at least one other heterochromatic region (the telomere of the short arm of chromosome II). Included in the Gypsy-like 'superfamily' is a clade of elements, known as Tat, which is a sister group to Athila to the exclusion of the remaining Gypsy-like elements [Tat does not differ from those of the remaining Gypsy-like elements ; Tat show insertion bias towards the pericentromeric regions, but again to a lesser degree than Athila.elements . The ageCopia-like elements have been active until recently but tend to be eliminated by purifying selection, their age distribution  = NoeaK-, where N(K) is the number of elements observed with intra-element LTR divergence K, and No and a are constants to be fitted. The rate of elimination can then be estimated by linear regression of the log-transformed data . Figure Copia-like insertions (R2 = 0.94), and for complete insertions outside the proximal regions . Complete Copia-like elements are eliminated from the genome with a half-life of 648,000 years . Insertions exclusively outside the proximal (heterochromatic) regions are lost more rapidly, with a half-life of 472,000 years .Given that n Figure , bottom A. thaliana the accumulation of insertions in the pericentromeric heterochromatin is the result of both insertion bias (for Metaviridae elements) and a lower probability of fixation in euchromatin (Pseudoviridae).The results above indicate that within a single genome, distinct evolutionary mechanisms can lead to the non-random distribution of retrotransposons, as in A. thaliana were already present in its common ancestor with Brassica oleracea (the two species diverged around 15-20 Myr ago), and that copy numbers are generally higher in B. oleracea [A. thaliana and B. oleracea was responsible for the larger genome of the latter. Here I have shown that the major LTR-RT families have been active in A. thaliana since its divergence from its closest relatives, such as A. lyrata. The transpositional activity of Metaviridae elements has declined relative to its level between 1 and 2 Myr ago, perhaps suggesting that the host genome has more efficiently suppressed their transposition since. However, Pseudoviridae (Copia-like) elements in A. thaliana have been subject to constant turnover. They have been recently active and show no insertion bias, and I estimate that the half-life of a complete element inserted in the euchromatic (non-coding) regions of the chromosome arms as around 470,000 years. Most of these Pseudoviridae insertions are lost before they reach fixation, and the half-life estimate provides a measure of the pace at which natural selection on the host constrains the genomic distribution and copy number of Pseudoviridae insertions. Turnover of Pseudoviridae insertions, in contrast to the longer persistence of Metaviridae elements that have declined in activity, is consistent with the higher sequence diversity among the Pseudoviridae than the Metaviridae in A. thaliana (107 Repbase update (RU) 'families' represented in 215 complete Pseudoviridaeelements, 25 RU 'families' in 349 complete Metaviridae elements, where 'families' were defined on the basis of sequence divergence); frequent reverse transcription during transposition would be likely to lead to faster evolution than that generated by the host genome DNA polymerase error rate on chromosomal insertions.It has recently been shown that most TE lineages in oleracea . The autA. thaliana genome does not correlate with local recombination rate [The lower probability of fixation in euchromatin relative to heterochromatin implies that insertions into euchromatin are more deleterious to the host (and perhaps that purifying selection is less efficient in heterochromatin due to a much reduced rate of recombination). TE density in the ion rate , providiion rate .Copia-like elements, if such a process occurred more often in the euchromatic than in the pericentromeric regions of the genome, and if it were frequent enough. One mechanism that removes LTR-RT DNA from the genome is solo-LTR formation via unequal homologous recombination between intra-element LTRs. However, this mechanism cannot be the driving force shaping the distribution of complete Copia-like elements because Copia-like solo-LTRs are also non-randomly distributed and clustered in proximal regions . Copia-like solo-LTRs are either eliminated faster from distal than proximal regions, like complete elements, or solo-LTR formation on average occurs more slowly than extinction for euchromatic insertions. Despite clustering around the centromeres, Copia-like solo-LTRs are significantly more dispersed than complete elements. This suggests that solo-LTRs do form before extinction for distal insertions, but are probably less efficiently eliminated (possibly because they are less deleterious to the host genome) than complete elements. Another known mechanism of  DNA loss operates via small deletions due to illegitimate recombination (between short repeats); this has been shown to occur in the A. thaliana genome by an analysis of internal deletions in LTR-RTs [Drosophila, rates of spontaneous deletions in euchromatin and heterochromatin do not seem to differ [A. thaliana the relative rates between the two chromatin domains are unknown, but fragmented  Copia-like insertions are as clustered around the centromeres as complete ones . Therefore small, spontaneous deletions cannot account for the genomic distribution of complete elements. Larger deletions (that remove the entire LTR-RT sequence) occurring primarily in euchromatin would be necessary to explain the observed accumulation pattern; if such a mechanism existed it would be an important force for genome size contraction. As there is no evidence for such mechanism, and given that I estimate that the half-life of (complete) insertions to be less than half the average time to fixation for a neutral allele, a lower probability of fixation in euchromatin relative to the pericentromeric heterochromatin is more likely to be driving the genomic distribution of Pseudoviridae elements.As an alternative to selection, a neutral mutational process that deletes (part of the) insertions could in principle be driving the distribution of  LTR-RTs . In Droso differ . In A. tAthila elements have been known to be present in the A. thaliana core centromeric arrays of the 180-bp satellite repeats and are abundant in pericentromeric heterochromatin [Copia-like elements, the striking compartmentalization of both recent and older Athila insertions in the pericentromeric heterochromatin indicates that these elements actively target those regions, and represents an example of a group of retrotransposons that have evolved to colonize a particular 'genomic niche'. Passive accumulation could not explain the distribution of Athila insertions unless they were generally much more deleterious to their host than Copia-like ones. Given the absence of complete Athila insertions from euchromatin, any one insertion would have to be so deleterious as to be almost immediately eliminated by purifying selection, even from intergenic DNA. Rather, it is likely that Athila elements preferentially insert into the pericentromeric heterochromatin and it is possible that this group of elements has been co-opted to play a part in centromere function. There is some evidence that such hypothetical role cannot be that of cis-acting sequences [A. thaliana [It is interesting to note that the integrase proteins encoded by LTR-RTs differ between the Pseudoviridae and the Metaviridae in their carboxy-terminal domains, as they have different characteristic motifs ,36. Thishromatin ,41. In tequences , but it equences -45 pointequences , centromequences -51, chicequences  and A. tthaliana .A. thaliana. The rapid and recent genome evolution inferred for A. thaliana may be a feature common to other higher eukaryotes, in particular those with compact genomes. High turnover of TE insertions in euchromatin also occurs in Drosophila and pufferfish [A. thaliana, be due to diverse evolutionary mechanisms.Both purifying selection (at the host level) against insertions (in euchromatin) and a decline in transpositional activity (of Metaviridae elements) appear to have limited the recent contribution of retrotransposon DNA to genome size expansion in fferfish , for exaA. thaliana.A methodology was developed for the automated mining of sequence data to retrieve the sequence and chromosomal location of genomic 'fossils' of LTR-RTs, identifying complete elements and solo-LTRs among the retrieved sequence fragments, and estimating the age of the insertion events that gave origin to these elements. This methodology was applied to the genome sequence of A. thaliana genome (around 116 Mbp of available sequence) was performed using sequence-similarity search algorithms [A. thaliana 'families' of LTR-RTs. The programs RepeatMasker [Sequences of the organellar and the five nuclear chromosomes (version 200303) were obtained from the Munich Information Center for Protein Sequences (MIPS) . Computagorithms  against gorithms ,57 sequeatMasker  and WU-BatMasker  were useAthila, Gypsy-like , and Copia-like . The Metaviridae was split into two groups (Athila and Gypsy-like), as initial mining of the A. thaliana genome revealed that Athila elements have been particularly successful in colonizing it. Their copy number is roughly double the number of all other members of the Metaviridae, and higher than the total of all Pseudoviridae elements. Athila form a clade and are retroviral-like elements that are likely to have an envelope (env) gene [Copia- and Gypsy-like elements are typical LTR-RTs, although one of the Copia-like 'families' (metaI) comprises non-autonomous elements [endovir1 [atcopia41-43 [env gene. A fourth 'superfamily' was used to include TRIMs. These are short, non-autonomous elements that feature LTRs but no coding genes and cannot currently be classified into either the Pseudoviridae or the Metaviridae; they are described