The immune-modulatory potential of helminth-derived proteins in cellular models of inflammation: a systematic review with cross-study quantitative data analysis

Abstract

Helminths are parasitic worms that secrete a plethora of immune regulatory molecules which allow them to moderate inflammatory responses by their host’s immune system to ensure their survival within the host. This ability to drive a compatible existence with their host has led to research into the potential therapeutic effects of helminth-derived molecules the regulation of overactive immune and inflammatory responses in a wide variety of diseases. This systematic review with cross-study quantitative data analysis aims to synthesize the published data on helminth-derived immunomodulatory peptides/polypeptides/proteins (HDIPs) with a focus on determining the extent to which they modulate the inflammatory response in in vitro cellular models of inflammation. In accordance with PRISMA 2020 guidelines, a predefined systematic search of the PubMed, Web of Science and Medline databases identified relevant studies published up to September 2025, and 79 articles were included after screening. We found that most published studies used LPS or Concanavalin A stimulated macrophages, peripheral blood mononuclear cells or dendritic cells as the cellular model of inflammation. Twenty helminth species from which > 60 isolated HDIPs were derived were tested in these models, with the nematodes, Haemonchus contortus and Acanthocheilonema viteae, and the trematode, Fasciola hepatica, the most explored species. A common property of these molecules was the ability to significantly reduce the expression or production of pro-inflammatory cytokines such as IL-12, IL-1β, IL-6 and TNF, and significantly increase the expression or production of anti-inflammatory cytokines such as IL-10, TGFβ and IL-4. The effects on other cytokines, including IFNγ which is known to have both pro- and anti-inflammatory effects, were less consistent, with HDIPs either decreasing or increasing the levels of this cytokine. This systematic review with cross-study quantitative data analysis synthesizes the existing literature in this field and shows that the HDIPs secreted by several helminth species have consistently demonstrated effects though modification of cytokine levels and, as such, have therapeutic potential in conditions in which overactive immune and inflammatory responses play a pathogenic role.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-38162-x.

Introduction

Helminths are parasitic worms, classified into nematodes (roundworms, whipworms and hookworms), trematodes (blood-flukes) and cestodes (tapeworms)¹. These parasites are able to infect a wide variety of species and can cause mild to severe disease in their hosts. Soil-transmitted helminth infections, considered the most important group of neglected tropical diseases by the World Health Organization (WHO), are typically caused by nematode parasites and can lead to intestinal issues, kidney damage, dermatitis, respiratory issues, allergy symptoms, malnutrition, fatigue and other health issues². The parasite eggs are deposited through human and animal faeces where the larvae can survive in water or soil for weeks until they are ingested (or otherwise enter the body) and then migrate into the intestines, liver, or other tissues where they mature, develop and lay eggs to complete the growth cycle³. It is estimated that over 1.5 billion people around the world are infected with one or more of these helminth species, with infections affecting those in developing countries at higher rates due to poor sanitation facilities and infected drinking water².

Interestingly, in developed or high-income countries where worm infections are low, there has been notable increases in immune-mediated conditions such as colitis, allergy and eczema in recent decades that align with patterns of decreasing helminth infection^4,5. This inverse correlation between helminth infection and immune/inflammatory disease suggests that by limiting exposure to the diverse pathogens through safer hygiene and sanitation practices, more humans today lack the immune-protective effects provided by helminth infection against inflammatory and allergenic diseases⁶. This idea has evolved into the ‘old friends’ hypothesis, proposing that, because helminths evolved alongside the adaptive immune system they provide some protection against certain diseases, and in their chronic infective state, they are more like ‘old friends’ than foe⁷. Indeed, in endemic populations it seems the ubiquitous presence of helminths has even impacted the immune response at the genome level with evidence of SNP in genes associated with the Th2 immune response⁸. These observations have stimulated extensive research into elucidating the molecular mechanisms behind the immunomodulatory properties of worms, and particularly the molecules they secret as a potential novel source of biotherapeutics for the treatment of immune and inflammatory conditions.

Although helminth infections can be detected and ejected by the host immune system (primarily through IL-4-driven Th2 responses⁹), they can also secrete a plethora of immune-modulatory molecules which can allow then to remain undetected for long periods of time by their host’s immune system¹⁰. These helminth-derived immunomodulatory peptides/polypeptides/proteins (HDIPs) can modulate the host’s immune response by shifting the balance between the Th1 and Th2 responses. This can lead to a state of immune tolerance during the parasite’s chronic infective stage, allowing them to remain tolerated by the host for months, years or even decades. While HDIPs are secreted for parasite protection inside the host, because they are immunosuppressive, they may have therapeutic potential as isolated (or synthetic) peptides/polypeptides/proteins in a wide variety of diseases in which overactive immune and inflammatory responses play a pathogenic role^1,11. Multiple types of HDIPs from a variety of helminth species have been isolated and purified (or synthesized) in the past twenty years. These have been shown to modulate the immune and inflammatory response in multiple cell types, particularly through altered cytokine responses. However, to date, there has been no systematic review of this literature, and thus, this review aims to systematically synthesize the published data on HDIPs and the extent to which they modulate the inflammatory response in cellular models of inflammation.

Methods

Search strategy

This study was completed in accordance with the PRISMA 2020 guidelines¹² to find articles in which HDIPs were assessed in cellular models of inflammation. The literature search was completed in the PubMed, Web of Science and Medline databases with the specific search string: helminth AND secret* AND (immunomod* OR immunosup* OR antiinflam*). This search identified a total of 781 records, spanning July 2001 to September 2025. These articles were then screened according to the strategy outlined below and depicted in the PRISMA flow diagram (Fig. 1). This yielded 79 articles which were included in this systematic review. All records were managed in the Endnote and Microsoft Excel software packages.

Fig. 1.

PRISMA flow chart. Flow chart based on PRISMA 2020 guidelines¹² detailing the screening strategy employed for the study selection in the present study.

Screening strategy

Once all duplicate records were removed, the remaining articles were screened by title and abstract according to the following inclusion and exclusion criteria. Inclusion criteria: (1) original research study, (2) HDIPs, (3) cellular inflammatory model. Exclusion criteria: (1) review article, (2) not helminth-derived (3), undefined helminth-derived molecule, (4) not peptide/polypeptide/protein helminth-derived molecule, (5) not tested in inflammatory model and (6) not peer-reviewed or redacted. In the full-text screen, three other exclusion criteria were applied (7) no cytokine measure, (8) no appropriate control and (9) not in a cellular (in vitro) model. After screening, 63 articles met the inclusion and exclusion criteria. The reference lists in these articles were then further screened by title, and those naming a specific species (rather than using the term “helminth”) were further screened using the same inclusion and inclusion criteria as the other articles. This led to inclusion of a further 16 articles. Thus, a total of 79 articles were included in this systematic review (Table 1 and Supplementary Excel file).

Table 1.

Studies and individual records included in this review.

