Mutation ARV - Evidence

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MARVEL on RT mutations at position 333

HIVdb Algorithm: Comments & Scores

G333D/E are polymorphisms that occur more frequently in persons receiving NRTIs than in untreated persons. They may facilitate AZT resistance in isolates that contain M184V and multiple TAMs.
G333D/E are polymorphisms that occur more frequently in persons receiving NRTIs than in untreated persons. They may facilitate AZT resistance in isolates that contain M184V and multiple TAMs.

Mutation	3TC	FTC	ABC	AZT	D4T	DDI	TDF
G333D	0	0	0	5	0	0	0
G333E	0	0	0	5	0	0	0

Footnote:Mutation scores on the left are derived from published literature linking mutations and ARVs (the complete details can be found in the HIVdb Release Notes).

Genotype-treatment correlation

Mutation frequency according to subtype and drug-class experience.

The frequency of each mutation at position 333 according to subtype and drug-class experience. Data are shown for the 8 most common subtypes. The number of persons in each subtype/treatment category is shown beneath the subtype. Mutations occurring at a frequency >0.5% are shown. Each mutation is also a hyper-link to a separate web page with information on each isolate, including literature references with PubMed abstracts, the GenBank accession number, and complete sequence and treatment records.

Pos	WT	RTI Naive Persons								NRTI (but no NNRTI) Treated Persons
Pos	WT	A 3215	B 22174	C 7465	D 1180	F 684	G 1316	AE 3654	AG 2607	A 172	B 4055	C 303	D 126	F 99	G 142	AE 325	AG 75
333	G		E ^7.2 D ^0.8	E ^0.9	E ^3.5	E ^1.8	E ^1.5				E ^9.5 D ^1.1	E ^4.1		E ^6.7
Footnote: The query page Mutation Prevalence According to Subtype and Treatment to examine the frequency of all mutations according to subtype and treatment; The program HIVSeq provides similar output for mutations in user-submitted sequences; A complete description of the program that generates these tables can be found at Rhee et al AIDS 2006.

Mutation frequency according to treatment with individual ARVs.

The first row shows the frequency of the mutation in persons who are RTI-naive (indicated in green). The second row shows the frequency of the mutation in persons who have received one or more NRTIs (+/- NNRTIs). The following rows show the frequency of the mutation in persons who have received only a single NRTI. Mutation rates that differ significantly between treated and untreated isolates are indicated in yellow.

Mutation	NRTI	NNRTI	NumSeq	NumMut	% Mutant	p
G333D	0	0	11764	49	0.40
G333D	>=1	>=0	6799	51	0.70	0.004
G333D	AZT	>=0	123	0
G333D	DDI	>=0	3	0
G333D	D4T	>=0	17	0
G333D	ABC	>=0	0	0
G333D	D4T+3TC	>=0	750	3	0.40	0.823
G333D	AZT+3TC	>=0	444	0
G333D	D4T+DDI	>=0	118	2	1.60	0.160
G333D	AZT+DDI	>=0	56	0
G333D	ABC+3TC	>=0	17	0
G333D	TDF+3TC	>=0	67	1	1.40	0.682
G333D	TDF+FTC	>=0	19	0

Mutation	NRTI	NNRTI	NumSeq	NumMut	% Mutant	p
G333E	0	0	11764	541	4.50
G333E	>=1	>=0	6799	458	6.70	0.000
G333E	AZT	>=0	123	11	8.90	0.039
G333E	DDI	>=0	3	0
G333E	D4T	>=0	17	1	5.80	0.744
G333E	ABC	>=0	0	0
G333E	D4T+3TC	>=0	750	18	2.40	0.006
G333E	AZT+3TC	>=0	444	21	4.70	1.000
G333E	D4T+DDI	>=0	118	9	7.60	0.181
G333E	AZT+DDI	>=0	56	2	3.50	0.964
G333E	ABC+3TC	>=0	17	1	5.80	0.744
G333E	TDF+3TC	>=0	67	4	5.90	0.810
G333E	TDF+FTC	>=0	19	0

Mutation	NRTI	NNRTI	NumSeq	NumMut	% Mutant	p
G333R	0	0	11764	9	0.00
G333R	>=1	>=0	6799	3	0.00	0.592
G333R	AZT	>=0	123	0
G333R	DDI	>=0	3	0
G333R	D4T	>=0	17	0
G333R	ABC	>=0	0	0
G333R	D4T+3TC	>=0	750	0
G333R	AZT+3TC	>=0	444	0
G333R	D4T+DDI	>=0	118	0
G333R	AZT+DDI	>=0	56	0
G333R	ABC+3TC	>=0	17	0
G333R	TDF+3TC	>=0	67	0
G333R	TDF+FTC	>=0	19	0

Footnote: About one-half of the untreated isolates belong to non-subtype B isolates; About 20% of the treated isolates belong to non-subtype B isolates; A page containing summaries for all of the mutations at this position can be found here.

Genotype-phenotype correlation

Phenotypes of top 10 common patterns of drug resistance mutations with mutations at position 333.

Mutation pattern data is not available for G333.

A complete summary of additional in vitro susceptibility data for viruses with G333 obtained using other assays including the Antivirogram can be found here.

Phenotypic coefficients using machine learning

Least Square Regression (LSR) was used to learn the relative contribution of each mutation to the fold decrease in susceptibility for an ARV. The figure on the left (click to enlarge the figure) shows the regression coefficients (which correlate with the contribution to resistance) for the 23 nonpolymorphic NRTI-resistance mutations shown to contribute decreased susceptibility to at least one NRTI. A complete description of the method that generates this figure can be found at Rhee et al PNAS 2006.

MARVEL on RT mutations at position 333

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