in ['Families' of LTR-retrotransposons (as classified in Repbase update) are present in low copy numbers; therefore, for the purpose of this analysis they were grouped into three 'superfamilies': nv) gene . Most ofelements  and a feendovir1 , atcopiapia41-43 ) are retribed in .Athila (10 families): athila2 - 5, athila4A - D, athila6A, athila7, athila8A and B; Gypsy-like (15 families): atgagpol1, atgp2 and 3, atgp2N, atgp5 - 10, atgp9B, atlantys1 - 3, tat1; Copia-like (107 families): atcopia1 - 97, atcopia8A and B, atcopia18A, atcopia32B, atcopia38A and B, atcopia65A, endovir1, TA1-2, meta1; TRIM (3 families): katydid-At1, katydid-At2, katydid-At3.The four superfamilies comprise the following 'families'. Perl script, LTR_MINER (available on request), was written to parse all the chromosomal LTR-RT fragments reported by RepeatMasker (WU-BLAST hits of similarity to reference sequences) and identify complete elements and solo-LTRs. LTR_MINER performs the pattern-recognition function of assembling hits that originated from single LTR-RT insertion events. The algorithm involves: 'defragmentation' of LTR hits. If a chromosomal LTR fossil contains insertions/deletions (indels) relative to the most similar library sequence, it may be reported as multiple hits (fragments). Defragmentation is the identification of multiple hits that correspond to the same LTR. Parameters were set so that LTR hits were defragmented only when they were separated by no more than 550 bp, belonged to the same family, had the same orientation on the chromosome, and their combined length did not exceed the length of the corresponding family reference sequence by more than 20 bp.A LTR, IR, LTR. In order to check whether the pattern could be straddling a nested insertion of the same family, the search is then recursively extended from each end of the pattern for further contiguous hits to an IR and a LTR (of same family and orientation). The two LTRs of the innermost pattern are classified as a pair of intra-element LTRs.An intact LTR-RT insertion consists of at least three hits: LTR-IR-LTR . After LTR defragmentation, LTR_MINER searches for contiguous patterns of LTR_MINER also identifies such elements provided an IR is present between the LTRs. A maximum pairing distance between LTRs was set at 30 kb.LTR_MINER was set to classify a LTR fragment as a solo-LTR if no other LTR or IR (of same family and orientation) is present within a 5 kbp radius from the fragment's ends. The aim was the identification of elements resulting from deletion (of the IR and one LTR) events via homologous recombination between intra-element LTRs, and not to classify as solo-LTRs sequences that are separated from IRs because of insertions.K) between each intra-element LTR pair was then estimated using Kimura's two-parameter model [Nucleotide sequence divergence between pairs of intra-element LTRs was used as a molecular clock, as these pairs are identical at the time of insertion . All miner model . To reduK greater than 0.2 they were inspected by eye and manually edited if necessary . Estimates of the ages of insertion were obtained by using the equation t = K/2r, where t is the age, and r is nucleotide substitution rate for the host genome DNA polymerase. The value of 1.5 × 10-8 substitutions per site per year was used for r , estimated in [Chs and Adh loci in Arabidopsis/Arabis species.Alignments with fewer than 80 nucleotides were discarded. As CLUSTAL-W alignments could be poor if LTR sequences were only partially overlapping, for all LTR pairs with mated in  for the Finally, if recombination between LTRs from different insertions had occurred frequently, the dating method above would be invalid for obtaining the age profiles of different families. To detect possible recombination events, multiple alignments of all LTRs (including solos) of certain families were generated using BLASTALIGN , a progrA. thaliana. Additional data file A. thaliana. Additional data file Perl script LTR_MINER, used to de-fragment sequence similarity hits to LTR-retrotransposons, and identify complete and solo-LTR elements. Additional data file Perl script in Additional data file Perl script used in conjunction with LTR_MINER, used to divide long sequences into smaller chunks labeled by their coordinate range. Additional file data Perl script in Additional data file The following additional data files are available with the online version of this article. Additional data file A. thalianaThe entire dataset of chromosomal coordinates and ages of complete LTR-retrotransposons in Click here for additional data fileA file describing the data fields in Additional data file 1Click here for additional data fileA. thalianaThe entire dataset of chromosomal coordinates of solo-LTRs in Click here for additional data fileA file describing the data fields in Additional data file 3Click here for additional data filePerl script LTR_MINER, used to de-fragment sequence similarity hits to LTR-retrotransposons, and identify complete and solo-LTR elementsThe Click here for additional data filePerl script in Additional data file 5A file describing the utility and usage of the Click here for additional data filePerl script used in conjunction with LTR_MINER, used to divide long sequences into smaller chunks labeled by their coordinate rangeThe Click here for additional data filePerl script in Additional data file 7A file describing the utility and usage of the Click here for additional data file


Drosophila protein-protein interaction map was constructed using the LexA system, complementing a previous map using the GAL4 system and adding many new interactions.A  Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions.Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives.To complement the Gal4-based interaction data, we used the same set of Drosophila and other organisms.The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in  Protein-protein interactions have an essential role in a wide variety of biological processes. A wealth of data has emerged to show that most proteins function within networks of interacting proteins, and that many of these networks have been conserved throughout evolution. Although some of these networks constitute stable multi-protein complexes while others are more dynamic, they are all built from specific binary interactions between individual proteins. Maps depicting the possible binary interactions among proteins can therefore provide clues not only about the functions of individual proteins but also about the structure and function of entire protein networks and biological systems.Saccharomyces cerevisiae. One protein is fused to a DNA-binding domain (BD) and the other is fused to a transcription activation domain (AD). An interaction between the two proteins results in activation of reporter genes that have upstream binding sites for the BD. To map interactions among large sets of proteins, the BD and AD expression vectors are placed initially into different haploid yeast strains of opposite mating types. Pairs of BD and AD fused proteins can then be tested for interaction by mating the appropriate pair of yeast strains and assaying reporter activity in the resulting diploid cells [One of the most powerful technologies used in recent years for mapping binary protein interactions is the yeast two-hybrid system . In a yeid cells . Large aid cells -6. Most Caenorhabditis elegans and Drosophila melanogaster [in vivo. Unfortunately, this is a common feature of all high-throughput methods for generating interaction data, including affinity purification of protein complexes and computational methods to predict protein interactions [High-throughput two-hybrid screens have been used to map interactions among proteins from bacteria, viruses, yeast, and most recently, nogaster -10. Analnogaster -13. One ractions -14. A soractions -13. ThusC. elegans [A second shortcoming of the large-scale screens has been the high rate of false negatives, or missed interactions. This is evident from comparing the high-throughput data with reference data collected from published low-throughout studies. Such comparisons with two-hybrid maps from yeast  and C. e elegans , for exa elegans ,12. For  elegans . These r elegans . Thus, tDrosophila proteins by using two different yeast two-hybrid systems  and different screening strategies. Results from the screens using the Gal4 system have already been published [et al. successfully amplified 12,278 Drosophila open reading frames (ORFs) and subcloned a majority of them into the Gal4 BD and Gal4 AD expression vectors by recombination in yeast. They screened the arrays using a library-screening approach and detected 20,405 interactions involving 7,048 proteins. To extend these results we subcloned the same amplified Drosophila ORFs into vectors for use in the LexA-based two-hybrid system, and constructed arrays of BD and AD yeast strains for high-throughput screening. Our expectation was that maps generated with these arrays would include interactions missed in previous screens, and would also partially overlap the Gal4 map, providing opportunities for cross-validation.We set out to map interactions among the approximately 14,000 predicted ublished . In thatInitially, we screened for interactions involving proteins that are primarily known or suspected to be cell-cycle regulators. We chose cell-cycle proteins as a starting point for our interaction map because cell-cycle regulatory systems are known to be highly conserved in eukaryotes, and because previous results have suggested that the cell-cycle regulatory network is centrally located within larger cellular networks . This isDrosophila full-length ORFs from the Gal4 project [Escherichia coli that fortuitously activates transcription in yeast [Drosophila ORF insert . For all strains involved in an interaction reported here, the plasmid was isolated and the insert was sequenced to verify the identity of the ORF.We used the same set of 