References	Author	Cell type(s)	Inflamm (s)	Helminth (s)	HDIP	[HDIP] (µg/ml)	Time (hr.)
14	Schönemeyer 2001	hPBMC	Anti-CD3	O. volvulus	rOv17	0.5 µM	48
15	Goodridge 2001	mMacrophage	LPS & IFNγ	A. viteae	ES-62	1	18
15	Goodridge 2001	mMacrophage	LPS & IFNγ	A. viteae	ES-62	2	18
15	Goodridge 2001	mMacrophage	LPS & IFNγ	A. viteae	ES-62	5	18
16	Spolski 2002	mT cell	ConA	T. crassiceps	p66	20	72
17	McInnes 2003	hT cell	LPS	A. viteae	ES-62	2	18
17	McInnes 2003	hT cell	Type II collagen	A. viteae	ES-62	2	18
18	Goodridge 2005	mMacrophage	LPS & IFNγ	A. viteae	ES-62	2	18
18	Goodridge 2005	mMacrophage	LPS & IFNγ	A. viteae	ES-62	2	18
18	Goodridge 2005	mDendritic cell	LPS	A. viteae	ES-62	2	18
19	Rigano 2007	hDendritic cell	LPS	E. granulosus	EgAgB	10	18
20	Donnelly 2008	mSplenocyte	PMA & Anti-CD3	F. hepatica	rFhPrx	20	72
21	Brännström 2009	hMonocyte	LPS	S. mansoni	rSm16	20	6
21	Brännström 2009	hMonocyte	LPS	S. mansoni	rSm16	20	8
21	Brännström 2009	hMonocyte	Poly I:C	S. mansoni	rSm16	20	6
21	Brännström 2009	hMonocyte	Poly I:C	S. mansoni	rSm16	20	8
22	Sun 2012	mSplenocyte	PMA	S. japonicum	rSj16	0.5	72
23	Chhabra 2014	hT cell	Thapsigargin	A. caninum	AcK1t	0.3 µM	48
23	Chhabra 2014	hT cell	Thapsigargin	A. caninum	AcK1t	15 µM	48
23	Chhabra 2014	hT cell	Thapsigargin	A. caninum	AcK1t	3 µM	48
23	Chhabra 2014	hT cell	Thapsigargin	B. malayi	BmK2	0.01 µM	48
23	Chhabra 2014	hT cell	Thapsigargin	B. malayi	BmK2	0.1 µM	48
23	Chhabra 2014	hT cell	Thapsigargin	B. malayi	BmK2	1 µM	48
24	Wang 2014	gPBMC	ConA	H. contortus	Hco-gal-m	10	72
24	Wang 2014	gPBMC	ConA	H. contortus	Hco-gal-m	20	72
24	Wang 2014	gPBMC	ConA	H. contortus	Hco-gal-m	40	72
24	Wang 2014	gPBMC	LPS	H. contortus	Hco-gal-m	10	72
24	Wang 2014	gPBMC	LPS	H. contortus	Hco-gal-m	20	72
24	Wang 2014	gPBMC	LPS	H. contortus	Hco-gal-m	40	72
25	Du 2014	mMacrophage	LPS	T. spiralis	rTsP53	10	12
25	Du 2014	mMacrophage	LPS	T. spiralis	rTsP53	10	6
25	Du 2014	mMacrophage	LPS	T. spiralis	rTsP53	10	12
25	Du 2014	mMacrophage	LPS	T. spiralis	rTsP53	10	24
26	Shen 2014	mDendritic cell	LPS	S. japonicum	rSj16	10	24
27	Figueroa-Santiago 2014	hMacrophage	LPS	F. hepatica	Fh12	5	24
28	Dlugos 2015	mSplenocyte	ConA	T. canis	Tc-MUC	5	24
29	Ferguson 2015	mMacrophage	PAM3CSK4	S. mansoni	omega-1	0.25	48
30	Wang 2015	mDendritic cell	LPS	E. granulosus	EgFerritin	1	20
31	Sanin 2015	mMacrophage	LPS	S. mansoni	Sm16	10	24
31	Sanin 2015	mMacrophage	PAM3CSK4	S. mansoni	Sm16	10	24
31	Sanin 2015	mMacrophage	Poly I:C	S. mansoni	Sm16	10	24
32	Martin 2015	mMacrophage	LPS	F. hepatica	Fh12	5	24
33	Khatri 2015	mMacrophage	LPS	B. malayi	rBmCys	250	72
34	Behrendt 2016	rMicroglia	LPS	A. viteae	rAVv17	250 nM	2
34	Behrendt 2016	rMicroglia	LPS	A. viteae	rAVv17	250 nM	6
34	Behrendt 2016	rMicroglia	LPS	A. viteae	rAVv17	250 nM	24
34	Behrendt 2016	rMicroglia	LPS	A. viteae	rAVv17	500 nM	24
35	Gadahi 2016	gPBMC	ConA	H. contortus	rHcES-24	5	24
35	Gadahi 2016	gPBMC	ConA	H. contortus	rHcES-24	10	24
35	Gadahi 2016	gPBMC	ConA	H. contortus	rHcES-24	20	24
35	Gadahi 2016	gPBMC	ConA	H. contortus	rHcES-24	40	24
36	Cao 2016	mMacrophage	LPS	S. japonicum	rSjHSP70	1	24
36	Cao 2016	mMacrophage	LPS	S. japonicum	rSjHSP70	10	24
36	Cao 2016	mMacrophage	LPS	S. japonicum	rSjTPx	1	24
36	Cao 2016	mMacrophage	LPS	S. japonicum	rSjTPx	10	24
37	Gadahi 2016	gPBMC	ConA	H. contortus	rHcftt-2	5	24
37	Gadahi 2016	gPBMC	ConA	H. contortus	rHcftt-2	10	24
37	Gadahi 2016	gPBMC	ConA	H. contortus	rHcftt-2	20	24
37	Gadahi 2016	gPBMC	ConA	H. contortus	rHcftt-2	40	24
38	Eason 2016	mDendritic cell	LPS	A. viteae	ES-62	2	18
39	Silva-Alvarez 2016	hMacrophage	LPS	E. granulosus	EgAgB	1	12
39	Silva-Alvarez 2016	hMacrophage	LPS	E. granulosus	EgAgB	10	12
39	Silva-Alvarez 2016	hMacrophage	LPS	E. granulosus	EgAgB	20	12
39	Silva-Alvarez 2016	hMacrophage	PMA	E. granulosus	EgAgB	1	12
39	Silva-Alvarez 2016	hMacrophage	PMA	E. granulosus	EgAgB	10	12
39	Silva-Alvarez 2016	hMacrophage	PMA	E. granulosus	EgAgB	20	12
40	Lund 2016	hMonocyte	LPS	F. hepatica	FhHDM-1	25 µM	12
41	Alvarado 2016	mMacrophage	LPS	F. hepatica	FhHDM-1	25 µM	3
42	Ehsan 2017	gPBMC	ConA	H. contortus	Hc-AK	5	24
42	Ehsan 2017	gPBMC	ConA	H. contortus	Hc-AK	10	24
42	Ehsan 2017	gPBMC	ConA	H. contortus	Hc-AK	20	24
42	Ehsan 2017	gPBMC	ConA	H. contortus	Hc-AK	40	24
42	Ehsan 2017	gPBMC	ConA	H. contortus	Hc-AK	80	24
43	Floudas 2017	mDendritic cell	LPS	S. mansoni	rSmCypA	n.s	24
44	Wang 2017	gPBMC	LPS	H. contortus	rHCMIF-1	10	72
44	Wang 2017	gPBMC	LPS	H. contortus	rHCMIF-1	20	72
44	Wang 2017	gPBMC	LPS	H. contortus	rHCMIF-1	40	72
44	Wang 2017	gPBMC	LPS	H. contortus	rHCMIF-1	80	72
45	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-3	10	72
45	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-3	20	72
45	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-3	40	72
45	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-3	80	72
46	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-2	10	72
46	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-2	20	72
46	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-2	40	72
46	Wang 2017	gPBMC	LPS	H. contortus	rHCcyst-2	80	72
47	Lumb 2017	mDendritic cell	LPS	A. viteae	ES-62 11a	2	18
47	Lumb 2017	mDendritic cell	LPS	A. viteae	ES-62 11e	2	18
47	Lumb 2017	mDendritic cell	LPS	A. viteae	ES-62 11i	2	18
47	Lumb 2017	mDendritic cell	LPS	A. viteae	ES-62 12b	2	18
48	Amdare 2017	mMacrophage	LPS	W. bancrofti	rWbL2	100	72
49	Wang 2017	hPBMC	LPS	S. japonicum	SJMHE1	1	24
49	Wang 2017	mSplenocyte	LPS	S. japonicum	SJMHE1	1	24
50	Knuhr 2018	hMonocyte	LPS	S. mansoni	IPSE/alpha-1	0.1	24
51	Ball 2018	mMast cell	LPS	A. viteae	ES-62	2	24
51	Ball 2018	mMast cell	LPS	A. viteae	ES-62	2	24
52	Zheng 2018	mMacrophage	LPS	E. multilocularis	emu-TegP11	100	24
53	Togre 2018	mMacrophage	LPS	W. bancrofti	rWbL2	250	42
54	Wang 2019	mMacrophage	LPS	E. granulosus	rEgTPx	10	24
55	Aguayo 2019	mMacrophage	LPS	F. hepatica	nFhGST	15	18
55	Aguayo 2019	mMacrophage	LPS	F. hepatica	nFhGST	25	18
55	Aguayo 2019	mMacrophage	LPS	F. hepatica	nFhGST	30	18
56	Wang 2019	gPBMC	ConA	H. contortus	rHCA59	10	48
56	Wang 2019	gPBMC	ConA	H. contortus	rHCA59	20	48
56	Wang 2019	gPBMC	ConA	H. contortus	rHCA59	40	48
57	Vacca 2020	hEK	Cytokine(s)	H. polygyrus bakeri	HpBARI	0.1	120
58	Ehsan 2020	gPBMC	ConA	H. contortus	rHcES-15	10	72
58	Ehsan 2020	gPBMC	ConA	H. contortus	rHcES-15	20	72
58	Ehsan 2020	gPBMC	ConA	H. contortus	rHcES-15	40	72
58	Ehsan 2020	gPBMC	ConA	H. contortus	rHcES-15	80	72
59	Ilgova 2020	pMacrophage	LPS	E. nipponicum	rEnStef	10	6
59	Ilgova 2020	pMacrophage	LPS	E. nipponicum	rEnStef	20	6
60	Kobpornchai 2020	mMacrophage	LPS	T. spiralis	TsCstN	10	12
60	Kobpornchai 2020	mMacrophage	LPS	T. spiralis	TsCstN	10	24
60	Kobpornchai 2020	mMacrophage	LPS	T. spiralis	TsCstN	10	12
60	Kobpornchai 2020	mMacrophage	LPS	T. spiralis	TsCstN	10	24
61	Shiels 2020	hMacrophage	LPS	S. mansoni	rSm16	5	16
61	Shiels 2020	hMacrophage	LPS	S. mansoni	rSm16	10	16
61	Shiels 2020	hMacrophage	LPS	S. mansoni	Sm16(34–117)	20	16
61	Shiels 2020	hMacrophage	LPS	S. mansoni	rSm16	25	16
61	Shiels 2020	hMacrophage	LPS	S. mansoni	rSm16	50	16
62	Wang 2020	gPBMC	ConA	H. contortus	rHCRD	10	72
62	Wang 2020	gPBMC	ConA	H. contortus	rHCRD	20	72
62	Wang 2020	gPBMC	ConA	H. contortus	rHCRD	40	72
63	Wang 2020	gPBMC	ConA	H. contortus	rHCPTPA	10	72
63	Wang 2020	gPBMC	ConA	H. contortus	rHCPTPA	20	72
63	Wang 2020	gPBMC	ConA	H. contortus	rHCPTPA	40	72
64	Wang 2020	gPBMC	ConA	H. contortus	rHCETFα	10	72
64	Wang 2020	gPBMC	ConA	H. contortus	rHCETFα	20	72
64	Wang 2020	gPBMC	ConA	H. contortus	rHCETFα	40	72
65	Naqvi 2020	gPBMC	PMA	H. contortus	rHc-GDC	10	48
65	Naqvi 2020	gPBMC	PMA	H. contortus	rHc-GDC	20	48
65	Naqvi 2020	gPBMC	PMA	H. contortus	rHc-GDC	40	48
65	Naqvi 2020	gPBMC	PMA	H. contortus	rHc-GDC	80	48
66	Lu 2020	gPBMC	ConA	H. contortus	rHcADRM1	5	24
66	Lu 2020	gPBMC	ConA	H. contortus	rHcADRM1	10	24
66	Lu 2020	gPBMC	ConA	H. contortus	rHcADRM1	20	24
66	Lu 2020	gPBMC	ConA	H. contortus	rHcADRM1	40	24
67	Lu 2020	gPBMC	ConA	H. contortus	rHcABHD	10	24
67	Lu 2020	gPBMC	ConA	H. contortus	rHcABHD	20	24
67	Lu 2020	gPBMC	ConA	H. contortus	rHcABHD	40	24
67	Lu 2020	gPBMC	ConA	H. contortus	rHcABHD	80	24
68	Montero 2020	mDendritic cell	LPS	T. crassiceps	GK-1	10	24
68	Montero 2020	mDendritic cell	LPS	T. crassiceps	GK-1	100	24
69	Buitrago 2021	hMacrophage	LPS	N. americanus	Na-AIP-1	100	24
70	Ehsan 2021	gPBMC	ConA	F. gigantica	rFg-CaBP4	5	26
70	Ehsan 2021	gPBMC	ConA	F. gigantica	rFg-CaBP4	10	26
70	Ehsan 2021	gPBMC	ConA	F. gigantica	rFg-CaBP4	20	26
70	Ehsan 2021	gPBMC	ConA	F. gigantica	rFg-CaBP4	40	26
71	Xie 2021	mMacrophage	LPS	S. japonicum	rSj-Cys	2	24
72	Corbet 2021	mFibroblast	Cytokine(s)	A. viteae	ES-62	2	24
72	Corbet 2021	mFibroblast	Cytokine(s)	A. viteae	ES-62	2	24
72	Corbet 2021	mFibroblast	LPS	A. viteae	ES-62	2	24