12,278 amplified  project  to generin yeast . In the in yeast . Previouin yeast ,19. The in yeast ,6, and tAs a first step toward generating a LexA-based protein-interaction map, we chose 152 BD-fused proteins that were either known or homologous to regulators of the cell cycle or DNA damage repair . We used all 152 proteins as 'baits' to screen the 12,000-member AD array. We used a pooled mating approach  in whichAltogether we detected interactions with 106 of the 152 baits, which resulted in a protein-interaction map with 1,814 unique interactions among the products of 488 genes . The map includes interactions that were already known or that could be predicted from known orthologous or paralogous interactions (see below). The map also includes a large number of novel interactions, including many involving functionally unclassified proteins.in vivo experimental verification impracticable. Thus, we set out to assess the quality of the data by examining the topology of the interaction map, by looking for enrichment of genes with certain functions, and by comparing the LexA map with other datasets. First we examined the topology of the interaction map, because recent studies have shown that cellular protein networks have certain topological features that correlate with biological function [k) varies over a broad range (from 1 to 84) and the distribution of proteins with k interactions follows a power law, similar to previously described protein networks [As is common with data derived from high-throughput screens, the number of novel interactions detected was large, making direct function . In our networks ,21. Mostnetworks , charactnetworks ,6,16. Monetworks -25. ThusP < 0.00002). In the AD list, these two functions are enriched four- and threefold, respectively (Table P < 0.001). Thus, the enrichment for proteins and pairs of interacting proteins annotated with the same function suggests that many of the novel interactions will be biologically significant. It also suggests that the map will be useful for predicting the functions of novel proteins on the basis of their connections with proteins having known functions, as described for other interaction maps [Next we assessed the list of proteins in the interaction map to look for enrichment of proteins or pairs of proteins with particular functions. An interaction map with a high rate of biologically relevant interactions should have a high frequency of interactions between pairs of proteins previously thought to be involved in the same biological process. Among the 488 proteins in the map, 153 have been annotated with a putative biological function using the Gene Ontology (GO) classification system ,27. Becaion maps ,28.Direct comparison of the LexA cell-cycle map with the Gal4 data revealed that only 28 interactions were found in common between the two screens Table . Moreovep < 10-6; Table 6 random networks having the same BD proteins, total interactions and topology as the LexA map, and found that none of these random maps shared more than two interactions in common with the Gal4 map. To assess the relative quality of the 28 common interactions we used the confidence scores assigned to them by Giot et al. [P < 0.0001), indicating that the overlap of the two datasets is significantly enriched for biologically relevant interactions. Thus, the detection of interactions by both systems could be used as an additional measure of reliability. The surprisingly small number of common interactions, however, severely limits the opportunities for cross-validation, and suggests that both datasets are far from comprehensive.Although only 28 interactions were found in both the Gal4 map and our map, this rate of overlap is significantly greater than expected by chance . This supports the notion that these datasets are enriched for true-positive interactions relative to randomly selected pairs of proteins. We found that the fractions of LexA- and Gal4-derived interactions that overlap with these datasets are similar ; andterlogs' ,33. As ein vivo [in vivo (for example Cdc2-CycE [in vivo (Cdk4-CycD) and two that do not occur in vivo [Further inspection of the LexA cell-cycle interaction map revealed biologically informative interactions and additional insights for interpreting high-throughput two-hybrid data. For example, we expected to observe interactions between cyclins and cyclin-dependent kinases (Cdks), which have been shown to interact by a number of assays. Our interaction map includes six proteins having greater than 40% sequence identity to Cdk1 . A map of all the interactions involving these proteins reveals that they are multiply connected with several cyclins . Similadc2-CycE , includi in vivo .in vivo. Such patterns are consistent with potential interactions between members of different protein families, even though they do not reveal the precise pair of proteins that interact in vivo. This class of informative false positives may be common in two-hybrid data where the interaction is assayed out of biological context. Experimentally reproducible interactions, whether or not they occur in vivo, can be used to discover interacting protein motifs or domains [in vivo interactions and functions of specific pairs of interacting proteins.Thus, while some of these interactions are false positives in the strictest sense, the data is informative nevertheless, as it clearly demonstrates a high incidence of paralogous interactions - where pairs of interacting proteins each have paralogs, some combinations of which also interact  domains ,36. TheyThe Cdk subgraph also illustrates that proteins with similar interaction profiles may have related functions or structural features. To look for other groups of proteins having similar interaction profiles we used a hierarchical clustering algorithm to cluster BD and AD fusion proteins according to their interactions . The resulting clustergram reveals several groups of proteins with similar interaction profiles Figure . One of Thus, the Skp cluster provides another example of how proteins with similar interaction profiles may be structurally or functionally related, and how such clusters may be enriched for biologically relevant interactions. This is consistent with previous results showing that protein pairs often have related functions if they have a significantly larger number of common interacting partners than expected by chance ,38. Theset al. [The interaction profile data is statistically confirmed by domain-pairing data, which shows that certain pairs of domains are found within interacting pairs of proteins more frequently than expected by chance Table . These iet al. , revealeDrosophila protein interaction map that was generated using the Gal4 yeast two-hybrid system [Drosophila ORFs, using the same PCR amplification products used in the Gal4 project, into the LexA two-hybrid vectors. Initially, we used a novel pooled mating approach [Proteome-wide maps depicting the binary interactions among proteins provide starting points for understanding protein function, the structure and function of protein complexes, and for mapping biological pathways and regulatory networks. High-throughput approaches have begun to generate large protein-interaction maps that have proved useful for functional studies, but are also often plagued by high rates of false positives and false negatives. Several analyses have shown that the set of interactions detected by more than one high-throughout approach is enriched for biologically relevant interactions, suggesting that the application of multiple screens to the same set of proteins results in higher-confidence, cross-validated interactions -13. Suchd system . We consapproach  to screeC. elegans proteins were shown to cover only about 10-13% of the corresponding reference datasets [The level of coverage for a high-throughput screen can be estimated by determining the percentage of a reference dataset that was detected; reference sets have been derived from published low-throughput experiments, for example, which are considered to have relatively low false-positive rates. High-throughput two-hybrid data for yeast and datasets ,10,13. TAlthough the relative contribution of these two factors is difficult to estimate, results from screens to map interactions among yeast proteins suggest that the major reason for the lack of coverage is that the screens are incomplete. Complete screens would identify all interactions that could possibly be detected by a given method; ideally therefore, two complete screens using the same method would identify all the same interactions. However, the rate of overlap among the different yeast proteome screens is low, even though they used very similar two-hybrid systems. Moreover, the overlap between screens is not significantly greater than the rate at which they overlap any reference set ,10. ThisDrosophila proteins are consistent with those for the yeast proteins. We used the LexA system to detect 1,814 reproducible interactions to complement the 20,439 interactions previously detected in a proteome-wide screen using the Gal4 system [Drosophila datasets are far from complete and that many more interactions could be detected by additional two-hybrid screening.The rates of coverage and completeness from our high-throughput two-hybrid screening with 4 system . The oveet al. estimated that at least 11% of the Gal4 interactions are likely to be biologically relevant, based on the prediction accuracy of their statistical model [The actual number of interactions that might be detected by complete two-hybrid screening might be roughly estimated from the partially overlapping datasets, as was performed for accurate estimation of the number of genes in the human genome ,46. In tin vivo. Using the approach described here, the interactions are shown to be reproducible during the one-on-one two-hybrid assays that are used to record reporter activity scores, suggesting that we have minimized the frequency of