72	Corbet 2021	mFibroblast	PAM3CSK4	A. viteae	ES-62	2	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	0.001	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	0.01	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	0.5	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	1	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	5	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	10	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	20	24
73	Smallwood 2021	hPBMC	PMA	A. caninum	Acan1	50	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	0.001	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	0.01	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	0.5	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	1	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	5	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	10	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	20	24
73	Smallwood 2021	hPBMC	PMA	N. americanus	Nak1	50	24
74	Ryan 2022	hT cell	Anti-CD3 & Anti-CD28	A. caninum	ANCCAN_07727	50	24
74	Ryan 2022	hT cell	Anti-CD3 & Anti-CD29	A. caninum	Ac-FAR-2	10	24
74	Ryan 2022	hT cell	Anti-CD3 & Anti-CD30	A. caninum	ANCCAN_07322	50	24
74	Ryan 2022	hT cell	Anti-CD3 & Anti-CD31	A. caninum	ANCCAN_22177	50	24
74	Ryan 2022	hT cell	Anti-CD3 & Anti-CD32	A. caninum	ANCCAN_08034	50	24
75	Kobpornchai 2022	hNeutrophil	fMLP	T. spiralis	rTsSERP1	10	4
75	Kobpornchai 2022	hNeutrophil	fMLP	T. spiralis	rTsSERP1	10	4
76	Wang 2022	mMacrophage	LPS	F. hepatica	rGSTO2	10	24
77	Loghry 2022	hMacrophage	Cytokine(s)	B. malayi	Bma-LEC-1	0.5 µM	72
77	Loghry 2022	hMacrophage	Cytokine(s)	B. malayi	Bma-LEC-2	0.5 µM	72
78	Zhang 2022	mMacrophage	LPS	F. hepatica	rFhCystatin	1	24
78	Zhang 2022	mMacrophage	LPS	F. hepatica	rFhCystatin	2.5	24
78	Zhang 2022	mMacrophage	LPS	F. hepatica	rFhCystatin	5	24
79	Li 2022	mMacrophage	LPS	T. spiralis	rTs-Cys	0.5	24
80	Zawistowska 2022	hDendritic cell	LPS	F. hepatica	FhCL5	25	48
80	Zawistowska 2022	hDendritic cell	LPS	F. hepatica	fhFABP1	25	48
80	Zawistowska 2022	hDendritic cell	LPS	F. hepatica	FhFABP5	25	48
81	Ruiz-Jiménez 2022	mDendritic cell	LPS	F. hepatica	Fh12	2.5	24
82	Stachyra 2023	mSplenocyte	LPS	T. britovi	CLP	10	24
82	Stachyra 2023	mSplenocyte	LPS	T. britovi	CLP	10	48
82	Stachyra 2023	mSplenocyte	LPS	T. britovi	CLP	10	72
83	Xifeng 2023	mMacrophage	LPS	F. hepatica	rGSTO1	10	24
84	Chantree 2023	mMacrophage	LPS	F. gigantica	rFgCyst	5	24
84	Chantree 2023	mMacrophage	LPS	F. gigantica	rFgCyst	10	24
84	Chantree 2023	mMacrophage	LPS	F. gigantica	rFgCyst	20	24
84	Chantree 2023	hMacrophage	LPS	F. gigantica	rFgCyst	5	24
84	Chantree 2023	hMacrophage	LPS	F. gigantica	rFgCyst	10	24
84	Chantree 2023	hMacrophage	LPS	F. gigantica	rFgCyst	20	24
84	Chantree 2023	mMacrophage	LPS	F. gigantica	rFgCyst	5	24
84	Chantree 2023	mMacrophage	LPS	F. gigantica	rFgCyst	10	24
84	Chantree 2023	mMacrophage	LPS	F. gigantica	rFgCyst	20	24
84	Chantree 2023	hMacrophage	LPS	F. gigantica	rFgCyst	5	24
84	Chantree 2023	hMacrophage	LPS	F. gigantica	rFgCyst	10	24
84	Chantree 2023	hMacrophage	LPS	F. gigantica	rFgCyst	20	24
85	Quinteros 2023	mMacrophage	LPS	F. hepatica	FhHDM-1	2.5 µM	24
85	Quinteros 2023	mMacrophage	LPS	F. hepatica	FhHDM-1	10 µM	24
85	Quinteros 2023	mMacrophage	LPS	F. hepatica	FhHDM-1	15 µM	24
86	Colomb 2024	mEpithelial	Freeze–thaw	H. polygyrus bakeri	HpARI2	10	48
86	Colomb 2024	mEpithelial	Freeze–thaw	H. polygyrus bakeri	HpARI3	10	48
87	Yang 2024	mMacrophage	LPS	C. pisiformis	rCpStefin	5	24
87	Yang 2024	mMacrophage	LPS	C. pisiformis	rCpStefin	10	24
87	Yang 2024	mMacrophage	LPS	C. pisiformis	rCpStefin	20	24
88	Ehsan 2024	gPBMC	ConA	F. gigantica	Fg-LGMN-1	5	2
88	Ehsan 2024	gPBMC	ConA	F. gigantica	Fg-LGMN-1	10	2
88	Ehsan 2024	gPBMC	ConA	F. gigantica	Fg-LGMN-1	20	2
88	Ehsan 2024	gPBMC	ConA	F. gigantica	Fg-LGMN-1	40	2
89	Folle 2024	mMacrophage	LPS	E. granulosus	EgAgB	1	24
89	Folle 2024	mMacrophage	LPS	E. granulosus	EgAgB	10	24
89	Folle 2024	hMacrophage	LPS	E. granulosus	EgAgB	1	24
89	Folle 2024	hMacrophage	LPS	E. granulosus	EgAgB	10	24
89	Folle 2024	mMacrophage	LPS	E. granulosus	rEgAgB8/1	1	24
89	Folle 2024	mMacrophage	LPS	E. granulosus	rEgAgB8/1	10	24
89	Folle 2024	hMacrophage	LPS	E. granulosus	rEgAgB8/1	1	24
89	Folle 2024	hMacrophage	LPS	E. granulosus	rEgAgB8/1	10	24
90	Gong 2024	mMacrophage	LPS	F. hepatica	CL7	15	12
90	Gong 2024	mMacrophage	LPS	F. hepatica	CL7	15	12
91	Li 2024	mMacrophage	LPS & IFNγ	E. multilocularis	Emu-serpin	50,000	24
92	Lalor 2025	mMacrophage	LPS	F. hepatica	FhHDM-1.c2	20	16
92	Lalor 2025	hMacrophage	LPS	F. hepatica	FhHDM-1.c2	20	16

References	Method	IL12 (%)	IL1β (%)	IL6 (%)	TNFα (%)	IL2 (%)	IFNγ (%)	IL17 (%)	IL4 (%)	TGFβ (%)	IL10 (%)
14	El	− 66					− 29				114
15	El	− 95			− 73
15	El	− 95			− 63
15	El	− 93			− 51
16	El						170				− 17
17	El			− 45	− 39
17	El			− 57	− 63		− 48
18	El	− 60
18	El	− 79
18	El	− 32
19	El	− 87		− 60	− 71						− 96
20	El						− 58		− 26
21	El			− 95
21	El			− 99							− 75
21	El			− 95
21	El			− 97							− 53
22	El						24		5	12	Excl
23	El						− 6
23	El						− 82
23	El						− 34
23	El						− 17
23	El						− 33
23	El						− 79
24	El			− 46	− 31						5
24	El			− 80	− 63						− 54
24	El			− 90	− 65						− 92
24	El			− 3	− 54						20
24	El			0	− 59						99
24	El			− 8	− 80						120
25	qP	− 70
25	El			− 29	− 37					48	39
25	El			− 22	− 24					40	31
25	El			− 20	− 21					45	24
26	qP										Excl
27	qP	− 94	− 46		− 77						215
28	El			84	18		93	− 22	− 90	33	− 48
29	El		Excl
30	El	Excl		47	Excl						293
31	El	− 98									− 63
31	El	36									34
31	El	− 86									− 62
32	qP	Excl	− 86	− 48	− 51
33	qP			− 87	− 74					93
34	qP										62
34	qP		2	30	1						4
34	qP		− 34	− 20	− 38						36
34	qP		− 30	− 39	− 16
35	El						− 4	35	− 2		58
35	El						− 10	167	3		73
35	El						− 21	141	11		150
35	El						− 38	163	17		171
36	qP		− 86	− 67	− 66
36	qP		− 94	− 87	− 75
36	qP		− 40	− 58	− 52
36	qP		− 71	− 76	− 65
37	El						72	19	− 5		37
37	El						108	77	− 12		30
37	El						122	137	− 38		69
37	El						133	221	− 40		73
38	El	− 38		− 30	− 49
39	El		− 38		− 71						− 3
39	El		− 70		− 81						30
39	El		− 91								6
39	El		− 4		7						− 3
39	El		− 48		− 65						30
39	El		− 67		− 70						6
40	El			− 90	− 90
41	El		38
42	El						13	15	38	− 5	16
42	El						18	28	39	− 19	21
42	El						31	34	62	− 20	37
42	El						36	42	59	− 21	46
42	El						39	47	52	− 28	71
43	El	− 16			− 17
44	El	− 6	− 21		− 8					10	16
44	El	− 12	− 32		− 24					31	24
44	El	− 21	− 46		− 37					48	39
44	El	− 30	− 65		− 45					63	63
45	El	− 5	− 15		− 10					24	16
45	El	− 11	− 34		− 17					51	23
45	El	− 18	− 46		− 34					69	37
45	El	− 23	− 58		− 46					84	62
46	El	− 3	− 17		− 7					3	18
46	El	− 10	− 28		− 15					3	23
46	El	− 16	− 41		− 33					0	39
46	El	− 21	− 55		− 38					2	61
47	El	− 100		− 79	− 67
47	El	− 100		− 49	− 61
47	El	− 100		− 71	− 44
47	El	− 100		− 77	− 68
48	El				− 61		− 55				92
49	El		− 11	− 34	− 40
49	El			− 22		− 22			− 57
50	El		− 13	22
51	El			− 13
51	El			− 10
52	qP	− 44	− 38	42	− 25				119		21
53	qP			− 80	− 91					219
54	qP				− 58						Excl
55	qP		− 99		− 85
55	qP		− 98		− 88
55	qP		− 98		− 83
56	qP					60	69	43	40	34	97
56	qP					54	106	146	169	37	163
56	qP					49	146	Excl	134	54	Excl
57	El			− 72
58	El						− 21	− 56	− 42	− 22	− 39
58	El						− 27	− 49	− 31	− 28	− 32
58	El						− 32	− 33	− 16	− 33	− 24
58	El						− 36	− 20	− 9	− 39	− 10
59	qP				− 24						− 26
59	qP				− 33						− 52
60	qP		− 65		− 75		− 94
60	qP		− 80		− 98		− 79
60	qP				− 51		− 79
60	qP				− 84		− 79
61	El			− 2	− 14
61	El			− 8	− 35
61	El			− 49	− 85
61	El			− 17	− 51
61	El			− 32	− 69
62	El				2	− 2	− 24	− 2	1	16	75
62	El				− 52	− 4	− 44	2	− 4	19	136
62	El				− 41	0	− 44	1	− 1	74	261
63	El				− 1	− 42	4	− 3	22	5	23
63	El				− 1	− 40	− 26	1	54	14	143
63	El				− 1	− 46	− 52	− 3	31	11	159
64	El				− 28	− 25	1	− 3	− 16	97	− 3
64	El				− 55	− 40	− 3	− 40	− 21	99	1
64	El				− 65	− 41	1	− 62	− 62	108	1
65	qP						11		5		5
65	qP						22		83		11
65	qP						11		180		11
65	qP						9		159		14
66	El					14	− 7	7	− 9	5	− 6
66	El					13	− 15	13	− 18	− 3	− 15
66	El					9	− 15	0	− 23	1	− 19
66	El					14	− 19	1	− 23	− 10	− 23
67	El					5	− 3	− 4	− 10	− 11	− 7
67	El					− 13	− 12	5	− 26	− 15	13
67	El					− 15	− 15	14	− 21	− 27	14
67	El					− 2	− 10	− 11	− 17	− 28	28
68	El	− 100		− 1	− 43
68	El	− 67		− 14	− 43
69	El				− 31
70	qP				1	− 4	12		200	15	− 16
70	qP				7	− 2	3		204	15	− 58
70	qP				− 1	32	161		Excl	Excl	− 72
70	qP				7	89	136		236	285	− 71
71	El			− 17			− 46			122	46
72	El			− 34
72	El			− 13
72	El			31
72	El			− 5
73	El				− 18	− 37
73	El				− 5	− 23
73	El				− 16	− 31
73	El				− 29	− 43
73	El				− 28	− 36
73	El				− 33	− 51
73	El				− 44	− 68
73	El				− 76	− 95
73	El				− 8	− 26
73	El				− 34	− 42
73	El				− 21	− 41
73	El				− 18	− 38
73	El				− 34	− 46
73	El				− 21	− 50
73	El				− 12	− 54
73	El				− 15	− 65
74	El				− 97		− 25	− 31
74	El				− 53		− 7	− 8
74	El				− 48		− 21	− 28
74	El				− 19		4	24
74	El				− 55		− 30	− 7
75	qP		− 58	− 72	− 72		− 71
75	El		− 55				− 61
76	qP	− 60	− 85	− 74	− 59		− 40			− 10	− 4
77	El										− 2
77	El										43
78	El			− 23	− 18					− 47	− 9
78	El			− 26	− 18					− 30	8
78	El			− 37	− 26					0	43
79	El			− 40	− 32						87
80	El	70		35			26		− 2		52
80	El	− 33		− 55			− 22		49		143
80	El	38		− 18			9		20		46
81	El	− 37		− 29							145
82	El			− 24	− 11						3
82	El			− 21	− 5						− 11
82	El			− 28	− 16						− 21
83	El		− 57	− 43	− 58						− 7
84	qP		− 25	− 54	− 22
84	qP		− 51	− 67	− 47
84	qP		− 70	− 76	− 77
84	qP		− 22	− 24	− 25
84	qP		− 59	− 67	− 58
84	qP		− 75	− 80	− 82
84	WB		− 10	− 18	− 19
84	WB		− 24	− 37	− 34
84	WB		− 42	− 43	− 43
84	WB		− 8	− 16	− 7
84	WB		− 24	− 28	− 15
84	WB		− 36	− 44	− 35
85	El			− 92	− 11
85	El				− 24
85	El				− 40
86	El
86	El
87	El		− 11	− 10	− 2
87	El		− 16	− 14	− 8
87	El		− 27	− 20	− 15
88	qP					52		19	− 40		− 24
88	qP					67		19	− 48		1
88	qP					80		47	− 82		18
88	qP					132		94	− 82		66
89	El	− 84		− 95
89	El	− 97		− 99
89	El		− 49	− 75
89	El		− 80	− 94
89	El	− 68		− 82
89	El	− 89		− 98
89	El		− 30	− 66
89	El		− 56	− 85
90	qP		− 73	− 68	− 68
90	qP		− 75	− 61	− 61
91	qP		− 88		− 86					162	243
92	El				− 61
92	El				− 85