technical false positives.False-positive interactions have been classified as technical or biological . A technin vivo, but that nevertheless have some biological basis for being detected and are potentially useful for guiding future experiments. In our data, for example, the Cdk and Skp proteins each interact with a different group of targets, which in turn interact with multiple Cdk or Skp proteins. From this data alone, we would accurately predict that Cdk proteins interact with cyclins, and that Skp proteins interact with F-box proteins, even though only some of the specific combinations are true in vivo partners. Similarly, from analysis of domain pairs in the LexA dataset, other patterns are evident, such as homeobox domains being associated with both protein kinase and cyclin domains  of interest. This narrows down the number of potential assays necessary to establish function for a given gene from hundreds to just a handful; conversely, when studying a specific function, such as the cell cycle, interaction data can identify which few genes, selected from thousands, may have a role in the process. Just as the sequencing of various genomes has not allowed unambiguous ascription of biological function to the majority of the identified genes, mapping of an interactome by high-throughput methods does not allow final assignment of interaction capacity or of higher functionality to a protein. This requires additional experiments, guided by these and other high-throughput data. The results presented here show that extending and combining different two-hybrid datasets will allow further refinement of the selection of functional analyses to be performed for each protein of the proteome.MAL62 promoter [GAL1 promoter was pJZ4-NRT, which was constructed from pJG4-5 [ADH1 terminator with the CYC1 terminator and inserting the 5' and 3' recombination tags (5RT1 and 3RT1 [S. cerevisiae) strain RFY231 (MAT trp1::hisG his3 ura3-1 leu2::3Lexop-LEU2) and RFY206 (Mata his3Δ200 leu2-3 lys2Δ201 ura3-52 trp1Δ::hisG) were previously described [lacZ reporter plasmid pSH18-34 [Yeast two-hybrid vectors used are related to those originally described for the LexA system . The vecpromoter . The vecm pJG4-5  by replaand 3RT1 ) into thand 3RT1 . Yeast  in yeast . We begat et al. . For thelacZ reporter plasmids, and containing both galactose and maltose to induce expression of the AD and BD fusions, respectively. The plates also lacked leucine to assay for expression of the LEU2 reporter, and contained X-Gal (40 μg/ml) to assay for expression of lacZ. These plates were photographed after 5 days at 30°C and interactions were scored as described [LEU2 and lacZ reporters were assayed on separate plates: growth on plates lacking leucine was scored from 0 (no growth) to 3 (heavy growth); the extent of blue on the X-Gal plates was scored from 0 (white) to 5 (dark blue). After re-testing interactions  the AD plasmids from interacting AD strains were rescued in bacteria and clones were sequenced to verify insert identity. Cloned plasmids were then reintroduced into RFY231 and used in all possible combinations of one-on-one mating operations with the appropriate BD strains to repeat the interaction assay a third time. The same set of BDs was also used to screen a pool of all approximately 13,000 AD strains using a library screening approach as described in the Additional data files. All interaction data from both screens are listed in Additional data file 3 and are also available at [The BD fused proteins used as baits in our screens are listed in Additional data file 2. The AD array was screened using a two-phase pooled mating approach . First, escribed . In the lable at ,50 and alable at  in the Plable at .Drosophila interlogs of yeast or worm interactions, a list of Drosophila proteins belonging to eukaryotic clusters of orthologous groups (KOGs) [S. cerevisiae) proteins, derived mostly from high-throughput yeast two-hybrid screens [The interaction profiles for the BD fused proteins and AD fused proteins were independently clustered and are plotted in Figure s (KOGs)  was obtas (KOGs) . Each fl screens ,55 and f screens ,57, was  screens ,59.Drosophila proteins belonging to those KOGs. Similarly, Drosophila-worm (C. elegans) interlogs were determined using the list of interactions between worm proteins determined by high-throughput yeast two-hybrid screening [Drosophila genetic interactions were obtained from Flybase [Drosophila Release 3.1 genome [For the interactions determined by precipitation of complexes, two lists were generated. One list includes the binary interactions between the bait protein and every protein that was co-precipitated, but not between the precipitated proteins (hub and spoke model). The second list included all possible binary interactions among the members of a complex (matrix model). The lists were each used to generate a list of interactions between KOG pairs, which in turn was used to generate a list of potential interactions between pairs of creening . Drosoph Flybase ,60. To c1 genome  and thesP values represented the number of times that the observed number of overlapping interactions was detected in 50,000 iterations of a random network, divided by 50,000. In most cases P < 0.0002 . Additional methods are in Additional data file 1. To compare the number of common interactions between the LexA and Gal4 maps with the number expected by chance, we generated 106 random LexA maps and found that they never contained more than two interactions in common with the Gal4 map; thus, the P-value for the 28 common interactions is significantly less than 10-6.Fifty thousand such random networks were generated for each two-hybrid dataset, and then compared with the yeast interlogs, worm interlogs, and genetic interactions to determine the amount of overlap expected by chance. P-values for overlap among datasets, and Supplementary Table 6, Interactions from the LexA and Gal4 screens that successfully used the same BD bait proteins; Additional data file The following additional data are available with the online version of this paper. Additional data file Supplementary Materials and methodsClick here for additional data fileSupplementary Table 1: BD 'baits' used in the LexA screensClick here for additional data fileSupplementary Table 2: Interactions detected in the LexA screensClick here for additional data fileSupplementary Table 3: Enrichment of Gene Ontology classes (the complete list)Click here for additional data fileSupplementary Table 4: Enrichment of Domain pairs (the complete list)Click here for additional data fileP-values for overlap among datasetsa nd Supplementary Table 6: Interactions from the LexA and Gal4 screens that successfully used the same BD bait proteinsSupplementary Table 5: Click here for additional data fileSupplementary Figure 1: Interaction maps of other clustersClick here for additional data fileSupplementary Figure 2: Proteins clustered by interaction profileClick here for additional data fileThe legends to Supplementary Figures 1 and 2Click here for additional data file


Together, vaccination and screening are so effective at preventing the sequelae of persistent high-risk human papillomavirus (hr-HPV) infection that the World Health Organization has set ambitious targets for their global implementation by 2030, in an attempt to achieve this goal.5 The COVID-19 pandemic has contributed further to this decline.6 Screening uptake shows geographic disparities and is particularly low in those aged <30 and >50 years of age, in people of non-White British ethnicities, those who identify as LGBTQIA+, and in socioeconomically deprived communities.7,8 The main barriers to screening are finding time to attend an appointment in primary care during working hours and the embarrassment and discomfort of the speculum examination.Yet uptake of screening in the UK is low and falling. The most recent figures suggest that only 70.2% of those eligible are up-to-date with screening.3 Various strategies to improve participation have been tried; however, non-speculum sampling options show greatest promise.9 Non-speculum sampling capitalises on the physiology of hr-HPV-infected cervical cells shedding through the lower genital tract, from where they can be collected with a vaginal swab or in the first fraction of voided urine as a flushed contaminant.Removing barriers is key to increasing participation, which has the potential to further reduce the UK incidence of cervical cancer by up to 13%.10 of vaginal sampling for hr-HPV detection has prompted nine countries to adopt it as their primary cervical screening collection method,11 and more will certainly follow, including Australia from July 2022. Growing evidence shows preference for self-sampling, with a recent survey suggesting 51.4% of UK invitees and over 70% of imperfect or never attenders would choose self-screening.12 Large pilot studies for population-based vaginal self-sampling, YouScreen and HPValidate, are underway to confirm the feasibility of national implementation, and their results are eagerly awaited. Self-sampling enables the convenience and privacy of home-based collection, reducing pressure on overburdened NHS primary care services. The practicalities of offering vaginal self-sampling need to be understood and learning sought from other programmes where this method has been adopted.The non-inferior test accuracyet al explored the diagnostic performance and acceptability of non-speculum clinician obtained vaginal samples compared to routine cervical screening in primary care and colposcopy clinic attendees. Published in this issue of the BJGP, their results suggest good diagnostic accuracy for cervical precancer detection using non-speculum clinician sampling and high acceptability.13 Previously published in this journal, the same group explored uptake of non-speculum clinician sampling for those aged ≥50 years who were late for screening by ≥12 months.14 Targeting this age group