A total of 79 articles were included in this review from which 229 separate records were identified. For each of these records, the percent change in cytokine levels is shown. The lowercase letter before each cell types denotes the species of origin: human (h), mouse (m), goat (g), rat (r), pig (p). Method refers to the method used for cytokine analysis: ELISA (El); qPCR (qP); WB (Western immunoblotting). See Supplementary Excel file for more details and specific values.

ConA, concanavalin A; EK, embryonic kidney; fMLP, N-Formylmethionine-leucyl-phenylalanine; Inflamm(s), inflammagen(s); LPS, lipopolysaccharide; PBMC, peripheral blood mononuclear cell; PMA, phorbol myristate acetate. Excl, values excluded as statistical outliers. Some cytokines were measured infrequently (< 5 records) and these have been omitted from this table (these include: IL-5, IL-8, IL-9, IL-13 and INFβ).

Data extraction

Variables manually extracted from the selected articles included cell type, inflammagen, helminth species and HDIP name, as well as cytokines measured (Table 1 and Supplementary Excel file). Because any given article may have had several experimental parameters (e.g. different HDIP concentrations or different incubation times etc.), to comprehensively synthesize this literature, each of these was considered a separate “record” for cytokine data analysis. Cytokine levels were measured by qPCR, Western immunoblotting or ELISA, and expressed as relative values or concentration as appropriate. Specific values were extrapolated from the relevant figure(s) within each article using GetData Graph Digitizer software. Based on the data extracted, we determined that a formal meta-analysis was inappropriate due to substantial heterogeneity across studies¹³, including differences in helminth species, helminth-derived peptides, cell models, inflammagens, cytokines assessed, analytical methods, and specific experimental design parameters. Instead, we conducted a cross-study quantitative analysis focusing on the percent change in inflammagen-induced cytokine levels following treatment with helminth-derived peptides. To do so, the effects of the HDIPs were quantitatively assessed by calculating the percent change in cytokine levels when cells were challenged with inflammagen in the absence versus the presence of the HDIPs. Data extraction was completed independently by two of the authors (SS and AF) and cross-checked for accuracy.

Statistical analyses

General study characteristics are shown as pie charts. All data related to percent cytokine change are shown as scatter plots depicting individual records with the mean ± standard error of the mean (SEM). To determine if the HDIPs reduced or increased cytokine levels beyond zero, data were analysed using one-sample t-test (with the hypothesised population mean set to zero). To determine the effect of cell type, inflammagen or cell species on the efficacy of HDIPs, the data were analysed by two-way ANOVA with post-hoc Bonferroni. The reader is also directed to the Supplementary Excel File where the extracted data is provided in more detail and can be sorted, filtered and/or pivoted for further analyses.

Results

General study characteristics

Seventy-nine articles were included in this review in which HDIPs were assessed for their immunomodulatory effects in in vitro cellular models of inflammation (Table 1 and Supplementary Excel file). We found that most published studies used macrophages, peripheral blood mononuclear cells (PBMCs) or dendritic cells as the target immune cell (Fig. 2A), and LPS or Concanavalin A to elicit inflammatory responses (Fig. 2B). Although 20 different helminth species tested in these studies, the most widely used were the nematodes, Haemonchus contortus and Acanthocheilonema viteae, the trematodes, Fasciola hepatica, Schistosoma mansoni and Schistosoma japonicum, and the cestode, Echinococcus granuloses (together tested in 65% of studies) (Fig. 2C). From the collective helminth species, > 60 different sequenced HDIPs were tested (Table 1 and Supplementary Excel file), and although there was no widely used HDIP, ES-62 (a phosphorylcholine-containing glycoprotein secreted by A. viteae) was used most frequently (n = 7 articles).

Fig. 2.

General study characteristics. A total of 79 articles were included in this review and a summary of the key characteristics of these articles is depicted in these pie charts. These show the proportions of these studies using different (A) cell types, (B) inflammagens and (C) helminth species. LPS, lipopolysaccharide; ConA, concanavalin A; PBMC, peripheral blood mononuclear cell.

Effect of HDIPs on cytokines

From the 79 articles included in this review, 229 separate records were identified in which HDIPs were assessed for their effect on cytokine levels in cellular models of inflammation. Taking all this data together, the effect of the HDIPs on the most widely assessed cytokines was first examined (Fig. 3). This revealed that levels of the pro-inflammatory cytokines, IL-12, IL-1β, IL-6 and TNFα were all significantly reduced by the HDIPs (IL-12: t₍₄₂₎ = 7.41, P < 0.0001; IL-1β:t₍₆₁₎ = 12.73, P < 0.0001; IL-6: t₍₉₂₎ = 10.55, P < 0.0001; TNFα:t₍₁₃₈₎ = 17.66, P < 0.0001). In contrast, levels of the anti-inflammatory cytokines, IL-10, TGFβ and IL-4 were significantly increased (IL-10: (t₍₁₀₉₎ = 4.80, P < 0.0001; TGFβ:t₍₅₆₎ = 3.54, P < 0.001; IL4: t₍₅₅₎ = 2.01, P < 0.05). The HDIPs also increased levels of the pro-inflammatory cytokine IL-17 (t₍₄₅₎ = 2.78, P < 0.01), but neither IL-2 nor IFNγ were significantly changed (from zero) overall.

Fig. 3.

Effect of HDIPs on cytokines overall. The effect of the HDIPs on the most widely assessed cytokines. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analysed by one-sample test with the hypothesised population mean set to zero. ****P < 0.0001, ***P < 0.001, **P < 0.01 and *P < 0.05. NS: not-significantly different to zero.

Effect of cell type and inflammagen on efficacy of HDIPs

The effects of main cell type and inflammagen on the ability of the HDIPs to modify cytokine levels was then assessed (Fig. 4). This highlighted that cell type (Fig. 4A) but not inflammagen (Fig. 4C) had a significant effect on the ability of the HDIPs to reduce pro-inflammatory cytokine expression. Specifically, the HDIPs reduced IL-12 and TNFα significantly more in macrophages than PBMCs (Fig. 4A; Cell type: F_(1, 247) = 32.78; P < 0.0001). In contrast, neither cell type (Fig. 4B) nor inflammagen (Fig. 4D) affected the ability of the HDIPs to increase anti-inflammatory cytokine levels.

Fig. 4.

Effect of cell type and inflammagen on efficacy of HDIPs. The effect of (A) & (B) cell type and (C) & (D) inflammagen on pro-inflammatory and anti-inflammatory cytokine levels. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analyzed by two-way ANOVA with post-hoc Bonferroni. ****P < 0.0001 Macrophage vs. PBMC.

Effects of HDIPs from specific species on cytokines

The effects of the HDIPs from the most widely used species were then assessed in terms of their effect on the most affected pro-inflammatory (IL-12, IL-1β, IL-6 and TNFα) and anti-inflammatory (IL-10, TGFβ and IL-4) cytokines (Fig. 5). This demonstrate that HDIPs from all the widely used species significantly reduced pro-inflammatory cytokine levels (Fig. 5A; Echinococcus granuloses: t₍₃₁₎ = 11.30, P < 0.0001; Schistosoma japonicum: t₍₁₆₎ = 9.18, P < 0.0001; Fasciola hepatica: t₍₄₇₎ = 9.03, P < 0.0001; Acanthocheilonema viteae: t₍₄₂₎ = 9.20, P < 0.0001; Schistosoma mansoni: t₍₂₀₎ = 7.41, P < 0.001; Haemonchus contortus: t₍₅₆₎ = 10.05, P < 0.0001). There was less data available for the anti-inflammatory cytokines, but it was evident that HDIPs from Haemonchus contortus were capable of both reducing pro-inflammatory cytokine levels, as well as significantly increasing anti-inflammatory cytokine levels (Fig. 5B; Haemonchus contortus: t₍₁₃₉₎ = 5.61, P < 0.0001).

Fig. 5.

Effect of HDIPs from specific species on cytokine levels. The effect of the HDIPs from the most widely used species on (A) pro-inflammatory (IL-12, IL-1β, IL-6 and TNFα) and (B) anti-inflammatory (IL-10, TGFβ and IL-4) cytokine levels. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analysed by one-sample test with the hypothesised population mean set to zero. ****P < 0.0001 and ***P < 0.001. NS, not-significantly different to zero.

Effects of HDIPs from specific species on specific cytokines

This data were then further subdivided to determine the effects of the HDIPs from the most widely used species on the most affected pro-inflammatory cytokines individually (Fig. 6). This demonstrated that HDIPs from Echinococcus granuloses significantly reduced levels of all of the most affected pro-inflammatory cytokines (Fig. 6: IL-12: t₍₄₎ = 17.84, P < 0.0001; IL-1β: t₍₉₎ = 6.60, P < 0.0001; IL-6: t₍₉₎ = 5.14, P < 0.001; TNFα: t₍₆₎ = 5.21, P < 0.01). HDIPs from the other species also significantly reduced levels of most of the pro-inflammatory cytokines (statistical outcomes in Fig. 6).

Fig. 6.

Effect of HDIPs from specific species on pro-inflammatory cytokine levels. The effect of the HDIPs from the most widely used species on (A) IL-12, (B) IL-1β, (C) IL-6 and (D) TNFα cytokine levels. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analysed by one-sample test with the hypothesised population mean set to zero. ****P < 0.0001, ***P < 0.001, **P < 0.01 and *P < 0.05. NS, not-significantly different to zero; ND, not done as too few points.

Similarly, the data were also subdivided to determine the effects of the HDIPs from the most widely used species on the most affected anti-inflammatory cytokines individually (Fig. 7). Although there was less data available for the anti-inflammatory cytokines, there were sufficient data points for Haemonchus contortus to note that HDIPs from this species significantly increase IL-10 and (to a lesser extent) TGFβ levels, but do not significantly increase IL-4 levels (Fig. 7: IL-10: t₍₅₇₎ = 4.91, P < 0.0001; TGFβ: t₍₄₀₎ = 2.58, P < 0.05; IL-4: t₍₄₀₎ = 1.94, P > 0.05).

Fig. 7.

Effect of HDIPs from specific species on anti-inflammatory cytokine levels. The effect of the HDIPs from the most widely used species on (A) IL-10, (B) TGFβ and (C) IL-4 cytokine levels. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analysed by one-sample test with the hypothesised population mean set to zero. ****P < 0.0001 and *P < 0.05. NS, not-significantly different to zero; ND, not done as too few points.

Effects of mechanistic classes of HDIPs on cytokines

The effect of HDIPs was also assessed based on their known functional classification (Fig. 8). This demonstrated that HDIPs that function as cysteine proteases were capable of both reducing pro-inflammatory (t₍₉₉₎ = 13.58, P < 0.0001) and increasing anti-inflammatory (t₍₃₇₎ = 3.79, P < 0.001) cytokine levels. In contrast, the other classes assessed (cathelicidin-like HDIPs, ShK-related HDIPs, fatty acid binding HDIPs, glutathione transferase HDIPs, galectin HDIPs and thioredoxin peroxidase HDIPs) were also capable of reducing pro-inflammatory cytokine levels but were less effective at increasing anti-inflammatory cytokine levels (or this was not assessed).

Fig. 8.

Effect of mechanistic classes of HDIPs on cytokines. The effect of the HDIPs from different mechanistic classes was assessed on A) pro-inflammatory (IL-12, IL-1β, IL-6 and TNFα) and B) anti-inflammatory (IL-10, TGFβ and IL-4) cytokine levels. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analysed by one-sample test with the hypothesised population mean was set to zero. ****P < 0.0001 and ***P < 0.001.

Effects of cell species on change in cytokine levels by HDIPs

Another consideration was whether or not the species from which the cells were derived had any effect on change in cytokine levels induced by the HDIPs. Sufficient records allowed for this comparison for selected pro-inflammatory cytokines in mouse versus human macrophages (Fig. 9A) and goat versus human PBMCs only (Fig. 9B). This highlighted that HDIPs reduced pro-inflammatory cytokine levels to the same extent in macrophages derived from mice and humans. In contrast, in PBMCs, there was significant difference in the ability of the HDIPs to reduce pro-inflammatory cytokine levels (Fig. 9B; Species x Cytokine: F_(1,85) = 20.84; P < 0.0001). Specifically, while HDIP-induced reductions in TNFα levels were not affected by species, the HDIPs reduced IL-2 levels in human PBMCs but not in goat PBMCs.

Fig. 9.

Effect of cell species on change in cytokine levels by HDIPs. The effect of HDIPs on selected pro-inflammatory cytokine levels in (A) macrophages derived from mice and humans, and (B) PBMCs derived from goats and humans. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown. Data were analyzed by two-way ANOVA with post-hoc Bonferroni. ****P < 0.0001 Goat vs. Human.

Effects of specific HDIPs on cytokines

Finally, the data were plotted by individual HDIP for the most widely assessed pro-inflammatory and anti-inflammatory cytokines (Fig. 10). The overwhelming trend for a reduction in proinflammatory cytokine levels (Fig. 10A) and an increase in anti-inflammatory cytokine levels (Fig. 10B) across HDIPs is clear.

Fig. 10.

Effect of specific HDIPs on cytokine levels. The effect of named HDIPs from H. contortus (blue), F. hepatica (purple) and A. viteae (pink) on (A) pro-inflammatory (IL-12, IL-1β, IL-6 and TNFα) and (B) anti-inflammatory (IL-10, TGFβ and IL-4) cytokine levels. Each data point represents a specific record extracted from Table 1/Supplementary Excel file, and the mean ± sem is also shown.

Discussion

Secretory products of helminths have become a topic of interest in the last 20 years due to their immunomodulatory properties, leading researchers to investigate HDIPs as therapeutic molecules for different immune and inflammatory conditions^1,4,11. Preclinical studies in both cellular and animal models have assessed these immunomodulatory properties using various helminth species, HDIPs, inflammagens, disease models, experimental parameters and measured outcomes in terms of functionality of immune cells and their secretome. In this review, we focussed on consolidating the studies in cellular models with a focus on the effect of HDIPs on cytokine levels. Using a systematic approach, we identified 79 articles in which > 60 HDIPs from 20 helminth species were assessed largely in LPS or Concanavalin A stimulated macrophages, peripheral blood mononuclear cells or dendritic cells. Regardless of inflammagen, cell type or species, the overwhelming effect of the HDIPs was a reduction in expression or production of pro-inflammatory cytokines such as IL-12, IL-1β, IL-6 and TNF, and an increase in anti-inflammatory cytokines such as IL-10, TGFβ and IL-4. Note that although IL-4 is a pleiotropic cytokine which has critical pro-inflammatory effects via driving Th2 responses (including to combat helminth infections⁹), it was included in this analysis with IL-10 and TGFβ because of its recognised anti-inflammatory effects⁹³. Because of their profound effect on cytokine levels, this systematic review adds to the growing body of literature that supports the exploration of helminth secretory products as potential therapeutics in pathological conditions driven by overactive immune and inflammatory responses.