is deliberate and important as individuals aged >65 years account for around half of all cervical cancer deaths.15 Those inadequately screened prior to exiting the NHS Cervical Screening Programme and/or with unknown hr-HPV status are most at risk, prompting some countries to extend the screening age to 75 years or offer a one-off ‘catch up’ hr-HPV test.16 Self-sampling and non-speculum clinician sampling options increased the absolute uptake of screening by 17% compared to the control group, with just over half opting for non-speculum clinician obtained (22.5%) and self-sampling (35.8%) options.13 Non-speculum sampling could be offered as an empowering educational event to enable future home-based self-sampling. Further exploration in adequately powered studies with sufficient cases of cervical precancer to confirm clinical test accuracy and acceptability are now required.Despite its proven non-inferiority to routine cervical screening, adequate vaginal self-sample acquisition remains a user concern. To address this concern, Landy 17 and acceptability with users has been reported when urine is collected using a bespoke first void urine collection device called the Colli-Pee . The ACES  suite of studies in Manchester, UK, will establish with precision how urine compares to cervical sampling for cervical precancer detection, and provide proof of principle for home-based urine self-collection with postal return for hr-HPV testing in current cervical screening non-attenders.Another potential sample type for cervical screening is urine, which has advantages in terms of familiarity with self-collection and avoidance of an intimate procedure. Promising diagnostic accuracy for hr-HPV detectionet al among different ethnic groups13 and those with abnormal screening results . Choice is important, and a menu of screening options would seem the most pragmatic way of ensuring high uptake and satisfaction. Self-sampling is likely to be more cost-effective for a resource-limited NHS, and this must be taken into consideration when shaping the future direction of the Cervical Screening Programme. Healthcare provider and public education campaigns will be essential when implementing changes, as highlighted by the recent negative public reaction to changing the screening interval in Wales from 3- to 5-yearly, despite strong scientific evidence for its safety.18Despite recent advances in non-speculum sampling options for improving uptake of cervical screening, we must proceed with caution before discarding routine cervical screening methodology altogether. Further exploration of acceptability is needed given the differences in preference highlighted by Landy 19 A more streamlined approach would be to subject the hr-HPV positive sample to molecular triage, for example, by extended genotyping or methylation analysis, to distinguish those who need immediate colposcopy referral from those who can be safely deferred for a year.20,21 Emerging evidence suggests that methylation biomarkers may be effective means of molecular triage irrespective of sample type,22 and several rival tests are on the horizon.While ∼87% of those screened are negative for hr-HPV and need no further intervention, those who are positive present a quandary. Reflex cytology is not available for non-cervical sampling methods and, therefore, a repeat sample collected from the cervix is offered to collect this information. Encouragingly, 80%–90% of individuals with a positive self-sample state that they would attend for further investigations.The cervical screening landscape is evolving rapidly, and alongside its vaccine ally, brings with it an optimistic hope for the global elimination of cervical cancer. Non-speculum sampling approaches show huge promise for improving screening uptake and reducing the burden of disease. We must now focus on validating non-speculum sampling test accuracy, through YouScreen, HPValidate, and ACES studies, while determining its acceptability and feasibility for national implementation, being sure to cast the net as wide as possible to maximise its benefits.


With wide socioeconomic mortality differential among older adults in India, a constant question of death clustering across high-risk families and communities arises. The present study uses a follow-up survey from India to investigate the socioeconomic, demographic and health predictors of old-age mortality clustering. Data of 16,964 older adults nested within 12,981 households from 2352 communities were used from India Human Development Survey (IHDS) round-I (2005) who were further tracked down in round-II (2012). Bivariate association between the determinants of old-age mortality was investigated using the log-rank test. The multivariate analysis involved estimating the random-intercept Weibull proportional hazard model with three levels—individual (level 1), family (level 2) and community (level 3). We analyzed the sensitivity of multivariate results to unobservable variable and selection biases using the e-value method. The empirical analysis confirms that the risk of mortality is significantly heterogeneous between the families. The health status of older adults and the family’s socioeconomic status in the early years emerged as prominent predictors of a longer lifespan. With a strong association between household income and mortality hazard risk, the present study urges early life interventions as those started in late-life might have negligible impact on keeping the older adults alive and healthy. Historically, people used to survive merely 25 years from birth; however, with the evolution in living conditions of human society and a vast reduction in the mortality rates, average life expectancy at birth has grown nearly 45 years today2. Though initially, developed countries experienced such mortality reductions in a non-uniform manner, the mortality reduction in younger ages was comparably much higher than the others due to a decline in the fatality figure of infectious diseases3. Such progress was combined with the falling fertility rates and increasing life expectancy, leading to an increase in the share of the aged in the total population4.The famous phrase of Thomas Hobbes denotes human lives as “nasty, brutish and short”5. Developed countries that have already experienced the demographic transition try to break the stereotypes of the aged population being dependent, frail, and burdened for society. Policies are working towards promoting the wellbeing of older adults and promoting healthy aging among individuals6. However, such policy reforms are rare in developing nations where the focus is on fulfilling the development needs of children and youth. Notably, even if the pace of population aging started in the high-income countries, by 2050, 80% of the older population is expected to live in low-and-middle-income countries, which brings the old-age mortality issue to the central stage6.As per the United Nations report, the aged population would increase from 0.7 billion  in 2019 to 1.5 billion (16%) in 20507. Two longitudinal studies from UK and USA found a long term impact (i.e. nearly 2 and 6 years respectively) of socioeconomic and health status of individuals with survival in old ages9. A follow-up study from Taiwan found that individuals with higher ages, poor schooling, consistent unemployment and poor life satisfaction in wave 1 have a higher probability of mortality in later periods10. However, the health status of individuals (in the form of activities of daily living conditions and self-reported chronic diseases) emerged as the primary predictor among all the other factors. Though marriage had a significant protective impact on the lives of individuals, males were less likely to be alive in follow-up surveys10. A study from Bangladesh provided evidence that being head of the household and residing with a spouse or son helped reduce mortality among older adults11. Studies from Ethiopia, New Zealand, Israel and the United States found that living in a rural area, having different ethnic groups and continents of origin, and experiencing financial hardship or stress can easily trigger mortality at older ages15. Such differences in mortality risks across socioeconomic, cultural and environmental conditions suggest unequal distribution of mortality risks among older adults. These deaths may be clustered among certain families and communities, putting them under the higher-risk categories.A systematic review from developed and developing countries indicate that social determinants like socioeconomic, cultural and environmental conditions, living and working conditions, social and community networks, the lifestyle of individuals are prominent predictors of old-age mortality16. Lesser consideration towards the older adults can create a more significant loss in the future in the form of old-age mortality or repercussions like catastrophic health spending, social and financial insecurity and physical, social and emotional distress18. Few existing literatures from India have shown the effect of age, gender, caste and living standard on old-age mortality20. Studies have shown that prior co-morbidities among older adults have further worsened the old-age mortality risks. Thus, maintaining a healthy lifestyle that involves eating a balanced diet, physical activity, and avoiding substance abuse has contributed to fewer diseases, further reducing the mortality risk in older ages6. Despite knowledge of such determinants, the quality and quantity of life of older adults in India vary across families and communities. This brings the need to understand the risk factors for such unequal distribution of mortality risks among older adults by considering heterogeneity at the household and community levels. The present study improves upon the limitations of extant studies and aims to examine the risk factors of old-age mortality in India using a multilevel survival approach based on a nationally representative survey. Present study also uses the follow up survey data to indicate the predictors of old-age mortality and contributes to the recent literature in this area through robust evidence.In India, 60 years and above population is projected to rise 13.2% in 203122. Notably, IHDS wave-II was a panel survey, which re-interviewed 83% of the original IHDS wave-I households. Further details regarding the IHDS wave-II panel component are available in the user guide22.This research article utilized the India Human Development Survey (IHDS) wave-I and wave-II, jointly administered by the National Council of Applied Economic Research (NCAER) and the University of Maryland. IHDS is a nationally-representative, multi-topic, large-scale survey that provides essential information on health and morbidity, education, employment and economic status, fertility and marital relations, and social capital of the Indian population. IHDS wave-I and wave-II were conducted during 2005 and 2012, respectively, across all India’s states and union territories except for Andaman & Nicobar Islands and Lakshadweep. Both waves of IHDS adopted a multistage stratified random sampling design, and further details on sampling design, data collection and informed consent are available elsewhereThis study refers to persons aged 60 years and above as older adults. Intending to examine old-age mortality, this study utilized the tracking sheet data of IHDS wave-II from 2005 to 2012. Further, to explore the determinants of mortality in older adults, we merged the individual-, household- and community-level information from wave-I with the tracking sheet information in wave-II. The analytical sample of this study is 16,964 older adults residing in 12,891 families and nested within 2,352 communities in India.The information regarding the mortality status of older adults was obtained from the IHDS wave-II tracking sheet data. With the aim of re-interviewing wave-I households during wave-II, the IHDS collected data on the status of all wave-I respondents during wave-II (this information comprised the tracking sheet data). Notably, during wave-II, IHDS gathered information of the survival status of respondents and the year of death prior to wave-II if respondents were not alive. Therefore, this information on survival status and survival time was used to analyze the mortality of older adults in India. All older adults who died during this period were coded as “Yes”; otherwise, they were coded as “No”.23. Further, log-rank tests were performed to examine the association between the risk factors and older adults’ mortality status by adjusting for censored cases. Statistical details of the log-rank test are available elsewhere23.At the outset, we examined the sample distribution of older adults. Next, we estimated the incidence rate of old-age mortality between 2005 and 2012 and grouped it by gender and age group. Further, we performed bivariate and multivariable analyses to achieve the study objectives. Note that the mortality data described in “23. Notably, parametric survival regression models have the advantage of more efficiently utilizing the information from censored cases compared to semi-parametric regression models23. In the survival models, our event of interest is the binary survival status of the older adults between IHDS 2005 and 2012.The multivariable analysis involved estimating random-intercept parametric survival regression models. Survival regression models help utilize the information from censored records in the retrospective life-course data, thereby curtailing the loss of crucial information23. Based on theoretical knowledge and statistical evidence, we use the Weibull proportional hazard model in our study. The Weibull regression model is appropriate when the hazard of the failure event  is either monotonically increasing or decreasing23. Based on existing knowledge of human mortality, we know that the risk of mortality rises steadily among older adults with progressing age25. A similar trend is observed in our data  and Bayesian information criterion (BIC) scores of the five prominent random-intercept survival regression models . We aim to use the model with the lowest AIC and BIC scores, as that would best fit the data.Additionally, parametric survival regression models allow us to choose the underlying statistical distribution of time-to-old-age mortality27. The statistical description of the three-level random-intercept survival regression model is given below:In the random-intercept Weibull hazard model, we included individual (level 1), family (level 2) and community (level 3) as the three levels. 16,964 older adults from 12,981 families were nested within 2352 communities, forming a hierarchical structure in our study sample. In India, older adults from the same families of the same communities are likely to share the same socioeconomic characteristics and household environment, which means the mortality risk might also be shared. Estimating mortality hazard using standard survival regression would overestimate the risk in this scenario, and using a multilevel framework becomes necessaryHere, 28. It is calculated as29:The random-intercept regression models provide the Intraclass Correlation Coefficient (ICC) and Median Hazard Ratio (MHR), which measures the mortality clustering of older adults within the families and the communities, respectively. The family-level ICC measures the correlation in mortality risk among older adults belonging to the same family of the same community29. It is calculated as:Equivalently, the community-level ICC denotes the correlation in mortality risk among older adults of the same community30. The family-level and community MHR is calculated as:Equivalently, the family-level (or community-level) MHR gives the median relative change in the hazard of the old-age mortality among all possible identical older adults pairs from two separate randomly selected families (or communities) that are ordered by mortality risk23.Further, the multivariable association between the independent variables and old-age mortality risk was shown using hazard ratios (HR). The HR gives the hazard of old-age mortality compared to the baseline mortality risk among older adults belonging to a particular category of an explanatory variable when the effect of other explanatory variables and the community- and family-level variability remain constant32. The e-value method gives the e-value statistic, which is defined as the minimum strength of association  that an unmeasured confounder would need to have with both the treatment and the outcome variables after adjusting for the effect of other independent variables, such that the treatment-outcome variable association is nullified31. Therefore, the higher the e-value, the more robust is the corresponding hazard ratio to unobserved variable bias. The statistical significance of the e-value was determined from the CI limit 32. The CI limit was 1.00 if the e-value was not statistically significant at the 5% level32.Moreover, sensitivity analysis was performed by inspecting the presence of unobservable variable bias in the adjusted hazard ratios using the e-value method33. Unfortunately, IHDS does not provide sample weight in the tracking sheet data, and the study results are unweighted. Statistical significance was determined at the 5% level unless mentioned otherwise. Statistical estimations were performed using the STATA 14 software34.We checked and found that none of the multivariable models violated the multicollinearity assumption20. We included these variables, conditional to their availability in the IHDS dataset. All the below-mentioned characteristics were measured for the older adults during wave-I. The individual-level variables related to the older adults include:Age-group (in years) .Gender .Cardiovascular diseases .Hypertension .Diabetes .Respiratory illnesses .Other chronic illnesses .Activities of daily living .Smokes tobacco .Drinks alcohol .Marital status .Level of education .Working status .Participates in social groups .Headship status .Existing studies have shown several factors which explain the mortality among older adultsFamily structure . It was prepared from the information on household members and their relationship with the household head.Number of children in the household .35.Household wealth quintile . The household wealth quintile for wave-I was calculated using principal component analysis using the available information on household asset ownership. We used standard procedures documented elsewhereHousehold poverty .Caste of household head . The caste system is a form of social hierarchy exclusive to India. Constitutionally, three distinct social groups are recognized in India—scheduled tribes (ST), scheduled castes (SC) and other backward classes (OBC). The ST  and SC categories comprise the most socially backward. They traditionally belonged to the lowest rung of India’s now-defunct caste system. People of the OBC category, as the name implies, are members of a socially and economically backward community. However, their circumstances are better than those of the SC/ST population. The “Others” category consists of all people who do not belong to the three caste groups.Religion of household head .The household-level variables considered in our study are:38:Education level of community .Poverty status of community .Social standard of community .Taking a cue from extant research, we included three community contextual variablesWe constructed these three community contextual characteristics by aggregating the information on the education level of individuals, BPL status of household and caste of the household to the community level, respectively. Prior to aggregation, we constructed binary variables of each of the three characteristics. Community education level was defined as the proportion of individuals with more than 10 years of schooling among all individuals in the community. The higher the proportion of educated individuals, the greater the community's education standard. The community poverty status was defined as the proportion of BPL households among all households in the community. A higher proportion of below poverty line households means a greater prevalence of poverty in the community. Further, community social standard was constructed as the proportion of Non-SC/ST households among all households in the community. Therefore, the higher the proportion of Non-SC/ST households, the greater is the community’s social standard. For ease of interpretation, we categorized the proportions into three categories—“low” (lowest 33rd percentile), “medium” (middle 33rd percentile), and “high” (highest 33rd percentile).