Although this review identified > 60 HDIPs from 20 helminth species, the most widely used species for derivation of HDIPs were the nematodes, Haemonchus contortus and Acanthocheilonema viteae, the trematodes, Fasciola hepatica, Schistosoma mansoni and Schistosoma japonicum, and the cestode, Echinococcus granuloses. These have adapted to suppress their host’s immune system, allowing them to be tolerated by the host for significant periods of time. For example, the blood flukes, Schistosoma mansoni and Schistosoma japonicum have very long infection periods and can persist in humans for decades⁹⁴, whereas, in contrast, infections by the liver fluke, Fasciola hepatica⁹⁵, or the blood-sucking roundworm, Haemonchus contortus⁹⁶, are typically shorter, lasting months or years. Regardless of the duration of the infective period, these endoparasites have evolved multiple mechanisms through which they can evade, modulate and suppress the immune system of the host species, including the secretion of immunomodulatory HDIPs.

This study review revealed that > 60 different HDIPs have been assessed for their effect on cytokine responses in immune cells. The most common mechanistic class of the molecules assessed was cysteine protease activity which is associated with HDIPs from many species^97–99. Helminths, along with many other pathogens, have evolved to secrete cysteine proteases that facilitate invasion, infection and immune suppression of the host. These have various effects ranging from degradation (of extracellular matrix components, for example) of proteins to facilitate helminth invasion, to degradation of many proteins involved in the immune response. These cysteine protease HDIPs can cleave the hinge region in IgG antibodies, cleave and inactivate pro-inflammatory cytokines, and degrade and inactivate pathogen-detecting Toll-like receptors among many other mechanisms⁹⁸. This review demonstrated that HDIPs with cysteine protease activity consistently and significantly reduced pro-inflammatory cytokine levels and increased anti-inflammatory cytokine levels, in line with the existing literature. Another mechanistic class that was widely assessed were the cathelicidin-like HDIPs. These are the so-called helminth defense molecules (HDMs) secreted by trematodes such as Fasciola hepatica, Schistosoma mansoni and Schistosoma japonicum, that share that share structural and functional similarities with mammalian cathelicidin^100,101. Like the cysteine proteases, these have multiple mechanisms through which they can enable trematode infection and immune suppression of the host including molecular mimicry, LPS-binding and sequestration, and inhibition of the NLRP3 inflammasome activation¹⁰². In the present review, the cathelicidin-like HDIPs also profoundly reduced pro-inflammatory cytokine levels but effects on anti-inflammatory cytokines were not significant overall. Similarly, the other mechanistic classes assessed included ShK-related HDIPs, fatty acid binding HDIPs, glutathione transferase HDIPs, galectin HDIPs, and thioredoxin peroxidase HDIPs all of which, like the cathelicidin-like HDIPs, significantly reduced proinflammatory cytokine levels.

One unexpected aspect of the data consolidation in this review was the finding of a greater efficacy of HDIPs (greater suppression of pro-inflammatory cytokines) in macrophages compared with PBMCs. However, this is likely simply due to the heterogeneity of the PBMC population which includes many populations of lymphocytes (including T-cells, B-cells and natural killer cells) as well as monocytes which can be considered immature macrophages. Thus, relative to more uniform macrophage cultures, PBMC cultures are likely to have variable responsivity to the main inflammagens used and consequently, variability responsivity to the HDIPs. Additionally, the helminth molecules may also have a broad range of cellular targets—so some may interact with, and modulate, the activity of all cells within PBMC cultures, whereas others may only have specificity for one cell type—this has not been explored for many of the proteins being examined here, but might also explain the difference in efficacy. The effect of cell species from which the PBMCs were derived was another unexpected finding with HDIPs reducing IL-2 in human PBMCs but not goat PBMCs. This highlights the importance of cell species as a consideration for study design. Another interesting aspect of the current systematic review is that it revealed the paucity of studies using other, critically important, immune cells. For example, there was only a single study each in which the immunomodulatory effect of HDIPs in mast cells⁵¹ or microglia³⁴ was assessed. This highlights an important gap in literature as there is potential for the HDIPs to be effective immunomodulators in allergic and/or neuroinflammatory conditions. Another gap in the literature that was identified in this systematic review was the lack of variability in the inflammagen used with most studies using LPS. LPS is a gram-negative bacterial endotoxin that is recognized by Toll-like receptor 4 (TLR4) on multiple cell types, causing a signaling cascade which leads to the activation of NF-kB, triggering the release of proinflammatory molecules like TNFα, IL-6, and nitric oxide¹⁰³. Although LPS-induced inflammation is a widely accepted and common model for many inflammatory conditions, there is a need to test whether HDIPs can mitigate the effects of other clinically relevant inflammatory triggers, not only other pathogen-associated molecular patterns, but also damage-associated molecular patterns.

While this review focused on assessing the efficacy of HDIPs (in modulating cytokine levels) in cellular models, several studies have also assessed their efficacy in animal models on inflammatory disease such as inflammatory bowel disease⁴, multiple sclerosis⁹², rheumatoid arthritis¹⁰⁴ and asthma¹⁰⁵. The success of these, and other studies, studies led to a series of clinical trials of helminth therapy in which patients are infected with specific helminths to modulate the immune system and treat their inflammatory and autoimmune diseases. Infection with the hookworm, Necator americanus, for example, has been assessed in clinical trials of ulcerative colitis¹⁰⁶, multiple sclerosis¹⁰⁷ and celiac disease¹⁰⁸ with mixed results in terms of both efficacy and safety. This highlights the rationale for continued research and development of HDIPs as therapeutic drugs as these would reduce the risks associated with live parasitic infection. To date, only one clinical trial of a HDIP has been completed (NCT02281916) in which P28GST, a Schistosoma haematobium-derived glutathione S-transferase, was tested in patients with Crohn’s disease with some evidence of efficacy¹⁰⁹.

One limitation of this review, as with all systematic reviews, is the potential for important studies to have been omitted due to the systematic nature of the search and screening. While systematic reviews minimise bias by using a rigorous and predefined method to find relevant research, there is a need to balance sensitivity (finding all relevant studies) and specificity (reducing irrelevant results). While high sensitivity uses broader search terms and reduces the risk of excluding relevant studies, it can also lead to more irrelevant results and a heavier screening burden. In contrast, high specificity uses more specific search terms to identify highly relevant studies and reduce screening workload, but this risks excluding relevant studies. Despite these inherent limitations of systematic approaches to literature appraisal, this review systematically consolidates and summarises the effect of immunomodulatory HDIPs in cellular model of inflammation. It clearly demonstrates that suppression of pro-inflammatory, and enhancement of anti-inflammatory, cytokine expression and production in response to inflammagen stimulation in immune cells is a property of many HDIPs from many species. The review also revealed important gaps in this literature highlighting the opportunity to explore the immunomodulatory efficacy of HDIPs in other cell types using other inflammatory stimuli to determine their potential for other immune system disorders heretofore relatively unexplored. Consolidating this literature may direct future research into the use of HDIPs in a wider variety of preclinical models in order to determine their broader therapeutic potential.

Author contributions

SS, AF and ED led the design and implementation of the systematic review, and wrote the first draft of the manuscript; RL, SD, JPD and DMK provided expert input into the drafts of the manuscript.

Funding

SS is funded by the Parkinson’s Disease Research Award from Tony & Peigí O’Donoghue through the Galway University Foundation. ED would also like to acknowledge grants from the Michael J Fox Foundation for Parkinson’s Research (Grant Numbers: 17244 and 023410).

Data availability

All data is available in the Supplementary Excel file.

Declarations

Competing interests

The authors declare no competing financial or non-financial interests.