(d)Type of community .(e)39.Geographical region . The geographical regions divided India’s erstwhile 33 states and union territories into six areas based on administrative divisionsAdditionally, we included the following community-level characteristics:The present study utilized a publicly available secondary dataset with no information that would lead to the identification of the respondents. IHDS obtained the informed consent of respondents before the data collection. Therefore, no ethical approval was necessary for using these datasets. All survey methods were performed following the relevant guidelines and regulations.Table Figure Table Table Table Table Coming to family-level characteristics, we observed that older adults residing in nuclear  and joint/extended  families faced higher mortality risk than those residing in single generation households. The results also show an economic gradient in the mortality hazard among older adults. The risk of old-age mortality was 1.25 [CI: ] and 1.30 [CI: ] times more among individuals residing in poor and poorest wealth quintile households compared to the richest wealth quintile counterparts. Similarly, older adults from BPL households faced an elevated risk of old-age mortality at 1.13 times [CI: ].Contrary to the bivariate analysis, we find that the educational level, poverty status and social standard of community was not associated with mortality risk among older adults after adjusting for the effect of other independent variables and the community-level and family-level effects. However, older adults residing in communities from Northern  and Eastern  regions of India had higher mortality hazards compared to individuals residing in the Southern region.Table After estimating the Weibull survival regression models, we obtained the adjusted cumulative hazard curve of old-age mortality grouped by lifestyle, social and economic characteristics Fig. . Notably40. The present study reveals a significant loss in the old-age population between 2005 and 2012 with an unequal distribution of mortality risks across families and communities.Today, with substantial health advancements worldwide, people can expect to live into their sixties and beyond. Longer life has provided opportunities for older people  and opened their chance of contributing towards families and communities. However, the growing mortality and health risks during old age hinder such opportunities and contributions41. Long term consequence of widowhood status was prominent in the study as being widow brings higher mortality risks among older adults. Such misfortunate widowhood condition is also visible from extant Indian literature42. The possible explanation for such association includes the protective effect of marriages in social, psychological, economic and environmental support43. Household headship provides constant involvement and control on the household’s social, financial affairs and a sense of security and authority. This might be the reason that household headship in older adults prevents long term mortality risk in the present study.Using a follow-up survey from India, the present study shows that many high-risk families  in India lose multiple members in 60 years and above ages. Although older adults share common characteristics among communities, the present study does not find any significant clustering of mortality at the community level. Even after adjusting the unobserved heterogeneity at family and community levels, mortality risk was higher among older male adults than female counterparts. Consistent with the previous literature, older adults with poor education and those residing in unemployed condition experiences higher mortality risk44. However, in contrast to past evidence, the present study found an insignificant association between social participation in the first wave and mortality risk until the follow-up period. Such association might be possible due to the long-term window of observation. Since the older adults actively indulged in social activities might not continue due to poor health, leaving them in distress which can turn to a shorter lifespan. The health status of older adults is the prominent predictor among all the individual factors45. For instance, if an individual had poor health status in the first wave  then, better education, working status, marital status, or social participation will not be much helpful in reducing the long-term mortality risk until and unless they take early preventive measures.Ample evidence reveals an essential role of the social participation of older adults on long term survival as it may protect them from loneliness, depression, stress, or sadness of being away from loved ones46. Past evidence from India shows a socioeconomic disparity in older-age mortality which is also evident in this study; however, they were unable to show long term impact20. Consistent with a longitudinal study from Taiwan, the present study found that the most prosperous older adults and those living above the poverty line in the first wave enjoy a longer lifespan in the future10. Despite a higher proportion of older adults in southern regions of India, mortality risks were higher in central, northern and eastern areas of India. This might be possible due to individuals’ better health care-seeking behavior in southern regions of India47. However, despite having a poor health care system in the north-eastern regions, the mortality risk remains lower in the present study. Surprisingly, this may be due to the family level factors acting as a protective shield for the older adults or a higher female population in older ages49. For instance, the solid biological advantage of females and satisfaction of being closer to family and community might help in lowering the mortality risk of north-eastern older adults.Extended or joint families experience higher mortality risks among older adults. Such association is possible as the joint or extended families will have more older adults than a single or nuclear generation, making them vulnerable to mortality risks. Moreover, having no children can also be responsible for higher old-age mortality risks due to financial insecurity and loneliness50. Self-reported chronic conditions may create multiple problems in the form of biases like the accuracy of responses, so biological or clinical markers of chronic diseases should be considered while understanding the mortality dynamics of the individual51. The present study uses self-reported information of chronic diseases as the biological measures of older adults were unavailable. Additionally, the study results are unweighted and need to be interpreted accordingly.Despite providing robust evidence of heterogeneity in older-age mortality risk at the family level and revealing the long-term effect of individual, household and community factors on older-age mortality risks, the present study has its limitations. Ample evidence shows that depression and life satisfaction are emerging as prominent indicators of a longer lifespan; however, we could not capture their effect due to the unavailability of information in the data used for this studyIn India, families are the prime source of caregivers for older adults. With significantly higher mortality risk heterogeneity across Indian families, the present study confirms that the familial-level factors  in early years of life may have a noticeable impact on the longer lifespan of older adults. Along with the individual-level factors , health status in the form of chronic diseases and daily living activities remains to have a significant impact on the survival of older adults.2. However, with the growing development of treatments and drugs, a wealthier population pays quickly to cure diseases and ensure a longer life. This trend continues in developing countries, too, combined with the lesser knowledge of health behavior among the uneducated population, increasing the disparities across socioeconomic statuses. Such past evidence and the detrimental effect of poverty and lower income in the present study confirms the unequal share of mortality distribution in old-age across families. The present study will help the policymakers understand the development of such a mortality gradient in the old-age population of India and provide efficient evidence of policy interventions across high-risk families. The long-term consequences of socioeconomic status and health conditions on old-age mortality risk further urge early life interventions as those started in late-life might have negligible impact on keeping the older adults alive and healthy.Past literature from developed countries shows no health gradient among rich and poor before the enlightenment of science and advanced technologiesTraditionally, joint or extended families were one of the characteristics of Indian life where older adults enjoy authority along with care from younger generations. However, changes in living arrangements and lifestyle in past years bring a shift towards the caregiver role in families. The emergence of new health conditions like life satisfaction, stress, and depression among wealthy and low-income families, further, urges future research on the old-age mortality risks in India.


NULL males) respond vigorously to LDH3 immunization. However, partial Treg depletion elevated the wild-type male LDH3 responses to the level of normal females. In contrast to LDH3, zonadhesin (ZAN) in the sperm acrosome displays properties of a sequestered Ag. However, when ZAN and other sperm Ag are exposed by vasectomy, they rapidly induce testis Ag-specific tolerance, which is terminated by partial Treg-depletion, leading to bilateral EAO and ZAN Ab response. We conclude that some testis/sperm Ag are normally exposed because of the unique testicular anatomy and physiology. The exposed Ag: 1) maintain normal Treg-dependent systemic tolerance, and 2) are pathogenic and serve as target Ag to initiate EAO. Unexpectedly, the sequestered Ags, normally non-tolerogenic, can orchestrate de novo Treg-dependent, systemic tolerance when exposed in vasectomy.Continuous exposure of tissue antigen (Ag) to the autoantigen-specific regulatory T cells (Treg) is required to maintain Treg-dependent systemic tolerance. Thus, testis autoantigens, previously considered as sequestered, may not be protected by systemic tolerance. We now document that the complete testis antigen sequestration is not valid. The haploid sperm Ag lactate dehydrogenase 3 (LDH3) is continuously exposed and not sequestered. It enters the residual body (RB) to egress from the seminiferous tubules and interact with circulating antibody (Ab). Some LDH3 also remains inside the sperm cytoplasmic droplets (CD). Treg-depletion in the DEREG mice that express diphtheria toxin receptor on the Foxp3 promoter results in spontaneous experimental autoimmune orchitis (EAO) and Ab to LDH3. Unlike the wild-type male mice, mice deficient in LDH3 (wild-type female or LDH3  AIRE (Fezf2 (AIRE mutation (Fezf2 NULL mice (Inbred mice thymectomized between day 1 and day 5 d3tx) after birth spontaneously develop autoimmune disease in the ovary, prostate, stomach, testis and/or lacrimal gland tx after –6. PeripAIRE , the traE  ULL mice , 3) the ULL mice –16, and Tregs control T effector cell responses in the regional lymph node (LN) of the target organ and prevent autoimmune disease . There iThe Ag relevant to experimental autoimmune orchitis (EAO) are expressed in post-meiotic germ cells inside the seminiferous tubules (SFT). They are separated from the interstitial space by the blood-testis barrier (BTB) provided by the Sertoli cells with their basal and apical tight junctions . Rodent Several studies have argued against the complete sequestration of sperm Ag. First, mice immunized with testis homogenate in incomplete Freund’s adjuvant do not develop EAO but they produce Ab that react with the diploid preleptotene spermatocytes located outside the BTB, indicating that germ cells outside the BTB are immunogenic , 35. SecThe first definitive evidence against complete sperm Ag sequestration was obtained in a study that documented continuous sperm Ag egress from normal SFT and the protection of the exposed Ag by Treg-dependent systemic tolerance . Herein,We present our results in two parts. Part 1 will summarize our published studies on the responses to three individual sperm Ag in normal mice . Part 2 Ldh3 null mice injected with LDH3 Ab or in wildtype (WT) mice injected with Ab against ovalbumin (OVA). In addition, immune complexes were not detected beyond the testis, including the caput, corpus, and cauda epididymis.Lactate dehydrogenase 3 (LDH3) is a cytoplasmic Ag of meiotic germ cells located inside normal ST. LDH3 first appears at postnatal week 2. To detect LDH3 egress from the SFT, we injected rabbit Ab against mouse LDH3 intravenously into normal 8 week-old male mice . Twelve We also investigated the sperm Ag zonadhesin (ZAN), located in the inner acrosomal membrane . In contFollowing LDH3 Ab injection, immune complex formation is dependent on the age of the mice; they are detected in 5 week-old mice but not in mice at the ages of 3, 4, or 4.5 weeks. The onset at 5 weeks coincides with the onset of spermiation, a process wherein elongated haploid spermatids transform into sperm in the first wave of synchronous spermatogenesis . At sperWe detected LDH3 in the RB of normal WT mouse testis by indirect immunofluorescence. Intense staining of OVA was also noted in the RB of the OVA-Hi mice that co-localizes with LDH3. Detection was maximal at stage IX of the spermatogenic cycle . Some RBvia the basal Sertoli cell cytoplasm. These findings are consistent with an electronic microscopic study of labeled rat RB by Xiao et al. , which depletes ~60% of Tregs for 5 weeks  resulted in spontaneous EAO and Ab against LDH3 but not against ZAN. Injection of diphtheria toxin depletes 95% of Tregs from the LN and spleen of male B6AF1-DEREG mice that express transgenic diphtheria toxin receptor on Foxp3-positive Tregs . After 3Third, we compared the immune response to OVA between the OVA-Hi mice and OVA-Lo mice that express 40-fold lower level of transgenic OVA in the elongated spermatid . The OVABecause ZAN has the properties of a foreign Ag, we expected it to stimulate a robust Ab response when it becomes exposed. In 2011 and 2013, we tested this hypothesis in WT mice subjected to Uni-Vx, in which the intact contralateral gonad monitors the systemic responses to sperm release in the ipsilateral epididymis. The result from this unintentional study was unexpected.de novo testis Ag-specific Treg responses that tempers an excessive immune response against the sequestered self-Ag, as seen in tissue injury and testis infections.Epithelial cell apoptosis and desquamation occur in the epididymis within 24 hours after Uni-Vx and sperm are extruded into the interstitial space. To our surprise, the Uni-Vx mice did not have detectable testis Ag-specific or ZAN-specific immune responses for the next 3-4 months , 40. ThiThe ZAN-specific Tregs emerge rapidly following uni-Vx. When Treg depletion is delayed for 7 days from the time of Uni-Vx (day 0), the Treg-dependent tolerance against ZAN is no longer reversible . The ZANThe ZAN-specific tolerance after Uni-Vx is long lasting. Compared to age-matched control mice, the Uni-Vx mice showed resistance to testis Ag induction of sperm Ab and EAO up to 12–16 months . Howevervia molecular mimicry (We have reviewed the supporting evidence that Ag in the normal testis, relevant to testicular autoimmunity, are not completely sequestered. In  mimicry , and dis mimicry .The process of meiotic Ag egress in the SFT also highlights the process of molecular recycling in spermatogenesis that involves the Sertoli cells. In addition to the re-utilization of RB contents in spermatogenesis and spermiogenesis, the apical-to-basal directional movement of macromolecules in the normal SFT epithelium is also documented for the re-utilization of iron in spermatogenesis, and for the recycling of tight junctional molecules in BTB biosynthesis –61In contrast to the exposed testis Ag, some testicular Ag are normally sequestered behind the BTB. They are normally non-tolerogenic and are not protected by the nTreg. However, when they become exposed, as demonstrated by vasectomy, the normally sequestered Ag can rapidly elicit iTreg that prevent or temper concomitant autoimmune responses against them. Remarkably, the Ag-specific iTreg response is strong enough to maintain tolerance for many months after vasectomy. It is possible that when the sequestered sperm Ag are exposed by bacterial or viral infections, or other types of injuries, they too can induce an iTreg-dependent mechanism for protection . Study oOur work emphasizes the importance of the Treg-dependent systemic immune protection of male germ cell Ags. It compliments the extensive research on local regulation in the testis, but by no means minimize the importance of local regulation. However, the findings of Treg-depleted DEREG mice indicate that loss of Treg alone can be sufficient to cause EAO. Future research will focus on how the systemic mechanism and local mechanisms are complimentary and function in concert.KT and JH designed the experiments that constitute this review. KT, JH, HQ, KW, CR, AP, DH, YC, and EG conducted the experiments and analyzed the data. JH, DH, EG, YC provided experimental tools or generate the mouse models. KT and JH wrote the manuscript. All authors contributed to the article and approved the submitted version.The study was supported by NIH grants RO1 AI 41236 and RO1 AI 51420.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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